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Journal articles on the topic 'CAD Reader'

Author: Grafiati

Published: 28 June 2021

Last updated: 1 February 2022

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1

Yongping, Liang, Ping Zhou, Zhang Juan, Zhao Yongfeng, Wengang Liu, and Yifan Shi. "Performance of Computer-Aided Diagnosis in Ultrasonography for Detection of Breast Lesions Less and More Than 2 cm: Prospective Comparative Study." JMIR Medical Informatics 8, no. 3 (March 2, 2020): e16334. http://dx.doi.org/10.2196/16334.

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Background Computer-aided diagnosis (CAD) is used as an aid tool by radiologists on breast lesion diagnosis in ultrasonography. Previous studies demonstrated that CAD can improve the diagnosis performance of radiologists. However, the optimal use of CAD on breast lesions according to size (below or above 2 cm) has not been assessed. Objective The aim of this study was to compare the performance of different radiologists using CAD to detect breast tumors less and more than 2 cm in size. Methods We prospectively enrolled 261 consecutive patients (mean age 43 years; age range 17-70 years), including 398 lesions (148 lesions>2 cm, 79 malignant and 69 benign; 250 lesions≤2 cm, 71 malignant and 179 benign) with breast mass as the prominent symptom. One novice radiologist with 1 year of ultrasonography experience and one experienced radiologist with 5 years of ultrasonography experience were each assigned to read the ultrasonography images without CAD, and then again at a second reading while applying the CAD S-Detect. We then compared the diagnostic performance of the readers in the two readings (without and combined with CAD) with breast imaging. The McNemar test for paired data was used for statistical analysis. Results For the novice reader, the area under the receiver operating characteristic curve (AUC) improved from 0.74 (95% CI 0.67-0.82) from the without-CAD mode to 0.88 (95% CI 0.83-0.93; P<.001) at the combined-CAD mode in lesions≤2 cm. For the experienced reader, the AUC improved from 0.84 (95% CI 0.77-0.90) to 0.90 (95% CI 0.86-0.94; P=.002). In lesions>2 cm, the AUC moderately decreased from 0.81 to 0.80 (novice reader) and from 0.90 to 0.82 (experienced reader). The sensitivity of the novice and experienced reader in lesions≤2 cm improved from 61.97% and 73.23% at the without-CAD mode to 90.14% and 97.18% (both P<.001) at the combined-CAD mode, respectively. Conclusions S-Detect is a feasible diagnostic tool that can improve the sensitivity for both novice and experienced readers, while also improving the negative predictive value and AUC for lesions≤2 cm, demonstrating important application value in the clinical diagnosis of breast cancer. Trial Registration Chinese Clinical Trial Registry ChiCTR1800019649; http://www.chictr.org.cn/showprojen.aspx?proj=33094

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Yongping, Liang, Zhang Juan, Ping Zhou, Zhao Yongfeng, Wengang Liu, and Yifan Shi. "Evaluation of the Quadri-Planes Method in Computer-Aided Diagnosis of Breast Lesions by Ultrasonography: Prospective Single-Center Study." JMIR Medical Informatics 8, no. 5 (May 5, 2020): e18251. http://dx.doi.org/10.2196/18251.

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Background Computer-aided diagnosis (CAD) is a tool that can help radiologists diagnose breast lesions by ultrasonography. Previous studies have demonstrated that CAD can help reduce the incidence of missed diagnoses by radiologists. However, the optimal method to apply CAD to breast lesions using diagnostic planes has not been assessed. Objective The aim of this study was to compare the performance of radiologists with different levels of experience when using CAD with the quadri-planes method to detect breast tumors. Methods From November 2018 to October 2019, we enrolled patients in the study who had a breast mass as their most prominent symptom. We assigned 2 ultrasound radiologists (with 1 and 5 years of experience, respectively) to read breast ultrasonography images without CAD and then to perform a second reading while applying CAD with the quadri-planes method. We then compared the diagnostic performance of the readers for the 2 readings (without and with CAD). The McNemar test for paired data was used for statistical analysis. Results A total of 331 patients were included in this study (mean age 43.88 years, range 17-70, SD 12.10), including 512 lesions (mean diameter 1.85 centimeters, SD 1.19; range 0.26-9.5); 200/512 (39.1%) were malignant, and 312/512 (60.9%) were benign. For CAD, the area under the receiver operating characteristic curve (AUC) improved significantly from 0.76 (95% CI 0.71-0.79) with the cross-planes method to 0.84 (95% CI 0.80-0.88; P<.001) with the quadri-planes method. For the novice reader, the AUC significantly improved from 0.73 (95% CI 0.69-0.78) for the without-CAD mode to 0.83 (95% CI 0.80-0.87; P<.001) for the combined-CAD mode with the quadri-planes method. For the experienced reader, the AUC improved from 0.85 (95% CI 0.81-0.88) to 0.87 (95% CI 0.84-0.91; P=.15). The kappa indicating consistency between the experienced reader and the novice reader for the combined-CAD mode was 0.63. For the novice reader, the sensitivity significantly improved from 60.0% for the without-CAD mode to 79.0% for the combined-CAD mode (P=.004). The specificity, negative predictive value, positive predictive value, and accuracy improved from 84.9% to 87.8% (P=.53), 76.8% to 86.7% (P=.07), 71.9% to 80.6% (P=.13), and 75.2% to 84.4% (P=.12), respectively. For the experienced reader, the sensitivity improved significantly from 76.0% for the without-CAD mode to 87.0% for the combined-CAD mode (P=.045). The NPV and accuracy moderately improved from 85.8% and 86.3% to 91.0% (P=.27) and 87.0% (P=.84), respectively. The specificity and positive predictive value decreased from 87.4% to 81.3% (P=.25) and from 87.2% to 93.0% (P=.16), respectively. Conclusions S-Detect is a feasible diagnostic tool that can improve the sensitivity, accuracy, and AUC of the quadri-planes method for both novice and experienced readers while also improving the specificity for the novice reader. It demonstrates important application value in the clinical diagnosis of breast cancer. Trial Registration ChiCTR.org.cn 1800019649; http://www.chictr.org.cn/showproj.aspx?proj=33094

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Chu, Gregory H., Matthew S. Brown, Hyun J. Kim, Andrew Kaplan, Katherine Yang, Martin Allen-Auerbach, Cheryce Poon, et al. "Computer-aided bone scan lesion area quantitation: Inter-reader measurement variability." Journal of Clinical Oncology 31, no. 15_suppl (May 20, 2013): e16019-e16019. http://dx.doi.org/10.1200/jco.2013.31.15_suppl.e16019.

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e16019 Background: Bone scan is essential for response assessment in subjects with metastatic prostate cancer. However, visual assessment is subjective and prone to inter-reader variability. A commercial computer-aided (CAD) system for bone scan lesion area (BSLA) calculation has been developed (MedQIA, LLC). BSLA provides an objective and sensitive measure for response assessment. New VEGF treatments showing promising results in reducing lesion burden on bone scan present an urgent need for validated systems capable of accurately assessing treatment response. Measurement of inter reader variability is fundamental to such a validation. In this work we measure inter-reader variability in quantitation of BSLA change using the CAD system. Methods: In a multi-center clinical trial of cabozantinib (Exelixis, Inc), bone scans were acquired according to a standardized imaging protocol. Scans at baseline and Week 12 were available for 113 subjects for this analysis. Automated CAD lesion detection was performed following image intensity normalization to increase consistency between subject time points. Two nuclear medicine physicians independently edited and approved the CAD result as needed, primarily to remove false positives due to degenerative joint disease. The system then calculated the percent change in BSLA at Week 12 from baseline (BSLAPCT). The BLSAPCT values from the two readers were compared to assess inter-reader variability. Additional analytic validation of the BSLA measure was performed in which the readers provided a categorical assessment of responder vs non-responder, using the CAD-normalized images, and the classification performance of BSLAPCT was assessed using ROC analysis. Results: The median (IQR) difference in BSLAPCT between the two readers was -1.8 (20.0) percentage points. The absolute difference was 9.4 (23.9) percentage points. The BSLAPCT response classification had an ROC area under curve (AUC) of 0.96 for both readers. Conclusions: Computer-aided quantitative assessment of change using bone scan lesion area demonstrated low variability between two readers. This is an important step toward the validation of an objective, quantitative, and sensitive response assessment technique.

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Gallas, Brandon D., and David G. Brown. "Reader studies for validation of CAD systems." Neural Networks 21, no. 2-3 (March 2008): 387–97. http://dx.doi.org/10.1016/j.neunet.2007.12.013.

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Koshkin, Vadim S., J. Louis Hinshaw, Kristen Wroblewski, and Abraham H. Dachman. "CAD-associated Reader Error in CT Colonography." Academic Radiology 19, no. 7 (July 2012): 801–10. http://dx.doi.org/10.1016/j.acra.2012.03.008.

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White, Charles S., Robert Pugatch, Thomas Koonce, Steven W. Rust, and Ekta Dharaiya. "Lung Nodule CAD Software as a Second Reader." Academic Radiology 15, no. 3 (March 2008): 326–33. http://dx.doi.org/10.1016/j.acra.2007.09.027.

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Lee, Jeongmin, Sanghee Kim, Bong Joo Kang, Sung Hun Kim, and Ga Eun Park. "Evaluation of the effect of computer aided diagnosis system on breast ultrasound for inexperienced radiologists in describing and determining breast lesions." Medical Ultrasonography 21, no. 3 (August 31, 2019): 239. http://dx.doi.org/10.11152/mu-1889.

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Aim: To investigate the effect of a computer-aided diagnosis (CAD) system on breast ultrasound (US) for inexperienced radiologists in describing and determining breast lesions.Materials and methods: Between October 2015 to January 2017, 500 suspicious or probable benign lesions in 413 patients were reviewed. Five experienced readers retrospectively reviewed for each of 100 lesions according to the Breast Imaging Reporting and Data System (BI-RADS) lexicon and category, with CAD system (S-detectTM). The readers then made final decisions by combining CAD results to their US results. Using the nested experiment design, five inexperienced readers were asked to select the appropriate BI-RADS lexicons, categories, CAD results, and combination results for each of the 100 lesions, retrospectively. Diagnostic performance of experienced and inexperienced radiologists and CAD were assessed. For each case, agreements in the lexicons and categories were analyzed among the experienced reader, inexperienced reader and CAD.Results: Indicators of the diagnostic performance for breast malignancy of the experienced group (AUC=0.83, 95%CI [0.80, 0.86]) were similar or higher than those of CAD (AUC = 0.79, 95%CI[0.74, 0.83], p=0.101), except for specificity. Conversely, indicators of diagnostic performance of inexperienced group (AUC=0.65, 95%CI[0.58, 0.71]) did not differ from or were lower than those of CAD(AUC=0.73, 95%CI[0.67, 0.78], p=0.013). Also, the diagnostic performance of the inexperienced group after combination with the CAD result was significantly improved (0.71, 95% CI [0.65, 0.77], p=0.001), whereas that of the experienced group did not change after combination with the CAD result, except for specificity and positive predictive value (PPV). Kappa values for the agreement of the categorization between CAD and each radiologist group were increased after applying the CAD result to their result of general US. Especially, the increase of the Kappa value was higher in the inexperienced group than in the experienced group. Also, for all the lexicons, the Kappa values between the experienced group and CAD were higher than those between the inexperienced group and CAD.Conclusion: By using the CAD system for classification of breast lesions, diagnostic performance of the inexperienced radiologists for malignancy was significantly improved, and better agreement was observed in lexicons between the experienced group and CAD than between the inexperienced group and CAD. CAD may be beneficial and educational for the inexperienced group.

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Chen, Meng, Ximing Wang, Guangyu Hao, Xujie Cheng, Chune Ma, Ning Guo, Su Hu, Qing Tao, Feirong Yao, and Chunhong Hu. "Diagnostic performance of deep learning-based vascular extraction and stenosis detection technique for coronary artery disease." British Journal of Radiology 93, no. 1113 (September 1, 2020): 20191028. http://dx.doi.org/10.1259/bjr.20191028.

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Objective: To investigate the diagnostic performance of deep learning (DL)-based vascular extraction and stenosis detection technology in assessing coronary artery disease (CAD). Methods: The diagnostic performance of DL technology was evaluated by retrospective analysis of coronary computed tomography angiography in 124 suspected CAD patients, using invasive coronary angiography as reference standard. Lumen diameter stenosis ≥50% was considered obstructive, and the diagnostic performances were evaluated at per-patient, per-vessel and per-segment levels. The diagnostic performances between DL model and reader model were compared by the areas under the receiver operating characteristics curves (AUCs). Results: In patient-based analysis, AUC of 0.78 was obtained by DL model to detect obstructive CAD [sensitivity of 94%, specificity of 63%, positive predictive value of 94%, and negative predictive value of 59%], While AUC by reader model was 0.74 (sensitivity of 97%, specificity of 50%, positive predictive value of 93%, negative predictive value of 73%). In vessel-based analysis, the AUCs of DL model and reader model were 0.87 and 0.89 respectively. In segment-based analysis, the AUCs of 0.84 and 0.89 were obtained by DL model and reader model respectively. It took 0.47 min to analyze all segments per patient by DL model, which is significantly less than reader model (29.65 min) (p < 0.001). Conclusion: The DL technology can accurately and effectively identify obstructive CAD, with less time-consuming, and it could be a reliable diagnostic tool to detect CAD. Advances in knowledge: The DL technology has valuable prospect with the diagnostic ability to detect CAD.

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Sohns, Christian, Besim Cetin Angic, Samuel Sossalla, Frank Konietschke, and Silvia Obenauer. "CAD in full-field digital mammography—influence of reader experience and application of CAD on interpretation of time." Clinical Imaging 34, no. 6 (November 2010): 418–24. http://dx.doi.org/10.1016/j.clinimag.2009.10.039.

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Wu, Ming-Hsun, Kuen-Yuan Chen, Shyang-Rong Shih, Ming-Chih Ho, Hao-Chih Tai, King-Jen Chang, Argon Chen, and Chiung-Nien Chen. "Multi-Reader Multi-Case Study for Performance Evaluation of High-Risk Thyroid Ultrasound with Computer-Aided Detection." Cancers 12, no. 2 (February 6, 2020): 373. http://dx.doi.org/10.3390/cancers12020373.

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Physicians use sonographic characteristics as a reference for the possible diagnosis of thyroid cancers. The purpose of this study was to investigate whether physicians were more effective in their tentative diagnosis based on the information provided by a computer-aided detection (CAD) system. A computer compared software-defined and physician-adjusted tumor loci. A multicenter, multireader, and multicase (MRMC) study was designed to compare clinician performance without and with the use of CAD. Interobserver variability was also analyzed. Excellent, satisfactory, and poor segmentations were observed in 25.3%, 58.9%, and 15.8% of nodules, respectively. There were 200 patients with 265 nodules in the study set. Nineteen physicians scored the malignancy potential of the nodules. The average area under the curve (AUC) of all readers was 0.728 without CAD and significantly increased to 0.792 with CAD. The average standard deviation of the malignant potential score significantly decreased from 18.97 to 16.29. The mean malignant potential score significantly decreased from 35.01 to 31.24 for benign cases. With the CAD system, an additional 7.6% of malignant nodules would be suggested for further evaluation, and biopsy would not be recommended for an additional 10.8% of benign nodules. The results demonstrated that applying a CAD system would improve clinicians’ interpretations and lessen the variability in diagnosis. However, more studies are needed to explore the use of the CAD system in an actual ultrasound diagnostic situation where much more benign thyroid nodules would be seen.

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Bornefalk, Hans. "Implications of unchanged detection criteria with CAD as second reader of mammograms." Medical Physics 33, no. 4 (March 16, 2006): 922–29. http://dx.doi.org/10.1118/1.2179148.

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Benammar Elgaaied, Amel, Donato Cascio, Salvatore Bruno, Maria Cristina Ciaccio, Marco Cipolla, Alessandro Fauci, Rossella Morgante, et al. "Computer-Assisted Classification Patterns in Autoimmune Diagnostics: The AIDA Project." BioMed Research International 2016 (2016): 1–9. http://dx.doi.org/10.1155/2016/2073076.

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Antinuclear antibodies (ANAs) are significant biomarkers in the diagnosis of autoimmune diseases in humans, done by mean of Indirect ImmunoFluorescence (IIF) method, and performed by analyzing patterns and fluorescence intensity. This paper introduces the AIDA Project (autoimmunity: diagnosis assisted by computer) developed in the framework of an Italy-Tunisia cross-border cooperation and its preliminary results. A database of interpreted IIF images is being collected through the exchange of images and double reporting and a Gold Standard database, containing around 1000 double reported images, has been settled. The Gold Standard database is used for optimization of a CAD (Computer Aided Detection) solution and for the assessment of its added value, in order to be applied along with an Immunologist as a second Reader in detection of autoantibodies. This CAD system is able to identify on IIF images the fluorescence intensity and the fluorescence pattern. Preliminary results show that CAD, used as second Reader, appeared to perform better than Junior Immunologists and hence may significantly improve their efficacy; compared with two Junior Immunologists, the CAD system showed higher Intensity Accuracy (85,5% versus 66,0% and 66,0%), higher Patterns Accuracy (79,3% versus 48,0% and 66,2%), and higher Mean Class Accuracy (79,4% versus 56,7% and 64.2%).

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Mary M, Anitha, Bharathy R, Jhanani Shree U, and Richard Jebasingh S. "Database Management using Embedded System and CAD." International Journal of Engineering & Technology 7, no. 3.1 (August 4, 2018): 191. http://dx.doi.org/10.14419/ijet.v7i3.1.17339.

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An aim of this paper is to use Radio Frequency Identification (RFID) technology in facilitating efficient file management system. RFID is an abbreviation of radio frequency identification, RFID was poised to take off and become a standard piece of library technology. An RFID transmitter is also needed to capture the radio frequency signal from the tag for turn out to be giving out by the RFID reader. Therefore RFID has been referred to as “a non-contact method of using radio frequency electromagnetic waves for communication between two remote entities”. An Objective of this paper is to design 3D modeling of file rack using Creo software, manage the files using RFID tag, read the files from RFID using microcontroller through embedded code and to display the files using 16x2 Liquid Crystal Display and 4x4 keypad. The position of the files in the rack will be identified using Light Emitting Diode.

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Fleury, Eduardo F. C., and Karem Marcomini. "Breast elastography: diagnostic performance of computer-aided diagnosis software and interobserver agreement." Radiologia Brasileira 53, no. 1 (February 2020): 27–33. http://dx.doi.org/10.1590/0100-3984.2019.0035.

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Abstract Objective: To determine the best cutoff value for classifying breast masses by ultrasound elastography, using dedicated software for strain elastography, and to determine the level of interobserver agreement. Materials and Methods: We enrolled 83 patients with 83 breast masses identified on ultrasound and referred for biopsy. After B-mode ultrasound examination, the lesions were manually segmented by three radiologists with varying degrees of experience in breast imaging, designated reader 1 (R1, with 15 years), reader 2 (R2, with 2 years), and reader 3 (R3, with 8 years). Elastography was performed automatically on the best image with computer-aided diagnosis (CAD) software. Cutoff values of 70%, 75%, 80%, and 90% of hard areas were applied for determining the performance of the CAD software. The best cutoff value for the most experienced radiologists was then compared with the visual assessment. Interobserver agreement for the best cutoff value was determined, as were the interclass correlation coefficient and concordance among the radiologists for the areas segmented. Results: The best cutoff value of the proportion of hard area within a breast mass, for experienced radiologists, was found to be 75%. At a cutoff value of 75%, the interobserver agreement was excellent between R1 and R2, as well as between R1 and R3, and good between R2 and R3. The interclass concordance coefficient among the three radiologists was 0.950. When assessing the segmented areas by size, we found that the level of agreement was higher among the more experienced radiologists. Conclusion: The best cutoff value for a quantitative CAD system to classify breast masses was 75%.

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Ciatto, S., D. Ambrogetti, G. Collini, A. Cruciani, E. Ercolini, G. Risso, and M. Rosselli Del Turco. "Computer-aided detection (CAD) of cancers detected on double reading by one reader only." Breast 15, no. 4 (August 2006): 528–32. http://dx.doi.org/10.1016/j.breast.2005.08.035.

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Kuhl, Christiane K., Alexandra Barabasch, Tim Dirrichs, Philipp Bruners, Lieven Kennes, Tim Bruemmendorf, and Sebastian Keil. "Target lesion selection as a source of variability of response classification by RECIST 1.1." Journal of Clinical Oncology 31, no. 15_suppl (May 20, 2013): 11077. http://dx.doi.org/10.1200/jco.2013.31.15_suppl.11077.

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11077 Background: Response classification in RECIST is based on manual uni-dimensional quantification of changes of target lesion size. In RECIST 1.1, the maximum number of target lesions was reduced to 5, with a maximum of 2 per organ. We analyzed the importance of different factors (manual vs. automated size measurement, uni- vs. three-dimensional size assessment, and between-reader-variability of target lesion selection) on response categorization. Methods: 41 female patients (58.1±13.2 y) with metastatic breast cancer underwent contrast-enhanced thoraco-abdominal CT for initial staging and first follow-up after systemic chemotherapy. Data were independently and prospectively interpreted by three radiologists. In addition, response was evaluated by a CAD system that allowed automated uni- and three-dimensional assessment of target lesions. Results: Response classification differed between readers in 19/41 patients (46%). In 25/41 patients, readers chose the same target lesions. In 6 of these 25 patients (24%), readers disagreed with regards to response classification. In 16/41 patients, readers chose different target lesions. In 13 of these 16 patients, readers disagreed (81%) (p < 0.001). When dichotomizing response classification according to its therapeutic implication into progressive vs. non-progressive disease, readers disagreed in 11/41 patients (27%). In 9 of these 11 patients, readers had chosen different target lesions. Classification by manual vs. automated uni-dimensional measurement differed in 11/41 of patients (27%). Classification by uni-dimensional vs. volumetric measurements differed in 6/41 of patients (15%). Conclusions: Response classification by RECIST suffers from substantial between-reader variability. Major source of variability is not the manual or uni-dimensional measurement, but the variable choice of target lesions between readers. [Table: see text]

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Fisichella, V. A., F. Jäderling, S. Horvath, P. O. Stotzer, A. Kilander, M. Båth, and M. Hellström. "Computer-aided detection (CAD) as a second reader using perspective filet view at CT colonography: effect on performance of inexperienced readers." Clinical Radiology 64, no. 10 (October 2009): 972–82. http://dx.doi.org/10.1016/j.crad.2009.05.012.

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Leenhardt, Romain, Cynthia Li, Jean-Philippe Le Mouel, Gabriel Rahmi, Jean Christophe Saurin, Franck Cholet, Arnaud Boureille, et al. "CAD-CAP: a 25,000-image database serving the development of artificial intelligence for capsule endoscopy." Endoscopy International Open 08, no. 03 (February 21, 2020): E415—E420. http://dx.doi.org/10.1055/a-1035-9088.

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Abstract Background and study aims Capsule endoscopy (CE) is the preferred method for small bowel (SB) exploration. With a mean number of 50,000 SB frames per video, SBCE reading is time-consuming and tedious (30 to 60 minutes per video). We describe a large, multicenter database named CAD-CAP (Computer-Assisted Diagnosis for CAPsule Endoscopy, CAD-CAP). This database aims to serve the development of CAD tools for CE reading. Materials and methods Twelve French endoscopy centers were involved. All available third-generation SB-CE videos (Pillcam, Medtronic) were retrospectively selected from these centers and deidentified. Any pathological frame was extracted and included in the database. Manual segmentation of findings within these frames was performed by two pre-med students trained and supervised by an expert reader. All frames were then classified by type and clinical relevance by a panel of three expert readers. An automated extraction process was also developed to create a dataset of normal, proofread, control images from normal, complete, SB-CE videos. Results Four-thousand-one-hundred-and-seventy-four SB-CE were included. Of them, 1,480 videos (35 %) containing at least one pathological finding were selected. Findings from 5,184 frames (with their short video sequences) were extracted and delimited: 718 frames with fresh blood, 3,097 frames with vascular lesions, and 1,369 frames with inflammatory and ulcerative lesions. Twenty-thousand normal frames were extracted from 206 SB-CE normal videos. CAD-CAP has already been used for development of automated tools for angiectasia detection and also for two international challenges on medical computerized analysis.

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Neri, Emanuele, Lorenzo Faggioni, Daniele Regge, Paola Vagli, Francesca Turini, Francesca Cerri, Eugenia Picano, Sabina Giusti, and Carlo Bartolozzi. "CT Colonography: Role of a second reader CAD paradigm in the initial training of radiologists." European Journal of Radiology 80, no. 2 (November 2011): 303–9. http://dx.doi.org/10.1016/j.ejrad.2010.07.014.

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Meybaum, Christian, Mareike Graff, Eva Maria Fallenberg, Gunda Leschber, and Dag Wormanns. "Contribution of CAD to the Sensitivity for Detecting Lung Metastases on Thin-Section CT – A Prospective Study with Surgical and Histopathological Correlation." RöFo - Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren 192, no. 01 (August 1, 2019): 65–73. http://dx.doi.org/10.1055/a-0977-3453.

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Purpose To assess the sensitivity of radiologists and a CAD system for the detection of lung metastases on thin-section computed tomographic (CT) scans prior to pulmonary metastasectomy. Materials and Methods All patients scheduled for resection of lung metastases were eligible for this prospective single-center trial. 95 patients with 115 surgical procedures (pulmonary metastasectomy using thoracotomy) were included. An experienced radiologist examined the CT scans for pulmonary metastases and documented his findings. A commercial CAD system was used as a second reader; additional CAD findings were recorded. A comparison of the sensitivity of the radiologist alone and with CAD was performed. Intraoperatively surgeons tried to identify the documented lesions and resected them as well as additionally palpable lesions. The standard of reference consisted of surgery and histopathology. Follow-up information for radiologically detected lesions missed during surgery was sought. Results 693 lesions (262 metastases) were detected radiologically or surgically, 646 of them were resected. The sensitivity of radiologists without CAD was 67.5 % for all lesions (87.4 % for metastases). CAD highly significantly (p < 0.001) increased the sensitivity to 77.9 % (92.7 %). During surgery, 143 additional lesions (19 metastases) were detected. 49 radiologically detected lesions were not palpable during surgery: 4 metastases, 5 benign lesions, and 40 lesions of an unknown nature. Conclusion CAD provides significant additional sensitivity for detecting lung metastases using MDCT compared to the performance of a radiologist alone. CT reveals a relevant number of non-palpable pulmonary lesions. Key Points: Citation Format

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Hwang, Eui Jin, Ki Beom Kim, Jin Young Kim, Jae-Kwang Lim, Ju Gang Nam, Hyewon Choi, Hyungjin Kim, Soon Ho Yoon, Jin Mo Goo, and Chang Min Park. "COVID-19 pneumonia on chest X-rays: Performance of a deep learning-based computer-aided detection system." PLOS ONE 16, no. 6 (June 7, 2021): e0252440. http://dx.doi.org/10.1371/journal.pone.0252440.

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Chest X-rays (CXRs) can help triage for Coronavirus disease (COVID-19) patients in resource-constrained environments, and a computer-aided detection system (CAD) that can identify pneumonia on CXR may help the triage of patients in those environment where expert radiologists are not available. However, the performance of existing CAD for identifying COVID-19 and associated pneumonia on CXRs has been scarcely investigated. In this study, CXRs of patients with and without COVID-19 confirmed by reverse transcriptase polymerase chain reaction (RT-PCR) were retrospectively collected from four and one institution, respectively, and a commercialized, regulatory-approved CAD that can identify various abnormalities including pneumonia was used to analyze each CXR. Performance of the CAD was evaluated using area under the receiver operating characteristic curves (AUCs), with reference standards of the RT-PCR results and the presence of findings of pneumonia on chest CTs obtained within 24 hours from the CXR. For comparison, 5 thoracic radiologists and 5 non-radiologist physicians independently interpreted the CXRs. Afterward, they re-interpreted the CXRs with corresponding CAD results. The performance of CAD (AUCs, 0.714 and 0.790 against RT-PCR and chest CT, respectively hereinafter) were similar with those of thoracic radiologists (AUCs, 0.701 and 0.784), and higher than those of non-radiologist physicians (AUCs, 0.584 and 0.650). Non-radiologist physicians showed significantly improved performance when assisted with the CAD (AUCs, 0.584 to 0.664 and 0.650 to 0.738). In addition, inter-reader agreement among physicians was also improved in the CAD-assisted interpretation (Fleiss’ kappa coefficient, 0.209 to 0.322). In conclusion, radiologist-level performance of the CAD in identifying COVID-19 and associated pneumonia on CXR and enhanced performance of non-radiologist physicians with the CAD assistance suggest that the CAD can support physicians in interpreting CXRs and helping image-based triage of COVID-19 patients in resource-constrained environment.

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Bao, Lingyun. "Reader Study of Sonologist' Performance in Reading Automated Breast Ultrasound (ABVS) Images with the CAD System." Ultrasound in Medicine & Biology 43 (2017): S13—S14. http://dx.doi.org/10.1016/j.ultrasmedbio.2017.08.981.

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Luu, Judy M., Anna Schmidt, Jacqueline Flewitt, Yoko Mikami, Henk ter Keurs, and Matthias G. Friedrich. "Cardiovascular risk is associated with a transmural gradient of myocardial oxygenation during adenosine infusion." European Heart Journal - Cardiovascular Imaging 20, no. 11 (December 24, 2018): 1287–95. http://dx.doi.org/10.1093/ehjci/jey202.

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Abstract Aims In patients with coronary artery disease (CAD), a transmural gradient of myocardial perfusion has been repeatedly observed, with the subendocardial layer showing more pronounced perfusion deficits. Oxygenation-sensitive cardiovascular magnetic resonance (OS-CMR) allows for monitoring transmural changes of myocardial oxygenation in vivo. We hypothesized that OS-CMR could help identify a transmural oxygenation gradient as a disease marker in patients at risk for CAD. Methods and results We assessed 34 patients with known CAD and 28 subjects with coronary risk factors but no evidence of significant CAD. Results were compared with 11 healthy volunteers. OS-CMR was performed at 1.5 T, applying a T2*-weighted cine steady state free precession sequence at baseline and during infusion of adenosine. A reader blinded to patient data quantified the relative change of myocardial oxygenation in OS-CMR, defined by the change of signal intensity (ΔSI%) between baseline and during adenosine infusion in the entire myocardium, the subepicardial layer, and the subendocardial layer. SI changes were homogenous throughout the myocardium in healthy subjects, whereas both, patients with risk factors only and patients with CAD, had a significantly smaller ΔSI% in the subendocardial layer than in the subendocardial layer. Both patient groups had an overall decreased ΔSI% across all layers when compared with healthy subjects (P < 0.05). Conclusion Even in the absence of overt CAD, cardiovascular risk factors are associated with a transmural gradient of the myocardial oxygenation response to adenosine as assessed by OS-CMR. An inducible transmural oxygenation gradient may serve as a non-invasive marker for cardiovascular risk.

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Yuan, R., J. Mayo, I. Streit, S. Atkar-Khattra, R. Myers, J. Yee, and S. Lam. "MA10.06 Randomized Clinical Trial with Computer Assisted Diagnosis (CAD) Versus Radiologist as First Reader of Lung Screening LDCT." Journal of Thoracic Oncology 14, no. 10 (October 2019): S287—S288. http://dx.doi.org/10.1016/j.jtho.2019.08.578.

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Balleyguier, Corinne, Julia Arfi-Rouche, Laurent Levy, Patrick R. Toubiana, Franck Cohen-Scali, Alicia Y. Toledano, and Bruno Boyer. "Improving digital breast tomosynthesis reading time: A pilot multi-reader, multi-case study using concurrent Computer-Aided Detection (CAD)." European Journal of Radiology 97 (December 2017): 83–89. http://dx.doi.org/10.1016/j.ejrad.2017.10.014.

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Vimaleswaran, Karani S. "Comment: “Evaluation of the Association of Omentin 1 rs2274907 A>T and rs2274908 G>A Gene Polymorphisms with Coronary Artery Disease in Indian Population: A Case Control Study”." Journal of Personalized Medicine 10, no. 4 (October 25, 2020): 190. http://dx.doi.org/10.3390/jpm10040190.

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The study by Jha et al. (2019) demonstrated an association of the single nucleotide polymorphism (SNP) rs2274907 A>T with coronary artery disease (CAD) in 100 CAD patients and 100 matched healthy controls from a South Indian population. There are serious concerns with regard to the interpretations of the study findings. The genotypes of the SNP are not in Hardy–Weinberg equilibrium (HWE) in both cases (p < 0.0001) and controls (p = 0.006), which is indicative of a technical error due to a problematic genotyping method. In addition, the genotype and allele frequencies reported in the study do not match with the frequencies listed in the SNP database for Asian Indians. While the study by Jha et al. reported ”T” allele as the minor allele, the dbSNP database reported ”A” as the minor allele. In summary, it can be concluded that the data presented in the study suffer from genotyping as well as data interpretation error and, hence, the findings should be considered by the reader with caution.

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Beyer, F., L. Zierott, E. M. Fallenberg, K. U. Juergens, J. Stoeckel, W. Heindel, and D. Wormanns. "Comparison of sensitivity and reading time for the use of computer-aided detection (CAD) of pulmonary nodules at MDCT as concurrent or second reader." European Radiology 17, no. 11 (May 22, 2007): 2941–47. http://dx.doi.org/10.1007/s00330-007-0667-1.

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Ha, T., Y. Jung, J. Y. Kim, S. Y. Park, D. K. Kang, and T. H. Kim. "Comparison of the diagnostic performance of abbreviated MRI and full diagnostic MRI using a computer-aided diagnosis (CAD) system in patients with a personal history of breast cancer: the effect of CAD-generated kinetic features on reader performance." Clinical Radiology 74, no. 10 (October 2019): 817.e15–817.e21. http://dx.doi.org/10.1016/j.crad.2019.06.025.

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Chen, Zhennong, Marzia Rigolli, Davis Marc Vigneault, Seth Kligerman, Lewis Hahn, Anna Narezkina, Amanda Craine, Katherine Lowe, and Francisco Contijoch. "Automated cardiac volume assessment and cardiac long- and short-axis imaging plane prediction from electrocardiogram-gated computed tomography volumes enabled by deep learning." European Heart Journal - Digital Health 2, no. 2 (March 22, 2021): 311–22. http://dx.doi.org/10.1093/ehjdh/ztab033.

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Abstract Aims To develop an automated method for bloodpool segmentation and imaging plane re-slicing of cardiac computed tomography (CT) via deep learning (DL) for clinical use in coronary artery disease (CAD) wall motion assessment and reproducible longitudinal imaging. Methods and results One hundred patients who underwent clinically indicated cardiac CT scans with manually segmented left ventricle (LV) and left atrial (LA) chambers were used for training. For each patient, long-axis (LAX) and short-axis planes were manually defined by an imaging expert. A DL model was trained to predict bloodpool segmentations and imaging planes. Deep learning bloodpool segmentations showed close agreement with manual LV [median Dice: 0.91, Hausdorff distance (HD): 6.18 mm] and LA (Dice: 0.93, HD: 7.35 mm) segmentations and a strong correlation with manual ejection fraction (Pearson r: 0.95 LV, 0.92 LA). Predicted planes had low median location (6.96 mm) and angular orientation (7.96°) errors which were comparable to inter-reader differences (P > 0.71). 84–97% of DL-prescribed LAX planes correctly intersected American Heart Association segments, which was comparable (P > 0.05) to manual slicing. In a test cohort of 144 patients, we evaluated the ability of the DL approach to provide diagnostic imaging planes. Visual scoring by two blinded experts determined ≥94% of DL-predicted planes to be diagnostically adequate. Further, DL-enabled visualization of LV wall motion abnormalities due to CAD and provided reproducible planes upon repeat imaging. Conclusion A volumetric, DL approach provides multiple chamber segmentations and can re-slice the imaging volume along standardized cardiac imaging planes for reproducible wall motion abnormality and functional assessment.

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Mahler, Beatrice, Gerard de Vries, Rob van Hest, Dan Gainaru, Dee Menezes, Gilda Popescu, Alistair Story, and Ibrahim Abubakar. "Use of targeted mobile X-ray screening and computer-aided detection software to identify tuberculosis among high-risk groups in Romania: descriptive results of the E-DETECT TB active case-finding project." BMJ Open 11, no. 8 (August 2021): e045289. http://dx.doi.org/10.1136/bmjopen-2020-045289.

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ObjectiveTo implement and assess the mobile X-ray unit (MXU) equipped with digital radiography, computer-aided detection (CAD) software and molecular point of care tests to improve early tuberculosis (TB) diagnosis in vulnerable populations in a TB outreach screening programme in Romania.DesignDescriptive study.SettingsPrisons in Bucharest and other cities in the southern part of Romania, homeless shelters and services for problem drug users in Bucharest, and Roma populations in Bucharest and Craiova.Participants5510 individuals attended the MXU service; 5003 persons were radiologically screened, 61% prisoners, 15% prison staff, 11% Roma population, 10% homeless persons and/or problem drug users and 3% other.InterventionsRadiological digital chest X-ray (CXR) screening of people at risk for TB, followed by CAD and human reading of the CXRs, and further TB diagnostics when the pulmonologist classified the CXR as suggestive for TB.Primary and secondary outcome measuresTen bacteriologically confirmed TB cases were identified translating into an overall yield of 200 per 100 000 persons screened (95% CIs of 109 to 368 per 100 000). Prevalence rates among homeless persons and/or problem drug users (826/100 000; 95% CI 326 to 2105/100 000) and the Roma population (345/100 000; 95% CI 95 to 1251/100 000) were particularly high.ResultsThe human reader classified 6.4% (n=317) of the CXRs as suspect for TB (of which 32 were highly suggestive for TB); 16.3% of all CXRs had a CAD4TB version 6 score >50. All 10 diagnosed TB patients had a CAD4TB score >50; 9 had a CAD4TB score >60.ConclusionsGiven the high TB prevalence rates found among homeless persons and problem drug users and in the Roma population, targeted active case finding has the potential to deliver a major contribution to TB control in Romania.

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Rojas-Sola, José Ignacio, Gloria del Río-Cidoncha, Arturo Fernández-de la Puente Sarriá, and Verónica Galiano-Delgado. "Blaise Pascal’s Mechanical Calculator: Geometric Modelling and Virtual Reconstruction." Machines 9, no. 7 (July 16, 2021): 136. http://dx.doi.org/10.3390/machines9070136.

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This article shows the three-dimensional (3D) modelling and virtual reconstruction of the first mechanical calculating machine used for accounting purposes designed by Blaise Pascal in 1642. To obtain the 3D CAD (computer-aided design) model and the geometric documentation of said invention, CATIA V5 R20 software has been used. The starting materials for this research, mainly the plans of this arithmetic machine, are collected in the volumes Oeuvres de Blaise Pascal published in 1779. Sketches of said machine are found therein that lack scale, are not dimensioned and certain details are absent; that is, they were not drawn with precision in terms of their measurements and proportions, but they do provide qualitative information on the shape and mechanism of the machine. Thanks to the three-dimensional modelling carried out; it has been possible to explain in detail both its operation and the final assembly of the invention, made from the assemblies of its different subsets. In this way, the reader of the manuscript is brought closer to the perfect understanding of the workings of a machine that constituted a major milestone in the technological development of the time.

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Tokunaga, Shuntaro, Nobuko Hazeki, Daisuke Tamura, Tatsuya Nagano, Kazuyuki Kobayashi, Sumiaki Matsumoto, Takatoshi Aoki, et al. "Computer-Aided Detection (CAD) as Concurrent vs. Second Reader for Lung Nodules on CT in a Japanese Multicenter Study: Evaluation of Reading Time and Observer Performance in Radiologists and Pulmonologists." Chest 144, no. 4 (October 2013): 589A. http://dx.doi.org/10.1378/chest.1703410.

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Popova, O., N. Silvestrova, and V. Koshel. "MODERN SOFTWARE FOR COMPUTER MODELING IN ARCHITECTURAL EDUCATION." Municipal economy of cities 1, no. 161 (March 26, 2021): 82–87. http://dx.doi.org/10.33042/2522-1809-2021-1-161-82-87.

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The article considers the most modern software that can be used in the development of various architectural and design projects of interior and exterior spaces, as well as highlights the features and benefits of using each of them in projects within the disciplines of specialty 191 Architecture and Urban Planning. The main problem of modern architectural education is that with evolution of computer technologies, began the active development, implementation and modernization of various software tools for various types of design, 3-D modeling, sketching, etc. This area has developed very rapidly and almost every year new versions of programs are developed, where new features appear that increase the quality, efficiency, convenience and speed of work with these programs. Due to such a rapid software update, the previously developed curricula for the specialty 191 Architecture and Urban Planning need to be updated. Today in the world there is a situation that the greater the number of programs owned by the employee and the higher the skill of working with them, the greater the value and need for staff in the global labor market. Therefore, modern educational programs need to be modernized by introducing practical work with new software, which was discussed in the article. Namely, the Magic Plan, Ruler App and Photo Ruler programs that will be useful for measuring the designed premises. Home Design 3D and Homestyler interior design programs will come in handy for developing and approving a general interior design solution with a customer. To develop the working documentation, the CAD programs Autodesk Revit, Autodesk Autocad, Graphisoft Archicad and the BIMx Viever application were considered. BIMx Viever may be needed for demonstrating the work done to the customer or additional consultations with him. The article also discussed 3-D modeling programs Sketchup, Sculptris, Autodesk 3ds Max, Grasshopper and visualization programs Corona Render, V-Ray, Lumion and Abvent Artlantis. Additionally, the programs that may be needed for designing a project album were considered, they include CAD programs, programs for working with PDF files such as Adobe Acrobat Reader, and graphic editors Adobe Photoshop and its analogues.

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Bogoni, Luca, Jane P. Ko, Jeffrey Alpert, Vikram Anand, John Fantauzzi, Charles H. Florin, Chi Wan Koo, et al. "Impact of a Computer-Aided Detection (CAD) System Integrated into a Picture Archiving and Communication System (PACS) on Reader Sensitivity and Efficiency for the Detection of Lung Nodules in Thoracic CT Exams." Journal of Digital Imaging 25, no. 6 (June 19, 2012): 771–81. http://dx.doi.org/10.1007/s10278-012-9496-0.

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McKeown, Mick, and Helen Spandler. "Mad Matters: A Critical Reader in Canadian Mad Studies edited by BrendaLe Francois, RobertMenzies and GeoffreyReaume (eds). Canadian Scholars Press, Toronto, 2013. 380 pp. ISBN 978-1-55130-534-9. Price CAD$49.95." International Journal of Mental Health Nursing 22, no. 5 (August 19, 2013): 472. http://dx.doi.org/10.1111/inm.12038.

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Giangiulio Lobo, Alejandra. "Reader-Response Theory: A Path Towards Wolfgang Iser." LETRAS, no. 54 (July 10, 2013): 13–30. http://dx.doi.org/10.15359/rl.2-54.1.

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Se estudia la teoría de la recepción a partir de diferentes autores y críticos literarios, para caracterizar los distintos tipos de lectores, según cada aproximación y los procesos de lectura y creación de significado. El ensayo se centra en el enfoque fenomenológico de Wolfgang Iser sobre la recepción del lector, la generación de significado y los tipos de lectores. Reader-response theory is studied from the perspective of different authors and literary critics to characterize the different types of readers, according to each approach, the reading process and the creation of meaning. The essay centers on Wolfgang Iser’s phenomenological approach to reader response, creation of meaning and types of readers.

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Gobetti, Eusdete de Jesus Trabach, Juliana Scarpat Cavalcante, Sylvia Helena Lessa Dias, and Thaynan Gonçalves Alcântara de Oliveira. "Roda de leitura na escola: um projeto de formação de leitores." Revista Científica Faesa 17, no. 1 (June 15, 2021): 158–75. http://dx.doi.org/10.5008/1809.7367.192.

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Este artigo refere-se a uma ação do projeto Roda de Leitura, desenvolvido na FAESA – Centro Universitário, realizado entre 2013 e 2017. Trata-se de aplicação do projeto por graduandas do curso de Pedagogia em uma escola da Rede Municipal de Vitória, no período de março a novembro de 2017, com o objetivo de vivenciar rodas de leitura com alunos do Ensino Fundamental, implementando dinâmicas e estratégias que propiciassem o desenvolvimento da competência leitora e o prazer de ler. Foi verificado que os alunos modificaram o comportamento leitor e apresentaram melhor desempenho em suas leituras e produções escritas. Esses aspectos puderam ser evidenciados por meio de depoimentos das professoras, observações e análises das atividades e dos registros realizados a cada roda de leitura.

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Compton, Donald L., Laura M. Steacy, Yaacov Petscher, Jay G. Rueckl, Nicole Landi, and Ken R. Pugh. "Linking Behavioral and Computational Approaches to Better Understand Variant Vowel Pronunciations in Developing Readers." New Directions for Child and Adolescent Development 2019, no. 165 (May 2019): 55–71. http://dx.doi.org/10.1002/cad.20294.

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Mackie, Cynthia. "Readers' Column." Culture Agriculture -, no. 31 (December 1987): 7–11. http://dx.doi.org/10.1525/cag.1987.-.31.7.

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Lisansky, Judith. "Readers Column." Culture Agriculture -, no. 32 (March 1987): 6–14. http://dx.doi.org/10.1525/cag.1987.-.32.6.

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Obuchowski, Nancy A. "Predicting Readers' Diagnostic Accuracy with a New CAD Algorithm." Academic Radiology 18, no. 11 (November 2011): 1412–19. http://dx.doi.org/10.1016/j.acra.2011.07.007.

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Giannini, Valentina, Simone Mazzetti, Giovanni Cappello, Valeria Maria Doronzio, Lorenzo Vassallo, Filippo Russo, Alessandro Giacobbe, Giovanni Muto, and Daniele Regge. "Computer-Aided Diagnosis Improves the Detection of Clinically Significant Prostate Cancer on Multiparametric-MRI: A Multi-Observer Performance Study Involving Inexperienced Readers." Diagnostics 11, no. 6 (May 28, 2021): 973. http://dx.doi.org/10.3390/diagnostics11060973.

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Recently, Computer Aided Diagnosis (CAD) systems have been proposed to help radiologists in detecting and characterizing Prostate Cancer (PCa). However, few studies evaluated the performances of these systems in a clinical setting, especially when used by non-experienced readers. The main aim of this study is to assess the diagnostic performance of non-experienced readers when reporting assisted by the likelihood map generated by a CAD system, and to compare the results with the unassisted interpretation. Three resident radiologists were asked to review multiparametric-MRI of patients with and without PCa, both unassisted and assisted by a CAD system. In both reading sessions, residents recorded all positive cases, and sensitivity, specificity, negative and positive predictive values were computed and compared. The dataset comprised 90 patients (45 with at least one clinically significant biopsy-confirmed PCa). Sensitivity significantly increased in the CAD assisted mode for patients with at least one clinically significant lesion (GS > 6) (68.7% vs. 78.1%, p = 0.018). Overall specificity was not statistically different between unassisted and assisted sessions (94.8% vs. 89.6, p = 0.072). The use of the CAD system significantly increases the per-patient sensitivity of inexperienced readers in the detection of clinically significant PCa, without negatively affecting specificity, while significantly reducing overall reporting time.

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RC, Eze, and Agena James E. "Independent National Electoral Commission (INEC) Electronic Card Readers (ECR) and the 2015 General Elections in Nigeria." Scholars Journal of Arts, Humanities and Social Sciences 4, no. 7 (July 2016): 796–805. http://dx.doi.org/10.21276/sjahss.2016.4.7.8.

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Hunter, Robert. "Readers Column; Response to the critiques on "Disappearing Jungles"." Culture Agriculture -, no. 33 (September 1987): 4–5. http://dx.doi.org/10.1525/cag.1987.-.33.4.

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Edney, Matthew H. "Plus ça change: Defining Academic Cartography for the Twenty-First CenturyTHE MAP READER: THEORIES OF MAPPING PRACTICE AND CARTOGRAPHIC REPRESENTATION / ed. Dodge Martin Kitchin Rob Perkins Chris. Chichester, UK: Wiley-Blackwell, 2011. Pp. xxvi, 478; figs. (col.); index. ISBN-13 978-0-470-74283-9 (cloth), CAD$131.95, US$119.95, €96.00, £80.00. ISBN-13 978-0-470-98007-1 (ebook), CAD$131.95, US$119.95, €96.00, £80.00. See http://onlinelibrary.wiley.com/book/10.1002/9780470979587 for complete contents and ePub options." Cartographica: The International Journal for Geographic Information and Geovisualization 47, no. 1 (March 2012): 64–69. http://dx.doi.org/10.3138/carto.47.1.64.

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Livingstone, Jenny, and Julian F. Fleron. "Technology Tips: Exploring Three-Dimensional Worlds Using Google SketchUp." Mathematics Teacher 105, no. 6 (February 2012): 469–73. http://dx.doi.org/10.5951/mathteacher.105.6.0469.

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Google SketchUp is free, powerful and widely used Computer Aided Design (CAD) software that can have a transformative impact on the teaching of geometry. This article introduces Google SketchUp to readers through lessons that can be integrated into geometry classrooms and also provides additional resources for readers interested in learning more.

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Buttel, Frederick H. "Readers Column; Political Economy Of and In the Farm Crisis." Culture Agriculture -, no. 33 (September 1987): 5–9. http://dx.doi.org/10.1525/cag.1987.-.33.5.

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Taylor, P., R. Given-Wilson, J. Champness, H. W. W. Potts, and K. Johnston. "Assessing the impact of CAD on the sensitivity and specificity of film readers." Clinical Radiology 59, no. 12 (December 2004): 1099–105. http://dx.doi.org/10.1016/j.crad.2004.04.017.

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Wood, Chris. "Computer Aided Detection (CAD) for Breast MRI." Technology in Cancer Research & Treatment 4, no. 1 (February 2005): 49–53. http://dx.doi.org/10.1177/153303460500400107.

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Since 1999, there has been a 40 percent increase per year in the number of breast MR studies performed in the United States. In addition, over 1200 sites in the United States have purchased surface coils for use in breast MR. This number is expected to grow to over 2,000 coils by the end of 2007. It is well accepted that MR sensitivity for invasive breast cancers is near 100%, but as the use of breast MRI increases, radiologists interpreting breast MR are challenged to achieve high specificity while retaining high sensitivity. Reading the large number of acquired MR images in a reasonable amount of time also becomes more important as the number of studies increases. Breast MR acquisition and image interpretation techniques have been refined through clinical optimization. The number of images to interpret, however, has increased to several hundred per case. Computer Aided Detection (CAD) algorithms have allowed radiologists to regain efficiency while maintaining optimized acquisition techniques. The first CAD system for breast MR (CADstream by Confirma, Inc.) was launched in January 2003. The CAD installed base has since grown to over 150 systems in the US. The primary reason for this quick adoption of CAD for breast MR is that the CAD software enables readers to increase their efficiency while potentially improving their overall accuracy. The full benefits CAD for Breast MR are realized when the interpreting radiologist has a thorough understanding of the algorithms used, and the limitations of CAD.

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Pincock, A. J. L. "Safe passage to Europe—Eurostar's in-cab signalling systems." Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit 212, no. 3 (May 1, 1998): 235–51. http://dx.doi.org/10.1243/0954409981530832.

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This paper describes the in-cab signalling systems used on Eurostar trains. It categorizes them in terms of their function as basic signal repetition systems (AWS, BRS and TBL1), beacon-based speed supervision systems (KVB and TBL2) and continuous in-cab signalling systems (TVM) and reviews their relative merits. The aim of the paper is to enable the reader to recognize the three generic groups of in-cab signalling systems and understand the philosopy behind the systems within each group.

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