Artículos de revistas sobre el tema "Inter-manual transfer"

The lack of (inter-)laboratory standardization has hampered the application of universal cutoff values for Alzheimer’s disease (AD) cerebrospinal fluid (CSF) biomarkers and their transfer to general clinical practice. The automation of the AD biomarker immunoassays is suggested to generate more robust results than using manual testing. Open-access platforms will facilitate the integration of automation for novel biomarkers, allowing the introduction of the protein profiling concept. A feasibility study was performed on an automated open-access platform of the commercial immunoassays for the 42-amino-acid isoform of amyloid-β (Aβ1–42), Aβ1–40, and total tau in CSF. Automated Aβ1–42, Aβ1–40, and tau immunoassays were performed within predefined acceptance criteria for bias and imprecision. Similar accuracy was obtained for ready-to-use calibrators as for reconstituted lyophilized kit calibrators. When compared with the addition of a standard curve in each test run, the use of a master calibrator curve, determined before and applied to each batch analysis as the standard curve, yielded an acceptable overall bias of −2.6% and −0.9% for Aβ1−42 and Aβ1–40, respectively, with an imprecision profile of 6.2% and 8.4%, respectively. Our findings show that transfer of commercial manual immunoassays to fully automated open-access platforms is feasible, as it performs according to universal acceptance criteria.

Pollen identification is an important task for the botanical certification of honey. It is performed via thorough microscopic examination of the pollen present in honey; a process called melissopalynology. However, manual examination of the images is hard, time-consuming and subject to inter- and intra-observer variability. In this study, we investigated the applicability of deep learning models for the classification of pollen-grain images into 20 pollen types, based on the Cretan Pollen Dataset. In particular, we applied transfer and ensemble learning methods to achieve an accuracy of 97.5%, a sensitivity of 96.9%, a precision of 97%, an F1 score of 96.89% and an AUC of 0.9995. However, in a preliminary case study, when we applied the best-performing model on honey-based pollen-grain images, we found that it performed poorly; only 0.02 better than random guessing (i.e., an AUC of 0.52). This indicates that the model should be further fine-tuned on honey-based pollen-grain images to increase its effectiveness on such data.

The demand for liquefied natural gas (LNG) ships is increasing for various reasons. Despite their enormous size, cargo holds inside freighters transporting LNG have traditionally been constructed by welding to high-quality standards for safety. This process traditionally relies on manual labor or semi-automatic moving devices. In this study, a methodology was designed for robot-based automated laser welding inside large LNG cargo holds. The developed approach offers a practical solution to challenging issues such as the corrugation of the membrane that forms the inner walls of LNG cargo holds and the inter-floor movement of robots. This study analyzes and restructures the work for laser welding using mobile robots inside LNG cargo holds composed of membranes. For realistic constraints, such as inter-floor movement of robots and high-quality welding of membrane corrugations, methods integrated with manual work have been proposed. Additionally, for the overall membrane laser welding of the LNG cargo hold space, an automated method using robots was suggested. The developed methodology has been realized as operational software for the movement of robots for laser welding in LNG cargo holds.

Strong inter-rater reliability has been established for the Hare Psychopathy Checklist–Revised (PCL-R), specifically by examiners in research contexts. However, there is less support for inter-reliability in applied settings. This study examined archival data that included a sample of sex offenders ( n = 178) who entered federal custody between 1992 and 1998. The offenders were assessed using the PCL-R on two occasions. The first assessment occurred at Millhaven Institution, the intake unit for federally incarcerated offenders in the province of Ontario. The second assessment took place upon inmates’ transfer to the Regional Treatment Center, which admits federal inmates with intense psychological and psychiatric needs. Intra-class correlation coefficients (ICCs) were calculated for item, total, factor, and facet scores. The ICC absolute agreement for the PCL-R total and factor scores from raters across both settings was slightly better than what has been previously reported by Hare. Results of this study show that the reliability of PCL-R scores in field settings can be comparable to those in research settings. Authors conclude by highlighting the importance of training, consultation, considering different scores for a given item, following the guidelines of the manual in addition to considering measures that enhance neutrality and reliability of findings in the criminal justice system.

Assessing and selecting the most viable embryos for transfer is an essential part of in vitro fertilization (IVF). In recent years, several approaches have been made to improve and automate the procedure using artificial intelligence (AI) and deep learning. Based on images of embryos with known implantation data (KID), AI models have been trained to automatically score embryos related to their chance of achieving a successful implantation. However, as of now, only limited research has been conducted to evaluate how embryo selection models generalize to new clinics and how they perform in subgroup analyses across various conditions. In this paper, we investigate how a deep learning-based embryo selection model using only time-lapse image sequences performs across different patient ages and clinical conditions, and how it correlates with traditional morphokinetic parameters. The model was trained and evaluated based on a large dataset from 18 IVF centers consisting of 115,832 embryos, of which 14,644 embryos were transferred KID embryos. In an independent test set, the AI model sorted KID embryos with an area under the curve (AUC) of a receiver operating characteristic curve of 0.67 and all embryos with an AUC of 0.95. A clinic hold-out test showed that the model generalized to new clinics with an AUC range of 0.60–0.75 for KID embryos. Across different subgroups of age, insemination method, incubation time, and transfer protocol, the AUC ranged between 0.63 and 0.69. Furthermore, model predictions correlated positively with blastocyst grading and negatively with direct cleavages. The fully automated iDAScore v1.0 model was shown to perform at least as good as a state-of-the-art manual embryo selection model. Moreover, full automatization of embryo scoring implies fewer manual evaluations and eliminates biases due to inter- and intraobserver variation.

We investigated the role of saccadic gaze fixations in encoding target locations for planning a future manual task consisting of a sequence of discrete target-oriented actions. We hypothesized that fixations of the individual targets are necessary for accurate encoding of target locations and that there is a transfer of sequence information from visual encoding to manual recall. Subjects viewed four targets presented at random positions on a screen. After various delays following target extinction, the subjects marked the remembered target locations on the screen with the tip of a hand-held stick. When the targets were presented simultaneously among distracting elements, the overall accuracy of marking increased with presentation time and total number of targets fixated because the subjects had to serially fixate the individual targets to locate them. Without distractors, the marking accuracy was similarly high regardless of duration of target presentation (0.25–8 s) and number of targets fixated; it was comparable to that with distractors when all four targets had been fixated. This indicates parallel encoding of target locations largely based on peripheral vision. Location memory was stable in these tasks over the delay periods investigated (0.5–8 s). With parallel encoding there was a “shrinkage” in the visuomotor transformation, i.e., the distances between the markings were systematically smaller than the corresponding inter-target distances. When the targets were presented sequentially without distractors, marking accuracy improved with the total number of targets fixated and shrinkage in the visuomotor transformation occurred only with parallel encoding, i.e., when subjects did not fixate the targets. In all experimental conditions for trials in which targets were fixated during encoding, there was little correspondence between the marking sequence and the sequence in which the targets were fixated. We conclude that subjects benefit from fixating targets for subsequent target-oriented manual actions when the targets are presented among distractors and when presented sequentially; when distinct targets are presented simultaneously against a blank background, they are efficiently encoded in parallel largely by peripheral vision.

Cardiac MRI is the gold standard for evaluating left ventricular myocardial mass (LVMM), end-systolic volume (LVESV), end-diastolic volume (LVEDV), stroke volume (LVSV), and ejection fraction (LVEF). Deep convolutional neural networks (CNNs) can provide automatic segmentation of LV myocardium (LVF) and blood cavity (LVC) and quantification of LV function; however, the performance is typically degraded when applied to new datasets. A 2D U-net with Monte-Carlo dropout was trained on 45 cine MR images and the model was used to segment 10 subjects from the ACDC dataset. The initial segmentations were post-processed using a continuous kernel-cut method. The refined segmentations were employed to update the trained model. This procedure was iterated several times and the final updated U-net model was used to segment the remaining 90 ACDC subjects. Algorithm and manual segmentations were compared using Dice coefficient (DSC) and average surface distance in a symmetric manner (ASSD). The relationships between algorithm and manual LV indices were evaluated using Pearson correlation coefficient (r), Bland-Altman analyses, and paired t-tests. Direct application of the pre-trained model yielded DSC of 0.74 ± 0.12 for LVM and 0.87 ± 0.12 for LVC. After fine-tuning, DSC was 0.81 ± 0.09 for LVM and 0.90 ± 0.09 for LVC. Algorithm LV function measurements were strongly correlated with manual analyses (r = 0.86–0.99, p < 0.0001) with minimal biases of −8.8 g for LVMM, −0.9 mL for LVEDV, −0.2 mL for LVESV, −0.7 mL for LVSV, and −0.6% for LVEF. The procedure required ∼12 min for fine-tuning and approximately 1 s to contour a new image on a Linux (Ubuntu 14.02) desktop (Inter(R) CPU i7-7770, 4.2 GHz, 16 GB RAM) with a GPU (GeForce, GTX TITAN X, 12 GB Memory). This approach provides a way to incorporate a trained CNN to segment and quantify previously unseen cardiac MR datasets without needing manual annotation of the unseen datasets.

Prostate segmentations are required for an ever-increasing number of medical applications, such as image-based lesion detection, fusion-guided biopsy and focal therapies. However, obtaining accurate segmentations is laborious, requires expertise and, even then, the inter-observer variability remains high. In this paper, a robust, accurate and generalizable model for Magnetic Resonance (MR) and three-dimensional (3D) Ultrasound (US) prostate image segmentation is proposed. It uses a densenet-resnet-based Convolutional Neural Network (CNN) combined with techniques such as deep supervision, checkpoint ensembling and Neural Resolution Enhancement. The MR prostate segmentation model was trained with five challenging and heterogeneous MR prostate datasets (and two US datasets), with segmentations from many different experts with varying segmentation criteria. The model achieves a consistently strong performance in all datasets independently (mean Dice Similarity Coefficient -DSC- above 0.91 for all datasets except for one), outperforming the inter-expert variability significantly in MR (mean DSC of 0.9099 vs. 0.8794). When evaluated on the publicly available Promise12 challenge dataset, it attains a similar performance to the best entries. In summary, the model has the potential of having a significant impact on current prostate procedures, undercutting, and even eliminating, the need of manual segmentations through improvements in terms of robustness, generalizability and output resolution.

The present study was conducted in Vijayapura and Bagalkot districts of Karnataka state during 2021-22.To study the Adoption level of farmers about redgram production technologies of UAS Dharwad by employing “Ex-post facto” research design and by using simple random sampling technique in Vijayapura and Bagalkot districts constituting a total sample size of 160 farmers. It was revealed that, 40.62 per cent of the redgram growers belonged to medium adoption level category, whereas, 36.25 and 23.13 per cent were observed in low- and high-level adoption categories, respectively.Redgram growers adopted recommended time of sowing (91.87 %), seed rate (73.75 %), variety (61.87 %), seed rate for mixed or inter crop (17.50 %), seed treatment with rhizobium (11.25 %), seed treatment with Phosphate Solubilizing Bacteria (6.87 %), spacing (73.75 %), FYM (71.25 %), chemical fertilizer (65.62 %), application of sulphur (15.62 %), application of zinc sulphate (23.13 %), foliar application of 19:19:19 (22.50 %), spraying of (2.0 %) DAP (15.62 %), first hoeing (93.12 %), second hoeing (55.63 %), Inter-cropping (14.37 %), post emergent herbicides (11.87 %), nipping (3.75 %), recommended method of controlling pod borer (81.25 %), control of spotted pod borer (47.50 %), control of pod fly (57.50%), controlling wilt (65.00%), controlling SMD (28.75 %), controlling phytophthora blight (26.25 %), mechanical harvesting (88.75 %) and manual harvesting (11.25 %). There is enough scope to encourage improved redgram production technologies by using mass contact methods and concerned transfer of technology centers. Thus, the efforts should be made to conduct training programmes and demonstrations.So, it is another vital thing that needs to be given priority to adoption of production technology.

Object Recent studies have emphasized measuring the sagittal vertical axis (SVA) and pelvic parameters (pelvic incidence, sacral slope, and pelvic tilt) when evaluating spinal disorders. An accurate and reproducible measurement is important for a reliable result. Although computerized measurement is more consistent than manual measurement, computerized measurement requires an expensive software program, the need to transfer images to a workstation, and additional education for users. An inexpensive and convenient computerized measurement program is desirable and necessary. The object of this study was to propose a computerized tool for measuring spinal and pelvic parameters and to evaluate the efficacy of this new tool compared with manual measurement. Methods The authors devised a tool that provides computerized measurements of the SVA and pelvic parameters in a picture archiving and communication system (PACS) without transferring images to another program. This tool was created by merging functions in the PACS. The resulting tool is easy to implement by merging functions (indicate the center of 2 points, plot a vertical and a horizontal line from a point, and measure the angles between lines) in any image viewer. The tool was made into icons on a toolbar in the PACS. Measurements of distance and angle were computerized by identifying crucial points after selecting the icon. For SVA, 4 points were identified around each corner of the C-7 body and a fifth point at the superior/posterior corner of the S-1 body. For pelvic parameters, 4 points were identified at the centers of each femoral head and at the anterior/superior and posterior/superior corners of S-1. Thirty-three whole-spine lateral radiographs were randomly selected from the radiographic database. To evaluate inter- and intraobserver variability between observers and method, skilled (2 years of experience) and unskilled (1 week of experience) observers measured SVA and pelvic parameters 3 times with a 7-day interval between each time using both computerized and manual measurement methods. The reliability was measured using the intraclass correlation coefficient. Results The computerized method showed better congruity than the manual method in both skilled and unskilled observers (p < 0.05), and the intraclass correlation coefficients were > 0.9. The skilled observer showed better agreement than the unskilled observer with both computerized and manual methods, and this difference was prominent in measuring pelvic parameters (p < 0.05). The computerized method required less time than the manual method, especially for the unskilled observer (p < 0.05). Conclusions A computerized measurement of pelvic parameters may be a more reliable and efficacious approach than manual measurements. This benefit is more prominent in the unskilled observer, and adding this simple function to an image viewer may be recommended in future studies.

Transarterial radioembolization (TARE) relies on directly injected 90Y- or 166Ho-loaded microspheres in the hepatic arteries. The activity to be injected is generally based on pre-TARE 99mTc-macro-aggregated-albumin (MAA) imaging, while the actual dose distribution is based on post-treatment images. The volume of interest (VOIs) propagation methods (i.e., rigid and deformable) from pre- to post-TARE imaging might affect the estimation of the mean absorbed dose in the tumor and non-tumoral liver (NTL), i.e., DT and DNTL, respectively. Methods: In 101 consecutive patients, liver and tumor were delineated on pre-TARE images and semi-automatically transferred on 90Y-PET/CT images with a rigid or deformable registration approach. Pre- and post-TARE volumes and DT/DNTL/DL were compared using correlation coefficient (CC) indexes, such as intra-class (ICC), Pearson’s (PCC), concordance (CCCo) and Bland–Altman analyses. The Kaplan–Meier curves of overall survival (OS) were calculated according to DT. Results: All computed CCs indicated very good (>0.92) agreement for volume comparison, while they suggested good (ICC ≥ 0.869, PCC ≥ 0.876 and CCCo ≥ 0.790) and moderate agreement in the intra- and inter-modality DT/DNTL/DL comparisons, respectively. Bland–Altman analyses showed percentage differences between the manual and deformable approaches of up to about −31%, 9% and 62% for tumoral volumes, DT and DNTL, respectively. The overall survival analysis showed statistically significant differences using DT cutoffs of 110, 90 and 85 Gy for the manual, rigid and deformable approaches, respectively. Conclusion: The semi-automatic transfer of VOIs from pre- and post-TARE imaging is feasible, but the selected method might affect prognostic DT/DNTL constraints.

We seek the development and evaluation of a fast, accurate, and consistent method for general-purpose segmentation, based on interactive machine learning (IML). To validate our method, we identified retrospective cohorts of 20 brain, 50 breast, and 50 lung cancer patients, as well as 20 spleen scans, with corresponding ground truth annotations. Utilizing very brief user training annotations and the adaptive geodesic distance transform, an ensemble of SVMs is trained, providing a patient-specific model applied to the whole image. Two experts segmented each cohort twice with our method and twice manually. The IML method was faster than manual annotation by 53.1% on average. We found significant (p < 0.001) overlap difference for spleen (DiceIML/DiceManual = 0.91/0.87), breast tumors (DiceIML/DiceManual = 0.84/0.82), and lung nodules (DiceIML/DiceManual = 0.78/0.83). For intra-rater consistency, a significant (p = 0.003) difference was found for spleen (DiceIML/DiceManual = 0.91/0.89). For inter-rater consistency, significant (p < 0.045) differences were found for spleen (DiceIML/DiceManual = 0.91/0.87), breast (DiceIML/DiceManual = 0.86/0.81), lung (DiceIML/DiceManual = 0.85/0.89), the non-enhancing (DiceIML/DiceManual = 0.79/0.67) and the enhancing (DiceIML/DiceManual = 0.79/0.84) brain tumor sub-regions, which, in aggregation, favored our method. Quantitative evaluation for speed, spatial overlap, and consistency, reveals the benefits of our proposed method when compared with manual annotation, for several clinically relevant problems. We publicly release our implementation through CaPTk (Cancer Imaging Phenomics Toolkit) and as an MITK plugin.

Various commercial auto-contouring solutions have emerged over past few years to address labor-intensiveness, and inter- and intra-operator variabilities issues of traditional manual anatomy contouring for head and neck (H&N) radiation therapy (RT). The purpose of this study is to compare the clinical performances between RaySearch Laboratories deep learning (DL) and atlas-based auto-contouring tools for organs at risk (OARs) segmentation in the H&N RT with the manual contouring as reference. Forty-five H&N computed tomography datasets were used for the DL and atlas-based auto-contouring tools to contour 16 OARs and time required for the segmentation was measured. Dice similarity coefficient (DSC), Hausdorff distance (HD) and HD 95th-percentile (HD95) were used to evaluate geometric accuracy of OARs contoured by the DL and atlas-based auto-contouring tools. Paired sample t-test was employed to compare the mean DSC, HD, HD95, and contouring time values of the two groups. The DL auto-contouring approach achieved more consistent performance in OARs segmentation than its atlas-based approach, resulting in statistically significant time reduction of the whole segmentation process by 40% (p < 0.001). The DL auto-contouring had statistically significantly higher mean DSC and lower HD and HD95 values (p < 0.001–0.009) for 10 out of 16 OARs. This study proves that the RaySearch Laboratories DL auto-contouring tool has significantly better clinical performances than its atlas-based approach.

Coronary computed tomography angiography (CCTA) is an effective imaging modality, increasingly accepted as a first-line test to diagnose coronary artery disease (CAD). The accurate segmentation of the coronary artery lumen on CCTA is important for the anatomical, morphological, and non-invasive functional assessment of stenoses. Hence, semi-automated approaches are currently still being employed. The processing time for a semi-automated lumen segmentation can be reduced by pre-selecting vessel locations likely to require manual inspection and by submitting only those for review to the radiologist. Detection of faulty lumen segmentation masks can be formulated as an Out-of-Distribution (OoD) detection problem. Two Normalizing Flows architectures are investigated and benchmarked herein: a Glow-like baseline, and a proposed one employing a novel coupling layer. Synthetic mask perturbations are used for evaluating and fine-tuning the learnt probability densities. Expert annotations on a separate test-set are employed to measure detection performance relative to inter-user variability. Regular coupling-layers tend to focus more on local pixel correlations and to disregard semantic content. Experiments and analyses show that, in contrast, the proposed architecture is capable of capturing semantic content and is therefore better suited for OoD detection of faulty lumen segmentations. When compared against expert consensus, the proposed model achieves an accuracy of 78.6% and a sensitivity of 76%, close to the inter-user mean of 80.9% and 79%, respectively, while the baseline model achieves an accuracy of 64.3% and a sensitivity of 48%.

The analysis of immunosuppressive drugs allows the physician to monitor, and eventually correct, immunosuppressive therapy. The panel of molecules under evaluation includes cyclosporine A (CsA), tacrolimus, sirolimus, and everolimus. Initially, assays were performed by immunometric methods, but in the past few years this methodology has been largely superseded by a more accurate and specific technique, liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS), which is now considered the “gold standard” for immunosuppressant analysis. Both LC-MS/MS and often also immunoassays require a preanalytical manual sample preparation, which involves time-consuming sequential operations whose traceability is often hampered and adds up to the probability of gross errors. The aim of this work was to compare an “open” LC-MS/MS with a fully automated system, consisting of LC instrumentation combined with a triple quadrupole MS, named Thermo ScientificTM CascadionTM SM Clinical Analyzer (Cascadion). Such automated systems suit the requirements of the reference method and are designed to completely eliminate all of the manual procedures. More than 2000 immunosuppressant samples were analyzed both with the open LC-MS/MS and with Cascadion. Statistics allowed the evaluation of linearity, intra- and inter-assay CV%, bias %, limit of detection and of quantitation, and Passing–Bablok and Bland–Altman plots. Results indicated a good correlation between the two methods. In both cases, methods confirmed their suitability for diagnostic settings. Cascadion could provide support when the presence of specialized personnel is lacking, and/or when great productivity and continuous workflow are required.

Abstract PURPOSE Accurate delineation of pediatric medulloblastoma (MB) tumors is required for accurate surgical resection and efficient treatment planning. However, manual delineation is time consuming and prone to errors and inter-reader variability. We present the first attempt at automatic segmentation of MB tumors via a transfer learning approach that utilizes adult brain tumor segmentations to optimize segmentation of 1) the pediatric tumor habitat, comprising enhancing tumor (ET), necrosis/non-enhancing tumor (NET), and edema sub-compartments, and 2) the individual tumor sub-compartments. METHODS Our cohort consisted of 300 adult tumor MRI scans (BRATS) and 78 pediatric MB scans (46 training, 32 testing) with Gd-T1w, T2w, and FLAIR protocols. Training set subjects were collected from Children’s Hospital of Los Angeles (N=18) and Cincinnati Children’s Hospital Medical Center (N=28), whereas test set subjects were collected from Children’s Hospital of Philadelphia. Preprocessing included age-specific atlas registration, skull stripping, and bias correction. Then, using nnUnet framework, we trained 3D- deep learning U-net models on BRATS dataset for the tumor sub-compartments: ET, edema, and NET + necrosis, as well as the tumor habitat. Our initial learning rate was 0.01, with stochastic gradient descent as optimizer, and an average of dice loss and cross-entropy loss as the loss function. We then performed transfer learning using Models Genesis on the pediatric subjects. RESULTS Our segmentation model yielded mean dice scores of 0.87± .02 for tumor habitat, .83± .04 for ET, .742±.05 for edema, and .54±.11 for NET + necrosis, across fivefold cross-validation runs. For test set, our model yielded mean dice scores of 0.80 for the tumor habitat, 0.67 for ET, 0.54 for edema, and 0.28 for NET + necrosis. CONCLUSION Our transfer learning model shows promise in accurate automatic delineation of the MB tumor habitat and its individual sub-compartments, towards efficient surgical and treatment planning in MB tumors.

Curtain wall glazing systems are a major part of a building due to the multiple roles they have, including occupant protection against environmental effects and the transfer of loads to the structural system. From a structural perspective, limited analytical guidelines and methods exist to aid designers in their determination of the curtain wall performance without extensive simulation or laboratory testing. This study takes experimental data from full-scale, “unitized”, four-sided structural sealant glazing (4SSG) curtain wall system mockups featuring a re-entrant corner subjected to cyclic racking displacements in accordance with the American Architectural Manufacturers Association AAMA 501.6 protocol to derive and establish equations that predict the relative displacements of the glass relative to the glazing frame, based on the amount of inter-story drift. Through derivation and testing, sealant cohesive failure and glass cracking were identified as limit states and corresponding drift levels were determined to control many of the equations. Displacements from the newly derived equations were correlated to the effective shear strain value experienced by the structural silicone in the mockup concurrently with the curtain wall’s drift capacity. This paper provides detailed derivation of the kinematic equations for possible use by glazing design professionals. Such equations can help designers to more easily predict the drifts that cause damage to such systems by manual calculations without the need for expensive mockup testing or time-intensive computer models.

Plant stomata are essential structures (pores) that control the exchange of gases between plant leaves and the atmosphere, and also they influence plant adaptation to climate through photosynthesis and transpiration stream. Many works in literature aim for a better understanding of these structures and their role in the evolution process and the behavior of plants. Although stomata studies in dicots species have advanced considerably in the past years, even there is not much knowledge about the stomata of cereal grasses. Due to the high morphological variation of stomata traits intra- and inter-species, detecting and classifying stomata automatically becomes challenging. For this reason, in this work, we propose a new system for automatic stomata classification and detection in microscope images for maize cultivars based on transfer learning strategy of different deep convolution neural netwoks (DCNN). Our performed experiments show that our system achieves an approximated accuracy of 97.1% in identifying stomata regions using classifiers based on deep learning features, which figures out as a nearly perfect classification system. As the stomata are responsible for several plant functionalities, this work represents an important advance for maize research, providing an accurate system in replacing the current manual task of categorizing these pores on microscope images. Furthermore, this system can also be a reference for studies using images from different cereal grasses.

Rice pests are one of the main factors affecting rice yield. The accurate identification of pests facilitates timely preventive measures to avoid economic losses. Some existing open source datasets related to rice pest identification mostly include only a small number of samples, or suffer from inter-class and intra-class variance and data imbalance challenges, which limit the application of deep learning techniques in the field of rice pest identification. In this paper, based on the IP102 dataset, we first reorganized a large-scale dataset for rice pest identification by Web crawler technique and manual screening. This dataset was given the name IP_RicePests. Specifically, the dataset includes 8248 images belonging to 14 categories. The IP_RicePests dataset was then expanded to include 14,000 images via ARGAN data augmentation technique to address the difficulties in obtaining large samples of rice pests. Finally, the parameters trained on the public image ImageNet dataset using VGGNet, ResNet and MobileNet networks were used as the initial values of the target data training network to achieve image classification in the field of rice pests. The experimental results show that all three classification networks combined with transfer learning have good recognition accuracy, among which the highest classification accuracy can be obtained on the IP_RicePests dataset via fine-tuning the parameters of the VGG16 network. In addition, following ARGAN data augmentation the dataset demonstrates high accuracy improvements in all three models, and fine-tuning the VGG16 network parameters obtains the highest accuracy in the augmented IP_RicePests dataset. It is demonstrated that CNN combined with transfer learning can employ the ARGAN data augmentation technique to overcome difficulties in obtaining large sample sizes and improve the efficiency of rice pest identification. This study provides foundational data and technical support for rice pest identification.

The analysis of bone marrow smears (BMS) serves as a critical diagnostic tool in hematology, offering valuable insights into the cellular composition and distinctive morphology of bone marrow cells. It enables the accurate diagnosis, classification, and monitoring of various hematologic diseases. Currently, both clinical routine diagnostics and scientific research, especially for the implementation of digital technologies, rely heavily on the manual selection of regions of interest (ROI) suitable for cell-level evaluation. Current guidelines recommend the manual evaluation of up to 500 cells per sample making the process time-consuming, cost-ineffective, subjective, and prone to human error. Consequently, significant inter-observer variability and inconsistencies in diagnosis often arise. Automated analysis of BMS using whole slide images (WSI) offers a promising approach to alleviate financial and time-related burdens while promoting standardization and streamlining of routine diagnostics. We have developed a machine learning approach utilizing a fully convolutional neural network (CNN) specifically designed for semantic segmentation based on the algorithm by Long et al. (IEEE Computer Society, 2015). The model is capable of learning deep semantic representations in its classification backbone combined with shape features in a shallow layer to produce detailed segmentations. The classification backbone consists of an adapted residual neural network with 50 layers (Resnet50) by He et al. (arXiv:1512.03385, 2015). To train the model, we curated a diverse dataset consisting of 60 different WSI BMS, each manually and independently annotated by at least two experts providing a robust ground truth (figure 1). Data augmentation was performed via horizontal flipping, random resizing, and random cropping. A train-test split of 80:20 was implemented with 5-fold internal cross-validation. The model's accuracy was evaluated as mean intersection-over-union ratio yielding high congruency between ground truth and model prediction (figure 2). The final model uses downscaled images effectively reducing data storage size and data transfer time decreasing fully automated ROI prediction time to &lt;300 ms for one WSI. The model allows for in-depth examination at the cellular level, harnessing the potential of thousands of relevant cells present in a WSI, in contrast to the limited range of 200 to 500 cells typically examined in conventional clinical routine diagnostics. Thereby, the model solves an essential bottleneck in computer vision in microscopy minimizing human manual labor and standardizing BMS processing results.

Lateral cephalometry has been widely used for skeletal classification in orthodontic diagnosis and treatment planning. However, this conventional system, requiring manual tracing of individual landmarks, contains possible errors of inter- and intravariability and is highly time-consuming. This study aims to provide an accurate and robust skeletal diagnostic system by incorporating a convolutional neural network (CNN) into a 1-step, end-to-end diagnostic system with lateral cephalograms. A multimodal CNN model was constructed on the basis of 5,890 lateral cephalograms and demographic data as an input. The model was optimized with transfer learning and data augmentation techniques. Diagnostic performance was evaluated with statistical analysis. The proposed system exhibited >90% sensitivity, specificity, and accuracy for vertical and sagittal skeletal diagnosis. Clinical performance of the vertical classification showed the highest accuracy at 96.40 (95% CI, 93.06 to 98.39; model III). The receiver operating characteristic curve and the area under the curve both demonstrated the excellent performance of the system, with a mean area under the curve >95%. The heat maps of cephalograms were also provided for deeper understanding of the quality of the learned model by visually representing the region of the cephalogram that is most informative in distinguishing skeletal classes. In addition, we present broad applicability of this system through subtasks. The proposed CNN-incorporated system showed potential for skeletal orthodontic diagnosis without the need for intermediary steps requiring complicated diagnostic procedures.

In computer vision, fine-grained classification has become an important issue in recognizing objects with slight visual differences. Usually, it is challenging to generate good performance when solving fine-grained classification problems using traditional convolutional neural networks. To improve the accuracy and training time of convolutional neural networks in solving fine-grained classification problems, this paper proposes a tree-structured framework by eliminating the effect of differences between clusters. The contributions of the proposed method include the following three aspects: (1) a self-supervised method that automatically creates a classification tree, eliminating the need for manual labeling; (2) a machine-learning matcher which determines the cluster to which an item belongs, minimizing the impact of inter-cluster variations on classification; and (3) a pruning criterion which filters the tree-structured classifier, retaining only the models with superior classification performance. The experimental evaluation of the proposed tree-structured framework demonstrates its effectiveness in reducing training time and improving the accuracy of fine-grained classification across various datasets in comparison with conventional convolutional neural network models. Specifically, for the CUB 200 2011, FGVC aircraft, and Stanford car datasets, the proposed method achieves a reduction in training time of 32.91%, 35.87%, and 14.48%, and improves the accuracy of fine-grained classification by 1.17%, 2.01%, and 0.59%, respectively.

Abstract. Prior to the beginning of the World Meteorological Organization's (WMO) Solid Precipitation Inter-Comparison Experiment (SPICE, 2013–2015), two precipitation measurement intercomparison sites were established in Saskatchewan to help assess the systematic bias in the automated gauge measurement of solid precipitation and the impact of wind on the undercatch of snow. Caribou Creek, located in the southern boreal forest, and Bratt's Lake, located in the southern plains, are a contribution to the international SPICE project but also to examine national and regional issues in measuring solid precipitation, including regional assessment of wind bias in precipitation gauges and windshield configurations commonly used in Canadian monitoring networks. Overlapping with WMO-SPICE, the Changing Cold Regions Network (CCRN) Special Observation and Analysis Period (SOAP) occurred from 2014 to 2015, involving other enhanced observations and cold regions research projects in the same geographical domain as the Saskatchewan SPICE sites. Following SPICE, the two Saskatchewan sites continued to collect core meteorological data (temperature, humidity, wind speed, etc.) as well as precipitation observations via several automated gauge configurations, including the WMO automated reference and the Meteorological Service of Canada's (MSC) network gauges. In addition, manual snow surveys to collect snow cover depth, density, and water equivalent were completed over the duration of the winter periods at the northern Caribou Creek site. Starting in the fall of 2013, the core intercomparison precipitation and ancillary data continued to be collected through the winter of 2017. Automated observations were obtained at a temporal resolution of 1 min, subjected to a rigorous quality control process, and aggregated to a resolution of 30 min. The manual snow surveys at Caribou Creek were typically performed every second week during the SPICE field program with monthly surveys following the end of the SPICE intercomparison period. The Saskatchewan SPICE data are available at https://doi.org/10.18164/63773b5b-5529-4b1e-9150-10acb84d59f0 (Smith and Yang, 2018). The data collected at the Saskatchewan SPICE sites will continue to be useful for transfer function testing, numerical weather prediction and hydrological forecasting verification, ground truth for remote-sensing applications, as well as providing reference precipitation measurements for other concurrent research applications in the cold regions.

The presence of foreign firms in a region can create spillovers to other firms, both within the intra-industries (industries itself) and inter-industries (other industries). Spillover from foreign firms can be in the form of technology transfer, efficiency improvement, managerial knowledge which ultimately is expected to increase productivity for other firms not only in large enterprises but also for micro small and medium enterprises (MSMEs). Hence, this paper aims to identify the potential backward spillover of foreign firms to MSMEs in East Java and the individual results show that the four highest backward spillovers values of foreign industries are industry of components and spare parts of the prime mover motor, industry of agricultural and forestry machinery and services for supporting, maintenance and repair, industry of pump and compressor and industry of measurement tools, manual regulator and testing. All of them are respectively located in Surabaya City, Pasuruan District, Pasuruan District and Mojekerto District. Furthermore, the general results show that the potential backward spillover exists in rubber, rubber goods and plastic goods industries as well as chemicals and goods from chemical material industries. The foreign firms which have highest backward spillovers are located in Pasuruan District, Mojekerto District, Sidoarjo District, Surabaya City and Gresik District. Therefore, the provincial government of East Java should immediately prepare and support both technically and financially to MSMEs in providing raw materials required by foreign firms based on the industrial classification and location results of this study. In addition, the provincial government of East Java can coordinate with the district governments in the areas of those foreign firms for preparing MSMEs in supplying raw materials. JEL Classification: F23, L60

Gastrointestinal (GI) tract diseases are on the rise in the world. These diseases can have fatal consequences if not diagnosed in the initial stages. WCE (wireless capsule endoscopy) is the advanced technology used to inspect gastrointestinal diseases such as ulcerative-colitis, polyps, esophagitis, and ulcers. WCE produces thousands of frames for a single patient’s procedure for which manual examination is tiresome, time-consuming, and prone to error; therefore, an automated procedure is needed. WCE images suffer from low contrast which increases inter-class and intra-class similarity and reduces the anticipated performance. In this paper, an efficient GI tract disease classification technique is proposed which utilizes an optimized brightness-controlled contrast-enhancement method to improve the contrast of the WCE images. The proposed technique applies a genetic algorithm (GA) for adjusting the values of contrast and brightness within an image by modifying the fitness function, which improves the overall quality of WCE images. This quality improvement is reported using qualitative measures, such as peak signal to noise ratio (PSNR), mean square error (MSE), visual information fidelity (VIF), similarity index (SI), and information quality index (IQI). As a second step, data augmentation is performed on WCE images by applying multiple transformations, and then, transfer learning is used to fine-tune a modified pre-trained model on WCE images. Finally, for the classification of GI tract disease, the extracted features are passed through multiple machine-learning classifiers. To show the efficacy of the proposed technique in the improvement in classification performance, the results are reported for the original dataset as well as the contrast-enhanced dataset. The results show an overall improvement of 15.26% in accuracy, 13.3% in precision, 16.77% in recall rate, and 15.18% in F-measure. Finally, a comparison with the existing techniques shows that the proposed framework outperforms the state-of-the-art techniques.

Acute myeloid leukemia (AML) is a type of acute leukemia that affects myeloid cells. It is characterized by uncontrolled proliferation of immature WBCs causing an accumulation of immature WBCs in the bone marrow and peripheral circulation. AML is an aggressive cancer that can be life-threatening if not treated promptly, therefore, early detection is crucial. Identification of immature WBCs and their subtypes is the first step towards AML diagnosis. Microscopic blood smear image analysis is commonly used to diagnose AML as it is less expensive non-invasive diagnostic tool compared to bone marrow biopsy and immunophenotype. Nonetheless, classifying immature WBCs presents challenges due to their high similarity and minimal interclass variation, especially for intermediate stages of myelopoiesis which make is susceptible to misclassification. The current diagnostic methods of AML are based on manual identification of immature WBCs using peripheral blood smear. These methos are time-consuming, prone to errors, and subject to inter-observer variation. Therefore, this study aims to develop a computer-aided diagnostic framework to accurately identify immature WBCs and classify them into various subtypes. The proposed framework consists of two main components: a binary classification system to classify cells into mature versus immature cells and a multiclassification system to further classify immature cells into four subtypes including myeloblast, monoblast, and promyelocytes. The proposed framework was designed to undergo four distinct phases: the first phase involves preprocessing, which includes data augmentation techniques aimed at addressing the imbalance distribution of WBCs. Augmentation encompasses geometric transformation and generation of synthetic images using convolutional Autoencoder (CAE). Feature extraction phase involves using image embedding and transfer-learning. Image embedding representation was obtained using CAE and then utilized by two pre-trained models: VGG19 and Resnet50. Classification phase was carried out using weighted ensemble of the VGG19 and Resnet50 with the optimal weights determined through Grid search. The validation phase evaluated the model performance using overall accuracy, precision, and sensitivity. The area under the ROC curve (AUC) was used to assess model discrimination ability. The proposed framework demonstrated excellent results, achieving an average accuracy of 99.5%, a sensitivity of 97.97%, and a precision of 94.12%. The model exhibited an excellent discriminatory capability, with an AUC of 99.9%. Furthermore, the proposed framework outperformed previous methods, showcasing improved performance. Key word: Acute myeloid leukemia, peripheral blood smear, immature white blood cells, deep learning, feature extraction, computer-aided diagnosis.

Dispatching, receiving, and transporting goods involve a large amount of manual effort. Within a logistics supply chain, a wide variety of transported goods need to be handled, recognized, and checked at many different points. Effective planning of automated guided vehicle (AGV) transportation can reduce equipment energy consumption and shorten task completion time. As the need for efficient warehouse logistics has increased in manufacturing systems, the use of AGVs has also increased to reduce working time. These processes hold automation potential, which we can exploit by using computer vision techniques. We propose a method for the complete automation of box recognition, covering both the types and quantities of boxes. To do this, an ELAN and GhostConv-based YOLO network (EGCY-Net) is proposed with a Conv-GhostConv Stack (CGStack) module and an ELAN-GhostConv Network (EGCNet). To enhance inter-channel relationships, the CGStack module captures complex patterns and information in the image by using ghost convolution to increase the model inference speed while retaining the ability to capture spatial features. EGCNet is designed and constructed based on ELAN and the CGStack module to capture and utilize hierarchical features efficiently in layer aggregation. Additionally, the proposed methodology involves the creation of a dataset comprising images of boxes taken in warehouse settings. The proposed system is realized on the NVIDIA Jetson Nano platform, using an Arducam IMX477 camera. To evaluate the proposed model, we conducted experiments with our own dataset and compared the results with some state-of-the-art (SOTA) models. The proposed network achieved the highest detection accuracy with the fewest parameters compared to other SOTA models.

“Digital” is the new popular expression in all sectors. With other sector, banking is additionally all around the world moving towards digitalization. Banks all things considered and over all areas are making immense investments in digital activities so as to keep up a competitive edge and convey the greatest to its customers. Selection of digitalization is significant for the banking sector. By grasping digitalization, banks can provide upgraded customer services. This provides accommodation to customers and aides in sparing time. Digitalization has transformed manual processes, transactions and exercises into digital services. Over all verticals, buyer needs have been met in entirely imaginative manners, upsetting existing enterprise esteem chains. Digitalization reduces human blunder and in this manner constructs customer unwaveringness. Today, individuals have nonstop access to banks due to online banking. Managing a lot of cash has additionally become simpler. Digitalization has additionally profited customers by encouraging cashless transactions. Customers need not store cash any longer and can make transactions at wherever and time. A few commercial banks began moving towards digital customer services to stay competitive and relevant in the race. Banks have profited in a few different ways by receiving more current innovations. E-banking has brought about reducing costs definitely and has created revenue through different channels. Commercial Banks in India have moved towards innovation by method for Bank Mechanization and Automation with the prologue to MICR based check processing, Electronic Funds transfer, Inter-availability among bank Branches and implementation of ATM (Automated Teller Machine) Channel have brought about the accommodation of Anytime banking. Solid activities are taken by the Reserve Bank of India for reinforces the Payment and Settlement systems in the banks. Indian government, banks, fintech companies have been advancing and changing the manner in which India spends its money. Simultaneously digital revolution additionally raises new challenges to the solidness and the uprightness of the financial system and the protection of buyers. Hence, the present study has been done on the digital revolution in the Indian Banking sector and the study based on the secondary sources of data.

Background: Using artificial intelligence (AI) with the concept of a deep learning-based automated computer-aided diagnosis (CAD) system has shown improved performance for skin lesion classification. Although deep convolutional neural networks (DCNNs) have significantly improved many image classification tasks, it is still difficult to accurately classify skin lesions because of a lack of training data, inter-class similarity, intra-class variation, and the inability to concentrate on semantically significant lesion parts. Innovations: To address these issues, we proposed an automated deep learning and best feature selection framework for multiclass skin lesion classification in dermoscopy images. The proposed framework performs a preprocessing step at the initial step for contrast enhancement using a new technique that is based on dark channel haze and top–bottom filtering. Three pre-trained deep learning models are fine-tuned in the next step and trained using the transfer learning concept. In the fine-tuning process, we added and removed a few additional layers to lessen the parameters and later selected the hyperparameters using a genetic algorithm (GA) instead of manual assignment. The purpose of hyperparameter selection using GA is to improve the learning performance. After that, the deeper layer is selected for each network and deep features are extracted. The extracted deep features are fused using a novel serial correlation-based approach. This technique reduces the feature vector length to the serial-based approach, but there is little redundant information. We proposed an improved anti-Lion optimization algorithm for the best feature selection to address this issue. The selected features are finally classified using machine learning algorithms. Main Results: The experimental process was conducted using two publicly available datasets, ISIC2018 and ISIC2019. Employing these datasets, we obtained an accuracy of 96.1 and 99.9%, respectively. Comparison was also conducted with state-of-the-art techniques and shows the proposed framework improved accuracy. Conclusions: The proposed framework successfully enhances the contrast of the cancer region. Moreover, the selection of hyperparameters using the automated techniques improved the learning process of the proposed framework. The proposed fusion and improved version of the selection process maintains the best accuracy and shorten the computational time.

Abstract. Land use land cover (LULC) classification is an essential task in Earth Observation (EO) as it helps in monitoring long-term developments, detecting changes and analysing their environmental impacts. Due to advancements in remote sensing, there is an abundance of open data available but annotating this data is expensive. As a result, many research works in EO create a labelled dataset for one selected region and perform a corresponding regional analysis. By employing transfer learning, we can reuse these labelled datasets for different regions and thereby minimize the manual annotation costs. However, there are some open questions: to what extent can the features learned in one region be transferred to another? Does a larger pre-training dataset mean better transfer learning performance? How can we estimate the transfer learning performance? To answer these questions, we divide a large EO dataset called BigEarthNet into sub-datasets by region and perform region to region transfer learning. We find that the models trained on one region do not perform well on another region. We applied transfer learning techniques and showed that the class imbalance can hinder learning. If the source region has additional classes which are dominant in the source region or has fewer images for the classes dominant in the target region, transfer learning can have negative impacts on the model performance in the target region. We also demonstrate the use of chi-squared distance in selecting an appropriate source region for transfer learning.

BackgroundThe diagnosis of rheumatoid arthritis is based on clinical, biological and imaging criteria [1]. Among these criteria, the quantification of the lesions through X-ray images is essential for the establishment of therapeutic strategies and the clinical follow-up of the patient [2]. The Sharp/van der Heijde (SvH) method is used to assess the severity of bone erosion and joint narrowing on X-rays of hands and feet: it is frequently used in clinical practices and observational studies [3]. The SvH manual scoring process requires a high level of expertise and is time consuming. It can also suffer from inter-observer interpretation variability.ObjectivesThe objective of this study is to use convolutional neural networks to automate the scoring of SvH and the classification of patients on this scale.MethodsThis work uses a public database of 3,818 X-rays of both hands. Each X-ray is paired with a rating of the disease’ severity evaluated with the SvH score. This score has values between 0 and 280 because only both hands are used.The database was separated into a training, validation and testing set (64%, 16% and 20% respectively). Two convolutional neural networks (CNN) were trained. The first predicts the value of the SvH score. The second classifies patients into two groups, depending on their score: less or more than 70.To improve the performance of the algorithms, various techniques have been implemented: transfer learning, data augmentation functions (rotation, flipping, noise) and hyperparameter and architecture search.ResultsThe prediction network has an average error of 16.00 (std 17.75), with a Pearson correlation of 0.86. Its results are presented in the figure with individual results and confidence intervals.The classification algorithm obtained a mean value of the area under the curve (AUC) of 0.97, an accuracy of 0.84 and a positive predictive value of 0.91.ConclusionThis study demonstrates the interest of using artificial intelligence algorithms in the diagnosis and standardized follow-up of patients with rheumatoid arthritis.These could help clinicians in the evaluation of osteoarticular lesions and the stratification of patients according to the severity of the lesions with great precision.References[1]Funovits J, Aletaha D, Bykerk V, Combe B, Dougados M, Emery P etal.Rheumatism classification criteria for rheumatoid arthritis: methodological report Phase 1. Ann Rheum Dis 2010; 69: 1589–95.[2]Colebatch AN, Edwards CJ, Ostergaard M,. EULAR recommendations for the use of imaging of the joints in the clinical management of rheumatoid arthritis. Ann Rheum Dis 2013;72:804-14.[3]Van der Heijde, D. M. F. M. (1999). How to read radiographs according to the Sharp/van der Heijde method.The Journal of rheumatology,26(3), 743-745.Figure 1.Acknowledgements:NIL.Disclosure of InterestsNone Declared.

ABSTRACTThis study examined intergenerational family contact. Three questions were considered: Is there a relationship between parent's class, child's class and family contact? Can class-related differences in family contact be explained by differences in geographical distance between parent and child? Is intergenerational family contact affected by children's social mobility? The questions were explored using data from a nationally-representative level of living survey. The results from logistic regressions showed that parent's class as well as the child's class were associated with intergenerational geographical distance and family contact more often than once a week. Those in or retired from non-manual occupations were less likely than manual workers to live close and to have family contact more than once a week. We found no evidence that a change in class position, upward nor downward, had any effect on family contacts. Rather, class-stable non-manual families socialise less frequently than other families, even when they live relatively close. The results therefore suggest that familial class-cohesiveness is a stronger determinant of inter-generational family contacts than social mobility. Future research should address the complex connection between social mobility and other forms of relations and transfers between generations.

Introduction: Flow cytometry is an integral part of routine diagnostics for hematological malignancies. Unfortunately, a decreasing number of skilled operators have to cope with increasing case volumes. The current methods are also known to produce inter- and intra-observer variability. Aim: To reduce labor time, dependency on expert knowledge and interpretation variability, we aimed to build and validate clinical-grade decision support software in the routine using machine learning (ML) for clinical cytometry. Methods: We obtained ≈100k flow cytometry cases from multiple centers, taken from clinical routine and annotated by expert users using multiple diagnostic methods. These cases span the full spectrum of hematological malignancy, including 47% non-malignant reference samples. As the data were not measured using harmonized protocols, we developed data pooling techniques to merge the data, including synthetic imputation of missing markers. After data processing, we applied multi-layer artificial neural networks to predict sub-type or non-malignancy (B-NHL and acute leukemias, ten classes at time of writing), directly from the data without any human preprocessing. Transfer learning was then used to fine-tune the model to a particular protocol for which the model could be used in routine. Besides sub-typing recommendations, users need to report cell population frequencies, traditionally measured using manual ‘gating’. We trained separate supervised ML models to classify single cells based on a combination of curated and refined routine gating data, expanded with in-house annotations produced by a highly trained cytometrist. To enable clinicians to receive the diagnostic recommendations and produce reports for the treating doctor, we built a CE-IVD web-application (“hema.to”) in which selected users could upload cytometry data, inspect the raw data including abnormalities in the immunophenotype, and audit the ML recommendations against the WHO criteria. The user feedback from each case was recorded, from which the consistency of the ML recommendations with expert opinion could be compared. Before the product was integrated in the clinical routine, a ‘dry run’ retrospective two-arm, four-center blinded clinical study using 96 randomly selected non-overlapping B-NHL cases revealed that analysis time was reduced by &gt;2x and accuracy saw a slight (non-significant) increase against a gold standard as compared to the control arm with a traditional workflow. Results: A worst-case sub-typing performance was computed by testing the model against historic data without expert supervision, and found an f1 score of &gt;90% and sensitivity of 96%. The ML models for acute leukemias specifically show an f1 score of ≈90%, a false positive rate of ≈1.5% and false negative rate of 2-6%. These quality metrics demonstrate that the system can be used for both screening and sub-typing. We therefore integrated hema.to into the routine for B-NHL diagnosis at Result Laboratorium (Dordrecht) and the HpH (Hamburg) since January, for the latter including a deep integration into the laboratory database with automated reporting of expressed CD markers. To date, over a thousand routine cases have been analyzed using the system. We then measured the relationship between predicted sub-typing confidence of the model and agreement with the final expert judgment. The data showed that, on average, confidence≈accuracy ( R2=0.94), indicating that the model is neither over- nor underconfident. As such, the model is a “trustworthy” classifier. Expert agreement over time revealed that the quality of the diagnostic recommendations has been stable over time despite a change of device type and was consistently in excess of 90% across various metrics, including a 100% specificity during the four most recent recorded months. Conclusions: We've shown that machine learning can offer decision support and automated reporting for screening and classifying blood cancers from flow cytometry in a real-world routine setting. Our next focus is to expand the number of indications and increase the model sensitivity to small pathological populations, including measurable residual disease. These results represent major strides towards decision support software for any lab and all hematological neoplasms. Such a software will not only speed up and simplify diagnostic workflows, but also improve the quality of the analysis.

The advent of autonomous vehicles (AVs) in the traffic stream is expected to innovatively prevent crashes resulting from human errors in manually driven vehicles (MVs). However, substantial safety benefits due to AVs are not achievable quickly because the mixed-traffic conditions in which AVs and MVs coexist in the current road infrastructure will continue for a considerably long period of time. The purpose of this study is to develop a methodology to evaluate the driving safety of mixed car-following situations between AVs and MVs on freeways based on a multi-agent driving-simulation (MADS) technique. Evaluation results were used to answer the question ‘What road condition would make the mixed car-following situations hazardous?’ Three safety indicators, including the acceleration noise, the standard deviation of the lane position, and the headway, were used to characterize the maneuvering behavior of the mixed car-following pairs in terms of driving safety. It was found that the inter-vehicle safety of mixed pairs was poor when they drove on a road section with a horizontal curve length of 1000 m and downhill slope of 1% or 3%. A set of road sections were identified, using the proposed evaluation method, as hazardous conditions for mixed car-following pairs consisting of AVs and MVs. The outcome of this study will be useful for supporting the establishment of safer road environments and developing novel V2X-based trafficsafetyinformation content that enables the enhancement of mixed-traffic safety.

Early childhood emotional dysregulation is critical in recognizing and preventing psychological well-being disorders, laying the groundwork for developing healthy emotional behaviors early on. This study aims to determine the direct influence of smartphone addiction, executive function, and the mother-child relationship on emotional dysregulation in early childhood in West Sumatra. This research method is a quantitative survey. The data collection technique in this research uses a questionnaire design on 309 parents who were selected using a simple random sampling method. This data processing tool uses the SmartPLS software. The results of the study indicate that smartphone addiction has a significant impact on emotional dysregulation in early childhood, executive function has a positive and significant effect on emotional dysregulation in early childhood, and the mother-child relationship has a positive and significant influence on emotional dysregulation in early childhood. 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Introducing existing, improved or radically new process technology in the process industries is not finished until it is operating well at the firm’s premises; a fact of growing importance in the perspective of digital transformation. However, since it is uncommon for firms in the process industries to develop own process technology, they are dependent on suppliers for the acquisition of new equipment. Relying on the development of an organisational framework, the authors’ industrial experiences, and a literature review, success factors for inter-firm process technology transfer were initially developed. To facilitate the company use, and as an illustrative case, the success factors were afterwards included in a survey to informants in the petrochemical industries. The findings indicate that companies would benefit from the development of an internal guide for inter-firm process technology transfer. The success factors could not only be used as components in such a manual, but also serve as a “check-list” for internal improvement programs for process technology transfer.

AbstractAccurate brain meningioma segmentation and volumetric assessment are critical for serial patient follow-up, surgical planning and monitoring response to treatment. Current gold standard of manual labeling is a time-consuming process, subject to inter-user variability. Fully-automated algorithms for meningioma segmentation have the potential to bring volumetric analysis into clinical and research workflows by increasing accuracy and efficiency, reducing inter-user variability and saving time. Previous research has focused solely on segmentation tasks without assessment of impact and usability of deep learning solutions in clinical practice. Herein, we demonstrate a three-dimensional convolutional neural network (3D-CNN) that performs expert-level, automated meningioma segmentation and volume estimation on MRI scans. A 3D-CNN was initially trained by segmenting entire brain volumes using a dataset of 10,099 healthy brain MRIs. Using transfer learning, the network was then specifically trained on meningioma segmentation using 806 expert-labeled MRIs. The final model achieved a median performance of 88.2% reaching the spectrum of current inter-expert variability (82.6–91.6%). We demonstrate in a simulated clinical scenario that a deep learning approach to meningioma segmentation is feasible, highly accurate and has the potential to improve current clinical practice.

Rationale: LV segmentation is crucial to analyzing heart pathologies. DL applications for LGE-CMR image segmentation offer automation and reliability over costly feature learning and manual input. However, existing DL algorithms suffer from poor quality and quantity of training data, lack of clinical relevance in evaluation methods, and low generalizability. Objective: We propose a neural network approach to LV segmentation with superior performance and ensured anatomical fidelity on various imaging protocols and patient populations, with direct clinical evaluation via LV volume calculation and ventricular arrhythmia (VA) risk assessment. Methods: We train a multistage network for LV segmentation on 4,000 LGE-CMR (PROSe-ICD study) and cine (MICCAI 2009 and 2017 datasets) short-axis images, where cine scans augment the data via a cine-to-LGE style transfer. At testing, images enter a network that crops and refines the region-of-interest (fig. 1A), which is fed into a network for differentiation of viable and hyper-enhanced myocardium (fig. 1B). An autoencoder network takes the union of these images, encodes them to a densely populated, reduced space, finds the nearest neighbor, and decodes them to anatomically correct segmented scans (fig. 1C). To assess VA risk, we fit a Cox regression model to survival data with image features (ex. LV volume) from manual or predicted segmentations. Results: Our segmented LGE-CMR scans (0.81 Dice score) surpass an inter-observer Dice score of 0.76, upholding anatomical guidelines across ambiguous regions such as apex and base (fig. 1D). Differences in calculated LV volumes (accuracy 98.4%, precision 78.9%) and C-indices of Cox regression models that stratify VA risk using automatic versus manual segmentations (0.62 vs. 0.60) show no statistical significance. Conclusion: We developed a DL LV segmentation algorithm tailored to clinical application, ensuring relevant predictions and outperforming inter-observed Dice scores.

BackgroundAccurate delineation of the midbrain nuclei, the red nucleus (RN), substantia nigra (SN) and subthalamic nucleus (STN), is important in neuroimaging studies of neurodegenerative and other diseases. This study aims to segment midbrain structures in high-resolution susceptibility maps using a method based on a convolutional neural network (CNN).MethodsThe susceptibility maps of 75 subjects were acquired with a voxel size of 0.83 × 0.83 × 0.80 mm3 on a 3T MRI system to distinguish the RN, SN, and STN. A deeply supervised attention U-net was pre-trained with a dataset of 100 subjects containing susceptibility maps with a voxel size of 0.63 × 0.63 × 2.00 mm3 to provide initial weights for the target network. Five-fold cross-validation over the training cohort was used for all the models’ training and selection. The same test cohort was used for the final evaluation of all the models. Dice coefficients were used to assess spatial overlap agreement between manual delineations (ground truth) and automated segmentation. Volume and magnetic susceptibility values in the nuclei extracted with automated CNN delineation were compared to those extracted by manual tracing. Consistencies of volume and magnetic susceptibility values by different extraction strategies were assessed by Pearson correlation coefficients and Bland-Altman analyses.ResultsThe automated CNN segmentation method achieved mean Dice scores of 0.903, 0.864, and 0.777 for the RN, SN, and STN, respectively. There were no significant differences between the achieved Dice scores and the inter-rater Dice scores (p &gt; 0.05 for each nucleus). The overall volume and magnetic susceptibility values of the nuclei extracted by the automatic CNN method were significantly correlated with those by manual delineation (p &lt; 0.01).ConclusionMidbrain structures can be precisely segmented in high-resolution susceptibility maps using a CNN-based method.

BackgroundIdentification of lung parenchyma on computer tomographic (CT) scans in the research setting is done semi-automatically and requires cumbersome manual correction. This is especially true in pathological conditions, hindering the clinical application of aeration compartment (AC) analysis. Deep learning based algorithms have lately been shown to be reliable and time-efficient in segmenting pathologic lungs. In this contribution, we thus propose a novel 3D transfer learning based approach to quantify lung volumes, aeration compartments and lung recruitability.MethodsTwo convolutional neural networks developed for biomedical image segmentation (uNet), with different resolutions and fields of view, were implemented using Matlab. Training and evaluation was done on 180 scans of 18 pigs in experimental ARDS (u2NetPig) and on a clinical data set of 150 scans from 58 ICU patients with lung conditions varying from healthy, to COPD, to ARDS and COVID-19 (u2NetHuman). One manual segmentations (MS) was available for each scan, being a consensus by two experts. Transfer learning was then applied to train u2NetPig on the clinical data set generating u2NetTransfer. General segmentation quality was quantified using the Jaccard index (JI) and the Boundary Function score (BF). The slope between JI or BF and relative volume of non-aerated compartment (SJI and SBF, respectively) was calculated over data sets to assess robustness toward non-aerated lung regions. Additionally, the relative volume of ACs and lung volumes (LV) were compared between automatic and MS.ResultsOn the experimental data set, u2NetPig resulted in JI = 0.892 [0.88 : 091] (median [inter-quartile range]), BF = 0.995 [0.98 : 1.0] and slopes SJI = −0.2 {95% conf. int. −0.23 : −0.16} and SBF = −0.1 {−0.5 : −0.06}. u2NetHuman showed similar performance compared to u2NetPig in JI, BF but with reduced robustness SJI = −0.29 {−0.36 : −0.22} and SBF = −0.43 {−0.54 : −0.31}. Transfer learning improved overall JI = 0.92 [0.88 : 0.94], P &lt; 0.001, but reduced robustness SJI = −0.46 {−0.52 : −0.40}, and affected neither BF = 0.96 [0.91 : 0.98] nor SBF = −0.48 {−0.59 : −0.36}. u2NetTransfer improved JI compared to u2NetHuman in segmenting healthy (P = 0.008), ARDS (P &lt; 0.001) and COPD (P = 0.004) patients but not in COVID-19 patients (P = 0.298). ACs and LV determined using u2NetTransfer segmentations exhibited &lt; 5% volume difference compared to MS.ConclusionCompared to manual segmentations, automatic uNet based 3D lung segmentation provides acceptable quality for both clinical and scientific purposes in the quantification of lung volumes, aeration compartments, and recruitability.

AbstractInfrared thermography (IRT) is widely used to assess skin temperature in response to physiological changes. Yet, it remains challenging to standardize skin temperature measurements over repeated datasets. We developed an open-access semi-automated segmentation tool (the IRT-toolbox) for measuring skin temperatures in the thoracic area to estimate supraclavicular brown adipose tissue (scBAT) activity, and compared it to manual segmentations. The IRT-toolbox, designed in Python, consisted of image pre-alignment and non-rigid image registration. The toolbox was tested using datasets of 10 individuals (BMI = 22.1 ± 2.1 kg/m2, age = 22.0 ± 3.7 years) who underwent two cooling procedures, yielding four images per individual. Regions of interest (ROIs) were delineated by two raters in the scBAT and deltoid areas on baseline images. The toolbox enabled direct transfer of baseline ROIs to the registered follow-up images. For comparison, both raters also manually drew ROIs in all follow-up images. Spatial ROI overlap between methods and raters was determined using the Dice coefficient. Mean bias and 95% limits of agreement in mean skin temperature between methods and raters were assessed using Bland–Altman analyses. ROI delineation time was four times faster with the IRT-toolbox (01:04 min) than with manual delineations (04:12 min). In both anatomical areas, there was a large variability in ROI placement between methods. Yet, relatively small skin temperature differences were found between methods (scBAT: 0.10 °C, 95%LoA[-0.13 to 0.33 °C] and deltoid: 0.05 °C, 95%LoA[-0.46 to 0.55 °C]). The variability in skin temperature between raters was comparable between methods. The IRT-toolbox enables faster ROI delineations, while maintaining inter-user reliability compared to manual delineations. (Trial registration number (ClinicalTrials.gov): NCT04406922, [May 29, 2020]).

Image archives of multi-class structural damages can be collected by manual inspection and then used for structural damage identification. On one hand, conventional image-processing-based approaches rely on optimal designs of hand-crafted feature detectors and lack universal adaptability for various application cases; on the other hand, regular supervised learning techniques require complete damage types and sufficient training examples to establish a robust damage recognition model, which brings up a time-labor-consuming image collection process. To solve these problems, this study proposes a nested attribute-based few-shot meta learning paradigm for structural damage identification. First, an external few-shot meta learning module is established based on different classification tasks named as meta-batches to produce robust classifiers for new damage types, in which support and query subsets including partial damage types and a few examples are randomly sampled from the original image dataset. Second, an embedded internal attribute-based transfer learning model is trained by minimizing the l2-norm and angular losses of attribute representation vectors in an end-to-end manner, where damage attributes act as the common inter-class knowledge and are transferred from the source damage space of support set to the target damage space of query set. Finally, the proposed approach is validated on a real-world structural damage image dataset, which contains 1000 examples of 10 representative damage types in total. Results show the proposed approach produces an overall accuracy of 93.5% and an average area under the ROC curve of 0.96 for 10 damage types. The general equilibrium of average precision and recall indicates that the trained model is balanced to both positive and negative examples for each damage type. Compared with a regular supervised learning model by directly classifying input images with one-hot vector labels, the proposed approach generates higher accuracy and better robustness. Parameter study suggests the proposed paradigm enables to train a stable and reliable meta learning classification model that can perform well across a series of settings about the ratio between support and query subsets. Theoretical analysis is also performed to explain why meta learning surpasses regular supervised learning.