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Journal articles on the topic 'Diagnostic informatics'

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Author: Grafiati

Published: 10 December 2022

Last updated: 29 January 2023

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1

Olivo, G., A. Calisti, G. Carluccio, G. Poletti, and A. Lotto. "Informatics in urodynamics." Urologia Journal 61, no. 1 (February 1994): 55–59. http://dx.doi.org/10.1177/039156039406100112.

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Urodynamics is a diagnostic technique which makes full use of informatics. In the urodynamic laboratory the computer collects data both during clinical evaluation and during tests. Wiest KG software, which has been utilised in our laboratory for over five years, is illustrated. The advent of microprocessors has led to the development of miniaturised diagnostic systems in the biomedical field, which can study essential body functions over long periods of time: Rigiscan and vesicourethral Holter. We may assume that, with the development of so-called expert systems, the computer will take over the crucial urodynamic investigation which leads to diagnosis.

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Roosan, Don, Anandi V. Law, Mazharul Karim, and Moom Roosan. "Improving Team-Based Decision Making Using Data Analytics and Informatics: Protocol for a Collaborative Decision Support Design." JMIR Research Protocols 8, no. 11 (November 27, 2019): e16047. http://dx.doi.org/10.2196/16047.

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Background According to the September 2015 Institute of Medicine report, Improving Diagnosis in Health Care, each of us is likely to experience one diagnostic error in our lifetime, often with devastating consequences. Traditionally, diagnostic decision making has been the sole responsibility of an individual clinician. However, diagnosis involves an interaction among interprofessional team members with different training, skills, cultures, knowledge, and backgrounds. Moreover, diagnostic error is prevalent in the interruption-prone environment, such as the emergency department, where the loss of information may hinder a correct diagnosis. Objective The overall purpose of this protocol is to improve team-based diagnostic decision making by focusing on data analytics and informatics tools that improve collective information management. Methods To achieve this goal, we will identify the factors contributing to failures in team-based diagnostic decision making (aim 1), understand the barriers of using current health information technology tools for team collaboration (aim 2), and develop and evaluate a collaborative decision-making prototype that can improve team-based diagnostic decision making (aim 3). Results Between 2019 to 2020, we are collecting data for this study. The results are anticipated to be published between 2020 and 2021. Conclusions The results from this study can shed light on improving diagnostic decision making by incorporating diagnostics rationale from team members. We believe a positive direction to move forward in solving diagnostic errors is by incorporating all team members, and using informatics. International Registered Report Identifier (IRRID) DERR1-10.2196/16047

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3

Malik, Nafees N. "Integration of diagnostic and communication technologies." Journal of Telemedicine and Telecare 15, no. 7 (October 2009): 323–26. http://dx.doi.org/10.1258/jtt.2009.009001.

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Three key areas in diagnostics will drive the convergence of diagnostic and communication technologies: point-of-care testing, micro-electromechanical systems and biomarker discovery. In addition, the communications revolution means that increasing numbers of people will be able to send data from their home to their doctor using the Internet. Also, the widespread availability of broadband opens up the possibly of realtime videoconferencing with clinicians. It is already possible for patients at home to monitor simple variables, such as heart rate and blood pressure, and send their results using communication technologies to their doctors, who can promptly review the information to diagnose problems. As diagnostic and communication technologies converge, it will be feasible for patients to transmit more complex health-care data periodically to their doctor, who will be able to identify problems early on and thus modify disease management to prevent exacerbations of patients' medical conditions. This will allow improved patient care in a wide range of health-care situations, from acute medical conditions to chronic disease.

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Versluis, Anke, Kyma Schnoor, Niels H. Chavannes, and Esther PWA Talboom-Kamp. "Direct Access for Patients to Diagnostic Testing and Results Using eHealth: Systematic Review on eHealth and Diagnostics." Journal of Medical Internet Research 24, no. 1 (January 12, 2022): e29303. http://dx.doi.org/10.2196/29303.

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Background The number of people with chronic diseases and the subsequent pressure on health care is increasing. eHealth technology for diagnostic testing can contribute to more efficient health care and lower workload. Objective This systematic review examines the available methods for direct web-based access for patients to diagnostic testing and results in the absence of a health care professional in primary care. Methods We searched the PubMed, Embase, Web of Sciences, Cochrane Library, Emcare, and Academic Search Premier databases in August 2019 and updated in July 2021. The included studies focused on direct patient access to web-based triage leading to diagnostic testing, self-sampling or testing, or web-based communication of test results. A total of 45 studies were included. The quality was assessed using the Mixed Methods Appraisal Tool. Results Most studies had a quantitative descriptive design and discussed a combination of services. Diagnostic test services mainly focused on sexually transmitted infections. Overall, the use was high for web-based triage (3046/5000, >50%, who used a triage booked a test), for self-sampling or self-testing kits (83%), and the result service (85%). The acceptability of the test services was high, with 81% preferring home-based testing over clinic-based testing. There was a high rate of follow-up testing or treatment after a positive test (93%). Conclusions The results show that direct access to testing and result services had high use rates, was positively evaluated, and led to high rates of follow-up treatment. More research on cost-effectiveness is needed to determine the potential for other diseases. Direct access to diagnostic testing can lower the threshold for testing in users, potentially increase efficiency, and lower the workload in primary care.

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Chaikovsky, I. "Difficult diagnostic problem with a clinical informatics view." Klinical Informatics and Telemedicine 11, no. 12 (January 30, 2015): 106–12. http://dx.doi.org/10.31071/kit2015.12.16.

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Odufuwa, T. O. Bola, Lola Solebo, and Sancy Low. "Diagnostic decision support in ophthalmology." Journal of Telemedicine and Telecare 13, no. 1_suppl (July 2007): 44–46. http://dx.doi.org/10.1258/135763307781645121.

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Isabel is a Web-based, diagnostic decision support tool designed to provide a differential diagnosis of a patient's condition for interpretation by a qualified health-care professional. We investigated the accuracy of the Isabel system in ophthalmic primary care. A total of 100 case histories were prospectively collected from ophthalmic primary care clinic records. The patient demographics and clinical features of each case were then entered into the Isabel system, and the results generated by the decision support tool for each case were compared with the diagnosis reached by the ophthalmic team. Of the 100 cases in the dataset, there was no matching diagnosis in the first 2 pages of Isabel results in 40 cases. Of the 60 cases in which there was a matching diagnosis on the first 2 pages of results, 31 had a >50% match between the terms of the query and the Isabel diagnosis reminder system's database. It remains to be established whether this is high enough to be clinically useful in a practice setting. Inclusion of specific ophthalmic knowledge would probably improve the accuracy of the Isabel clinical diagnostic decision support system.

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7

Hay, W. H., A. S. Elstein, C. P. Friedman, G. Murphy, and F. M. Wolf. "Computer-based Diagnostic Support Systems." Journal of the American Medical Informatics Association 4, no. 3 (May 1, 1997): 256. http://dx.doi.org/10.1136/jamia.1995.0040256.

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8

Lawson, Anton E., and Erno S. Daniel. "Inferences of clinical diagnostic reasoning and diagnostic error." Journal of Biomedical Informatics 44, no. 3 (June 2011): 402–12. http://dx.doi.org/10.1016/j.jbi.2010.01.003.

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Randles, Theodore J., and Cherian S. Thachenkary. "Toward an Understanding of Diagnostic Teleconsultations and Their Impact on Diagnostic Confidence." Telemedicine Journal and e-Health 8, no. 4 (December 2002): 377–85. http://dx.doi.org/10.1089/15305620260507512.

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10

Miller, Randolph A. "Evaluating Evaluations of Medical Diagnostic Systems." Journal of the American Medical Informatics Association 3, no. 6 (November 1, 1996): 429–31. http://dx.doi.org/10.1136/jamia.1996.97084516.

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Haug, P. J., J. P. Ferraro, J. Holmen, X. Wu, K. Mynam, M. Ebert, N. Dean, and J. Jones. "An ontology-driven, diagnostic modeling system." Journal of the American Medical Informatics Association 20, e1 (June 1, 2013): e102-e110. http://dx.doi.org/10.1136/amiajnl-2012-001376.

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Hsu, Wen-Chin, Christopher Denq, and Su-Shing Chen. "A diagnostic methodology for Alzheimer’s disease." Journal of Clinical Bioinformatics 3, no. 1 (2013): 9. http://dx.doi.org/10.1186/2043-9113-3-9.

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Killian, Daniel, Emma Gibson, Mphatso Kachule, Kara Palamountain, Joseph Bitilinyu Bangoh, Sarang Deo, and Jonas Oddur Jonasson. "An Unstructured Supplementary Service Data System for Daily Tracking of Patient Samples and Diagnostic Results in a Diagnostic Network in Malawi: System Development and Field Trial." Journal of Medical Internet Research 23, no. 7 (July 6, 2021): e26582. http://dx.doi.org/10.2196/26582.

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Background Diagnostics in many low- and middle-income countries are conducted through centralized laboratory networks. Samples are collected from patients at remote point-of-care health facilities, and diagnostic tests are performed at centralized laboratories. Sample transportation systems that deliver diagnostic samples and test results are crucial for timely diagnosis and treatment in such diagnostic networks. However, they often lack the timely and accurate data (eg, the quantity and location of samples prepared for collection) required for efficient operation. Objective This study aims to demonstrate the feasibility, adoption, and accuracy of a distributed data collection system that leverages basic mobile phone technology to gather reports on the quantity and location of patient samples and test results prepared for delivery in the diagnostic network of Malawi. Methods We designed a system that leverages unstructured supplementary service data (USSD) technology to enable health workers to submit daily reports describing the quantity of transportation-ready diagnostic samples and test results at specific health care facilities, free of charge with any mobile phone, and aggregate these data for sample transportation administrators. We then conducted a year-long field trial of this system in 51 health facilities serving 3 districts in Malawi. Between July 2019 and July 2020, the participants submitted daily reports containing the number of patient samples or test results designated for viral load, early infant diagnosis, and tuberculosis testing at each facility. We monitored daily participation and compared the submitted USSD reports with program data to assess system feasibility, adoption, and accuracy. Results The participating facilities submitted 37,771 reports over the duration of the field trial. Daily facility participation increased from an average of 50% (26/51) in the first 2 weeks of the trial to approximately 80% (41/51) by the midpoint of the trial and remained at or above 80% (41/51) until the conclusion of the trial. On average, more than 80% of the reports submitted by a facility for a specific type of sample matched the actual number of patient samples collected from that facility by a courier. Conclusions Our findings suggest that a USSD-based system is a feasible, adoptable, and accurate solution to the challenges of untimely, inaccurate, or incomplete data in diagnostic networks. Certain design characteristics of our system, such as the use of USSD, and implementation characteristics, such as the supportive role of the field team, were necessary to ensure high participation and accuracy rates without any explicit financial incentives.

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Grams, Ralph R., James K. Massey, Ze Ming Jin, and Scott Hickey. "Medical diagnostic support for spacecraft." Journal of Medical Systems 10, no. 2 (April 1986): 185–94. http://dx.doi.org/10.1007/bf00993124.

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15

Eckhouse, Richard H., Ruth A. Maulucci, and Elizabeth Leonard. "A computerized kinematic diagnostic system." Journal of Medical Systems 13, no. 5 (October 1989): 261–74. http://dx.doi.org/10.1007/bf00996460.

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Chia, Alvin, Adrian Lim, and Stephen Shumack. "Diagnostic accuracy and image fidelity in dermatology." Journal of Telemedicine and Telecare 12, no. 3_suppl (November 2006): 103–4. http://dx.doi.org/10.1258/135763306779379950.

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Fugleberg, Steen, Anne Greulich, and Doris Irene Stenver. "Computer-assisted diagnosis of acute azotaemia: Diagnostic strategy and diagnostic criteria." Computers in Biology and Medicine 21, no. 6 (January 1991): 399–406. http://dx.doi.org/10.1016/0010-4825(91)90041-7.

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18

Ben-Shabat, Niv, Ariel Sloma, Tomer Weizman, David Kiderman, and Howard Amital. "Assessing the Performance of a New Artificial Intelligence–Driven Diagnostic Support Tool Using Medical Board Exam Simulations: Clinical Vignette Study." JMIR Medical Informatics 9, no. 11 (November 30, 2021): e32507. http://dx.doi.org/10.2196/32507.

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Background Diagnostic decision support systems (DDSS) are computer programs aimed to improve health care by supporting clinicians in the process of diagnostic decision-making. Previous studies on DDSS demonstrated their ability to enhance clinicians’ diagnostic skills, prevent diagnostic errors, and reduce hospitalization costs. Despite the potential benefits, their utilization in clinical practice is limited, emphasizing the need for new and improved products. Objective The aim of this study was to conduct a preliminary analysis of the diagnostic performance of “Kahun,” a new artificial intelligence-driven diagnostic tool. Methods Diagnostic performance was evaluated based on the program’s ability to “solve” clinical cases from the United States Medical Licensing Examination Step 2 Clinical Skills board exam simulations that were drawn from the case banks of 3 leading preparation companies. Each case included 3 expected differential diagnoses. The cases were entered into the Kahun platform by 3 blinded junior physicians. For each case, the presence and the rank of the correct diagnoses within the generated differential diagnoses list were recorded. Each diagnostic performance was measured in two ways: first, as diagnostic sensitivity, and second, as case-specific success rates that represent diagnostic comprehensiveness. Results The study included 91 clinical cases with 78 different chief complaints and a mean number of 38 (SD 8) findings for each case. The total number of expected diagnoses was 272, of which 174 were different (some appeared more than once). Of the 272 expected diagnoses, 231 (87.5%; 95% CI 76-99) diagnoses were suggested within the top 20 listed diagnoses, 209 (76.8%; 95% CI 66-87) were suggested within the top 10, and 168 (61.8%; 95% CI 52-71) within the top 5. The median rank of correct diagnoses was 3 (IQR 2-6). Of the 91 expected diagnoses, 62 (68%; 95% CI 59-78) of the cases were suggested within the top 20 listed diagnoses, 44 (48%; 95% CI 38-59) within the top 10, and 24 (26%; 95% CI 17-35) within the top 5. Of the 91 expected diagnoses, in 87 (96%; 95% CI 91-100), at least 2 out of 3 of the cases’ expected diagnoses were suggested within the top 20 listed diagnoses; 78 (86%; 95% CI 79-93) were suggested within the top 10; and 61 (67%; 95% CI 57-77) within the top 5. Conclusions The diagnostic support tool evaluated in this study demonstrated good diagnostic accuracy and comprehensiveness; it also had the ability to manage a wide range of clinical findings.

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Cochon, Laila, Ronilda Lacson, Aijia Wang, Neena Kapoor, Ivan K. Ip, Sonali Desai, Allen Kachalia, Jack Dennerlein, James Benneyan, and Ramin Khorasani. "Assessing information sources to elucidate diagnostic process errors in radiologic imaging — a human factors framework." Journal of the American Medical Informatics Association 25, no. 11 (August 16, 2018): 1507–15. http://dx.doi.org/10.1093/jamia/ocy103.

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Abstract Objective To assess information sources that may elucidate errors related to radiologic diagnostic imaging, quantify the incidence of potential safety events from each source, and quantify the number of steps involved from diagnostic imaging chain and socio-technical factors. Materials and Methods This retrospective, Institutional Review Board-approved study was conducted at the ambulatory healthcare facilities associated with a large academic hospital. Five information sources were evaluated: an electronic safety reporting system (ESRS), alert notification for critical result (ANCR) system, picture archive and communication system (PACS)-based quality assurance (QA) tool, imaging peer-review system, and an imaging computerized physician order entry (CPOE) and scheduling system. Data from these sources (January-December 2015 for ESRS, ANCR, QA tool, and the peer-review system; January-October 2016 for the imaging ordering system) were collected to quantify the incidence of potential safety events. Reviewers classified events by the step(s) in the diagnostic process they could elucidate, and their socio-technical factors contributors per the Systems Engineering Initiative for Patient Safety (SEIPS) framework. Results Potential safety events ranged from 0.5% to 62.1% of events collected from each source. Each of the information sources contributed to elucidating diagnostic process errors in various steps of the diagnostic imaging chain and contributing socio-technical factors, primarily Person, Tasks, and Tools and Technology. Discussion Various information sources can differentially inform understanding diagnostic process errors related to radiologic diagnostic imaging. Conclusion Information sources elucidate errors in various steps within the diagnostic imaging workflow and can provide insight into socio-technical factors that impact patient safety in the diagnostic process.

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Kawamura, Ren, Yukinori Harada, Shu Sugimoto, Yuichiro Nagase, Shinichi Katsukura, and Taro Shimizu. "Incidence of Diagnostic Errors Among Unexpectedly Hospitalized Patients Using an Automated Medical History–Taking System With a Differential Diagnosis Generator: Retrospective Observational Study." JMIR Medical Informatics 10, no. 1 (January 27, 2022): e35225. http://dx.doi.org/10.2196/35225.

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Background Automated medical history–taking systems that generate differential diagnosis lists have been suggested to contribute to improved diagnostic accuracy. However, the effect of these systems on diagnostic errors in clinical practice remains unknown. Objective This study aimed to assess the incidence of diagnostic errors in an outpatient department, where an artificial intelligence (AI)–driven automated medical history–taking system that generates differential diagnosis lists was implemented in clinical practice. Methods We conducted a retrospective observational study using data from a community hospital in Japan. We included patients aged 20 years and older who used an AI-driven, automated medical history–taking system that generates differential diagnosis lists in the outpatient department of internal medicine for whom the index visit was between July 1, 2019, and June 30, 2020, followed by unplanned hospitalization within 14 days. The primary endpoint was the incidence of diagnostic errors, which were detected using the Revised Safer Dx Instrument by at least two independent reviewers. To evaluate the effect of differential diagnosis lists from the AI system on the incidence of diagnostic errors, we compared the incidence of these errors between a group where the AI system generated the final diagnosis in the differential diagnosis list and a group where the AI system did not generate the final diagnosis in the list; the Fisher exact test was used for comparison between these groups. For cases with confirmed diagnostic errors, further review was conducted to identify the contributing factors of these errors via discussion among three reviewers, using the Safer Dx Process Breakdown Supplement as a reference. Results A total of 146 patients were analyzed. A final diagnosis was confirmed for 138 patients and was observed in the differential diagnosis list from the AI system for 69 patients. Diagnostic errors occurred in 16 out of 146 patients (11.0%, 95% CI 6.4%-17.2%). Although statistically insignificant, the incidence of diagnostic errors was lower in cases where the final diagnosis was included in the differential diagnosis list from the AI system than in cases where the final diagnosis was not included in the list (7.2% vs 15.9%, P=.18). Conclusions The incidence of diagnostic errors among patients in the outpatient department of internal medicine who used an automated medical history–taking system that generates differential diagnosis lists seemed to be lower than the previously reported incidence of diagnostic errors. This result suggests that the implementation of an automated medical history–taking system that generates differential diagnosis lists could be beneficial for diagnostic safety in the outpatient department of internal medicine.

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THOMPSON, DAVID S., ROBERT OBERTEUFFER, and TODD DORMAN. "Sepsis Alert and Diagnostic System." CIN: Computers, Informatics, Nursing 21, no. 1 (January 2003): 22–26. http://dx.doi.org/10.1097/00024665-200301000-00009.

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Hofmann, Bjørn, Eivind Richter Andersen, and Elin Kjelle. "Visualizing the Invisible: Invisible Waste in Diagnostic Imaging." Healthcare 9, no. 12 (December 7, 2021): 1693. http://dx.doi.org/10.3390/healthcare9121693.

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There is extensive waste in diagnostic imaging, at the same time as there are long waiting lists. While the problem of waste in diagnostics has been known for a long time, the problem persists. Accordingly, the objective of this study is to investigate various types of waste in imaging and why they are so pervasive and persistent in today’s health services. After a short overview of different conceptions and types of waste in diagnostic imaging (in radiology), we identify two reasons why these types of waste are so difficult to address: (1) they are invisible in the healthcare system and (2) wasteful imaging is driven by strong external forces and internal drivers. Lastly, we present specific measures to address wasteful imaging. Visualizing and identifying the waste in diagnostic imaging and its ingrained drivers is one important way to improve the quality and efficiency of healthcare services.

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Diamond, L. W., V. G. Mishka, A. H. Seal, and D. T. Nguyen. "Are Normative Expert Systems Appropriate for Diagnostic Pathology?" Journal of the American Medical Informatics Association 2, no. 2 (March 1, 1995): 85–93. http://dx.doi.org/10.1136/jamia.1995.95261910.

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Stausberg, Jürgen, and Michael Person. "A process model of diagnostic reasoning in medicine." International Journal of Medical Informatics 54, no. 1 (April 1999): 9–23. http://dx.doi.org/10.1016/s1386-5056(98)00166-x.

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Jia, Zheng, Xian Zeng, Huilong Duan, Xudong Lu, and Haomin Li. "A patient-similarity-based model for diagnostic prediction." International Journal of Medical Informatics 135 (March 2020): 104073. http://dx.doi.org/10.1016/j.ijmedinf.2019.104073.

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Hajian-Tilaki, Karimollah. "Sample size estimation in diagnostic test studies of biomedical informatics." Journal of Biomedical Informatics 48 (April 2014): 193–204. http://dx.doi.org/10.1016/j.jbi.2014.02.013.

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Hasman, A., J. W. Arends, and L. M. de Bruijn. "Automatic Coding of Diagnostic Reports." Methods of Information in Medicine 37, no. 03 (July 1998): 260–65. http://dx.doi.org/10.1055/s-0038-1634526.

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AbstractA method is presented for assigning classification codes to pathology reports by searching similar reports from an archive collection. The key for searching is textual similarity, which estimates the true, semantic similarity. This method does not require explicit modeling, and can be applied to any language or any application domain that uses natural language reporting. A number of simulation experiments was run to assess the accuracy of the method and to indicate the role of size of the archive and the transfer of document collections across laboratories. In at least 63% of the simulation trials, the most similar archive text offered a suitable classification on organ, origin and diagnosis. In 85 to 90% ofthe trials, the archive's best solution was found within the first five similar reports. The results indicate that the method is suitable for its purpose: suggesting potentially correct classifications to the reporting diagnostician.

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Bankowitz, R., M. McNeil, S. Challinor, and R. Miller. "Effect of a Computer-Assisted General Medicine Diagnostic Consultation Service on Housestaff Diagnostic Strategy." Methods of Information in Medicine 28, no. 04 (October 1989): 352–56. http://dx.doi.org/10.1055/s-0038-1636788.

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Abstract:Quick Medical Reference (QMR) is a microcomputer-based decision sup´port system designed to provide diagnostic assistance in the field of internal medicine. In addition to providing plausible diagnostic hypotheses based upon patient specific findings, the program highlights history, physical and laboratory items which are potentially useful in discriminating among the diagnoses under consideration. We have evaluated the impact of a computer-assisted diagnostic consultation service on the diagnostic and management strategy of a housestaff in a university internal medicine training program. Differential diagnoses were obtained before and after the use of the program, and a questionnaire was used to asses the educational value of the service and the effect of the service on the diagnosis and planned management. Over an eight week period, 31 cases were identified which met inclusion criteria. The QMR consultation added a diagnosis to the original list in 14 out of 31 cases. The consultation reordered the diagnosis in an additional 7 cases, and in 8 cases a diagnosis was ruled out by the use ofthe program. After the use of the program the housestaff reported they would obtain an additional lab test in 10 cases, change the order of planned tests. in two cases and eliminate a lab test in one case. The use of the program V)las rated as helpful educationally in 81 % of the cases, and helpful with respect to management in also 81 % of the cases.

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Brox, Georg A., and Janis L. Huston. "The application of the MPEG-4 standard to telepathology images for electronic patient records." Journal of Telemedicine and Telecare 9, no. 1_suppl (June 2003): 19–21. http://dx.doi.org/10.1258/135763303322196204.

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summary We conducted a pilot study to compare the diagnostic quality of digital telepathology images compressed using two software packages, each of which incorporated the MPEG-4 standard. Both software packages produced lossy (rather than lossless) compressed images. Lossy images can be less reliable for diagnostic purposes and the aim of the study was to examine how the MPEG-4 standard affects image quality. The results showed that manipulation of the images by the software did not dramatically alter the image quality but they highlighted the need to develop both pixel-mapping software and associated standards. Standards will help to determine which bits of a diagnostic image can be safely removed during compression while ensuring the integrity and reliability of images for diagnostic purposes.

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Schiener, R., H. Pillekamp, L. Weber, K. Hartmann, and R. U. Peter. "A teledermatological approach to enhance diagnostic accuracy in dermatohistopathology." Journal of Telemedicine and Telecare 9, no. 3 (June 1, 2003): 135–39. http://dx.doi.org/10.1258/135763303767149924.

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We determined whether digital photographs of skin lesions could enhance diagnostic accuracy in dermatohistological evaluations. Two dermatohistopathologists examined 375 unsorted consecutive cases. On a standardized questionnaire they recorded whether the final diagnostic interpretation would be improved by the availability of digital images of the skin lesions. In 101 cases (27%) they said that digital photographs would be helpful. Subsequently, 30 histological analyses were performed with and without digital photographs of the skin lesions. Presentation of digital photographs reduced the number of differential diagnoses significantly, from a median of 3 to 2. Ratings of ability to make a single definitive diagnosis increased significantly with the presentation of digital photographs. Enhancement of information given by the digital images was scored a median of 6 (on a scale of 0–10, with higher scores reflecting greater enhancement). Digital photographs of skin lesions are likely to refine diagnostic accuracy in histopathology.

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Bang, Chang Seok, Jae Jun Lee, and Gwang Ho Baik. "Computer-Aided Diagnosis of Gastrointestinal Ulcer and Hemorrhage Using Wireless Capsule Endoscopy: Systematic Review and Diagnostic Test Accuracy Meta-analysis." Journal of Medical Internet Research 23, no. 12 (December 14, 2021): e33267. http://dx.doi.org/10.2196/33267.

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Background Interpretation of capsule endoscopy images or movies is operator-dependent and time-consuming. As a result, computer-aided diagnosis (CAD) has been applied to enhance the efficacy and accuracy of the review process. Two previous meta-analyses reported the diagnostic performance of CAD models for gastrointestinal ulcers or hemorrhage in capsule endoscopy. However, insufficient systematic reviews have been conducted, which cannot determine the real diagnostic validity of CAD models. Objective To evaluate the diagnostic test accuracy of CAD models for gastrointestinal ulcers or hemorrhage using wireless capsule endoscopic images. Methods We conducted core databases searching for studies based on CAD models for the diagnosis of ulcers or hemorrhage using capsule endoscopy and presenting data on diagnostic performance. Systematic review and diagnostic test accuracy meta-analysis were performed. Results Overall, 39 studies were included. The pooled area under the curve, sensitivity, specificity, and diagnostic odds ratio of CAD models for the diagnosis of ulcers (or erosions) were .97 (95% confidence interval, .95–.98), .93 (.89–.95), .92 (.89–.94), and 138 (79–243), respectively. The pooled area under the curve, sensitivity, specificity, and diagnostic odds ratio of CAD models for the diagnosis of hemorrhage (or angioectasia) were .99 (.98–.99), .96 (.94–0.97), .97 (.95–.99), and 888 (343–2303), respectively. Subgroup analyses showed robust results. Meta-regression showed that published year, number of training images, and target disease (ulcers vs erosions, hemorrhage vs angioectasia) was found to be the source of heterogeneity. No publication bias was detected. Conclusions CAD models showed high performance for the optical diagnosis of gastrointestinal ulcer and hemorrhage in wireless capsule endoscopy.

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Chang, Che Wei, Feipei Lai, Mesakh Christian, Yu Chun Chen, Ching Hsu, Yo Shen Chen, Dun Hao Chang, Tyng Luen Roan, and Yen Che Yu. "Deep Learning–Assisted Burn Wound Diagnosis: Diagnostic Model Development Study." JMIR Medical Informatics 9, no. 12 (December 2, 2021): e22798. http://dx.doi.org/10.2196/22798.

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Background Accurate assessment of the percentage total body surface area (%TBSA) of burn wounds is crucial in the management of burn patients. The resuscitation fluid and nutritional needs of burn patients, their need for intensive unit care, and probability of mortality are all directly related to %TBSA. It is difficult to estimate a burn area of irregular shape by inspection. Many articles have reported discrepancies in estimating %TBSA by different doctors. Objective We propose a method, based on deep learning, for burn wound detection, segmentation, and calculation of %TBSA on a pixel-to-pixel basis. Methods A 2-step procedure was used to convert burn wound diagnosis into %TBSA. In the first step, images of burn wounds were collected from medical records and labeled by burn surgeons, and the data set was then input into 2 deep learning architectures, U-Net and Mask R-CNN, each configured with 2 different backbones, to segment the burn wounds. In the second step, we collected and labeled images of hands to create another data set, which was also input into U-Net and Mask R-CNN to segment the hands. The %TBSA of burn wounds was then calculated by comparing the pixels of mask areas on images of the burn wound and hand of the same patient according to the rule of hand, which states that one’s hand accounts for 0.8% of TBSA. Results A total of 2591 images of burn wounds were collected and labeled to form the burn wound data set. The data set was randomly split into training, validation, and testing sets in a ratio of 8:1:1. Four hundred images of volar hands were collected and labeled to form the hand data set, which was also split into 3 sets using the same method. For the images of burn wounds, Mask R-CNN with ResNet101 had the best segmentation result with a Dice coefficient (DC) of 0.9496, while U-Net with ResNet101 had a DC of 0.8545. For the hand images, U-Net and Mask R-CNN had similar performance with DC values of 0.9920 and 0.9910, respectively. Lastly, we conducted a test diagnosis in a burn patient. Mask R-CNN with ResNet101 had on average less deviation (0.115% TBSA) from the ground truth than burn surgeons. Conclusions This is one of the first studies to diagnose all depths of burn wounds and convert the segmentation results into %TBSA using different deep learning models. We aimed to assist medical staff in estimating burn size more accurately, thereby helping to provide precise care to burn victims.

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Saudabayeva, G. S., G. K. Sholpankulova, and A. D. Toleukhanova. "PROBLEMS OF PROFESSIONAL TRAINING OF INFORMATICS FUTURE TEACHERS IN THE CONDITIONS OF DIGITALIZATION OF EDUCATION." BULLETIN Series of Pedagogical Sciences 69, no. 1 (May 31, 2021): 66–73. http://dx.doi.org/10.51889/2021-1.1728-5496.09.

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The article is devoted to the formation of diagnostic competence of future teacherspsychologists. Some aspects of the prerequisites for the formation of diagnostic competence are considered. Diagnostic competence is characterized as a new environment of human life and a factor of social change. The concepts related to diagnostic competence are analyzed on the basis of the analysis of scientific research of foreign, Russian and domestic scientists. Of particular importance in the education system is the issue of studying the experience of implementing the diagnostic competence of future educational psychologists . The article analyzes the main theoretical approaches in scientific research on the issues of diagnostic competence of future educational psychologists in the context of cultural and national values. An excursion into the history of the problems of diagnostic competence of future educational psychologists was conducted . Based on the analysis of scientific studies of Russian and domestic scientists, it was decided to determine the main methodological approaches to the diagnostic competence of future educational psychologists. When considering this issue, it is necessary to pay special attention to the historical, cultural and spiritual values of the ethnic group, along with modern socio-cultural realities. There is an urgent need for an interdisciplinary study of the problems of diagnostic competence of future educational psychologists

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Moberly, Aaron C., Margaret Zhang, Lianbo Yu, Metin Gurcan, Caglar Senaras, Theodoros N. Teknos, Charles A. Elmaraghy, Nazhat Taj-Schaal, and Garth F. Essig. "Digital otoscopy versus microscopy: How correct and confident are ear experts in their diagnoses?" Journal of Telemedicine and Telecare 24, no. 7 (May 8, 2017): 453–59. http://dx.doi.org/10.1177/1357633x17708531.

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Introduction With the growing popularity of telemedicine and tele-diagnostics, clinical validation of new devices is essential. This study sought to investigate whether high-definition digital still images of the eardrum provide sufficient information to make a correct diagnosis, as compared with the gold standard view provided by clinical microscopy. Methods Twelve fellowship-trained ear physicians (neurotologists) reviewed the same set of 210 digital otoscope eardrum images. Participants diagnosed each image as normal or, if abnormal, they selected from seven types of ear pathology. Diagnostic percentage correct for each pathology was compared with a gold standard of diagnosis using clinical microscopy with adjunct audiometry and/or tympanometry. Participants also rated their degree of confidence for each diagnosis. Results Overall correctness of diagnosis for ear pathologies ranged from 48.6–100%, depending on the type of pathology. Neurotologists were 72% correct in identifying eardrums as normal. Reviewers’ confidence in diagnosis varied substantially among types of pathology, as well as among participants. Discussion High-definition digital still images of eardrums provided sufficient information for neurotologists to make correct diagnoses for some pathologies. However, some diagnoses, such as middle ear effusion, were more difficult to diagnose when based only on a still image. Levels of confidence of reviewers did not generally correlate with diagnostic ability.

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Lincoln, Michael J., Charles W. Turner, Peter J. Haug, Homer R. Warner, John W. Williamson, Omar Bouhaddou, Sylvia G. Jessen, Dean Sorenson, Robert C. Cundick, and Morgan Grant. "Iliad training enhances medical students' diagnostic skills." Journal of Medical Systems 15, no. 1 (February 1991): 93–110. http://dx.doi.org/10.1007/bf00993883.

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Grams, Ralph R., James K. Massey, Scott Hickey, and Ze Ming Jin. "Diagnostic library support system for medical practice." Journal of Medical Systems 9, no. 5-6 (December 1985): 401–23. http://dx.doi.org/10.1007/bf00992577.

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Grams, Ralph R., Dake Zhang, and Beidi Yue. "MDX—A medical diagnostic decision support system." Journal of Medical Systems 20, no. 3 (June 1996): 129–40. http://dx.doi.org/10.1007/bf02281991.

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Brennan-Jones, Christopher G., Robert H. Eikelboom, and De Wet Swanepoel. "Diagnosis of hearing loss using automated audiometry in an asynchronous telehealth model: A pilot accuracy study." Journal of Telemedicine and Telecare 23, no. 2 (July 9, 2016): 256–62. http://dx.doi.org/10.1177/1357633x16641552.

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Introduction Standard criteria exist for diagnosing different types of hearing loss, yet audiologists interpret audiograms manually. This pilot study examined the feasibility of standardised interpretations of audiometry in a telehealth model of care. The aim of this study was to examine diagnostic accuracy of automated audiometry in adults with hearing loss in an asynchronous telehealth model using pre-defined diagnostic protocols. Materials and methods We recruited 42 study participants from a public audiology and otolaryngology clinic in Perth, Western Australia. Manual audiometry was performed by an audiologist either before or after automated audiometry. Diagnostic protocols were applied asynchronously for normal hearing, disabling hearing loss, conductive hearing loss and unilateral hearing loss. Sensitivity and specificity analyses were conducted using a two-by-two matrix and Cohen’s kappa was used to measure agreement. Results The overall sensitivity for the diagnostic criteria was 0.88 (range: 0.86–1) and overall specificity was 0.93 (range: 0.86–0.97). Overall kappa ( k) agreement was ‘substantial’ k = 0.80 (95% confidence interval (CI) 0.70–0.89) and significant at p < 0.001. Discussion Pre-defined diagnostic protocols applied asynchronously to automated audiometry provide accurate identification of disabling, conductive and unilateral hearing loss. This method has the potential to improve synchronous and asynchronous tele-audiology service delivery.

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Kaur, Arvinder, and Yugal Kumar. "Analyzing Healthcare Data Using Water Wave Optimization-Based Clustering Technique." International Journal of Reliable and Quality E-Healthcare 10, no. 4 (October 2021): 38–57. http://dx.doi.org/10.4018/ijrqeh.2021100103.

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The medical informatics field gets wide attention among the research community while developing a disease diagnosis expert system for useful and accurate predictions. However, accuracy is one of the major medical informatics concerns, especially for disease diagnosis. Many researchers focused on the disease diagnosis system through computational intelligence methods. Hence, this paper describes a new diagnostic model for analyzing healthcare data. The proposed diagnostic model consists of preprocessing, diagnosis, and performance evaluation phases. This model implements the water wave optimization (WWO) algorithm to analyze the healthcare data. Before integrating the WWO algorithm in the proposed model, two modifications are inculcated in WWO to make it more robust and efficient. These modifications are described as global information component and mutation operator. Several performance indicators are applied to assess the diagnostic model. The proposed model achieves better results than existing models and algorithms.

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Berner, E. S. "Diagnostic Decision Support Systems: How to Determine the Gold Standard?" Journal of the American Medical Informatics Association 10, no. 6 (August 4, 2003): 608–10. http://dx.doi.org/10.1197//jamia.m1416.

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Russell, Trevor G., Gwendolen A. Jull, and Richard Wootton. "The diagnostic reliability of Internet-based observational kinematic gait analysis." Journal of Telemedicine and Telecare 9, no. 2_suppl (December 2003): 48–51. http://dx.doi.org/10.1258/135763303322596255.

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Berner, E. S., J. R. Jackson, and J. Algina. "Relationships among Performance Scores of Four Diagnostic Decision Support Systems." Journal of the American Medical Informatics Association 3, no. 3 (May 1, 1996): 208–15. http://dx.doi.org/10.1136/jamia.1996.96310634.

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Berner, E. S., R. S. Maisiak, C. G. Cobbs, and O. D. Taunton. "Effects of a Decision Support System on Physicians' Diagnostic Performance." Journal of the American Medical Informatics Association 6, no. 5 (September 1, 1999): 420–27. http://dx.doi.org/10.1136/jamia.1999.0060420.

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Aljondi, Rowa, and Salem Alghamdi. "Diagnostic Value of Imaging Modalities for COVID-19: Scoping Review." Journal of Medical Internet Research 22, no. 8 (August 19, 2020): e19673. http://dx.doi.org/10.2196/19673.

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Background Coronavirus disease (COVID-19) is a serious infectious disease that causes severe respiratory illness. This pandemic represents a serious public health risk. Therefore, early and accurate diagnosis is essential to control disease progression. Radiological examination plays a crucial role in the early identification and management of infected patients. Objective The aim of this review was to identify the diagnostic value of different imaging modalities used for diagnosis of COVID-19. Methods A comprehensive literature search was conducted using the PubMed, Scopus, Web of Science, and Google Scholar databases. The keywords diagnostic imaging, radiology, respiratory infection, pneumonia, coronavirus infection and COVID-19 were used to identify radiology articles focusing on the diagnosis of COVID-19 and to determine the diagnostic value of various imaging modalities, including x-ray, computed tomography (CT), ultrasound, and nuclear medicine for identification and management of infected patients. Results We identified 50 articles in the literature search. Studies that investigated the diagnostic roles and imaging features of patients with COVID-19, using either chest CT, lung ultrasound, chest x-ray, or positron emission topography/computed tomography (PET/CT) scan, were discussed. Of these imaging modalities, chest x-ray and CT scan are the most commonly used for diagnosis and management of COVID-19 patients, with chest CT scan being more accurate and sensitive in identifying COVID-19 at early stages. Only a few studies have investigated the roles of ultrasound and PET/CT scan in diagnosing COVID-19. Conclusions Chest CT scan remains the most sensitive imaging modality in initial diagnosis and management of suspected and confirmed patients with COVID-19. Other diagnostic imaging modalities could add value in evaluating disease progression and monitoring critically ill patients with COVID-19.

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Ridderikhoff, Jacobus, and Egbert van Herk. "A diagnostic support system in general practice: Is it feasible?" International Journal of Medical Informatics 45, no. 3 (July 1997): 133–43. http://dx.doi.org/10.1016/s1386-5056(97)00022-1.

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Marko-Varga, György. "BioBanking - The Holy Grail of novel drug and diagnostic developments?" Journal of Clinical Bioinformatics 1, no. 1 (2011): 14. http://dx.doi.org/10.1186/2043-9113-1-14.

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Scaria, Joy, Aswathy Sreedharan, and Yung-Fu Chang. "Microbial Diagnostic Array Workstation (MDAW): a web server for diagnostic array data storage, sharing and analysis." Source Code for Biology and Medicine 3, no. 1 (2008): 14. http://dx.doi.org/10.1186/1751-0473-3-14.

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McGorry, P. D., C. S. Wallace, L. H. Low, D. L. Copolov, B. S. Singh, and D. P. McKenzie. "Constructing a Minimal Diagnostic Decision Tree." Methods of Information in Medicine 32, no. 02 (1993): 161–66. http://dx.doi.org/10.1055/s-0038-1634905.

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Abstract:Classification trees and discriminant function analysis were employed in order to ascertain whether a small number of diagnostic decision rules could be extracted from a large inventory of items. Several models, involving up to 17 symptoms, that led to a broad psychiatric diagnosis were then tested on a small validation sample of 53 patients. All methods, with the exception of CART used without any pruning, generated identical trees involving four items. Almost 90% of the validation sample was able to be correctly classified by all methods although poor classification performance was noted in the case of one particular diagnosis, Schizoaffective Psychosis. In contrast, stepwise linear discriminant analysis originally selected 17 items, although three out of the first four items selected were identical to those chosen by the tree-building methods. Although more research is required, there are indications that the latter methods may be usefully employed in constructing parsimonious decision trees.

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Lippi, Giuseppe, and Mario Plebani. "Integrated diagnostics." Biochemia medica 30, no. 1 (February 15, 2020): 18–30. http://dx.doi.org/10.11613/bm.2020.010501.

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The current scenario of in vitro and in vivo diagnostics can be summarized using the “silo metaphor”, where laboratory medicine, pathology and radiology are three conceptually separated diagnostic disciplines, which will increasingly share many comparable features. The substantial progresses in our understanding of biochemical-biological interplays that characterize many human diseases, coupled with extraordinary technical advances, are now generating important multidisciplinary convergences, leading the way to a new frontier, called integrated diagnostics. This new discipline, which is currently defined as convergence of imaging, pathology and laboratory tests with advanced information technology, has an enormous potential for revolutionizing diagnosis and therapeutic management of human diseases, including those causing the largest number of worldwide deaths (i.e. cardiovascular disease, cancer and infectious diseases). However, some important drawbacks should be overcome, mostly represented by insufficient information technology infrastructures, costs and enormous volume of different information that will be integrated and delivered. To overcome these hurdles, some specific strategies should be defined and implemented, such as planning major integration of exiting information systems or developing innovative ones, combining bioinformatics and imaging informatics, using health technology assessment for assessing cost and benefits, providing interpretative comments in integrated reports, developing and using expert systems and neural networks, overcoming cultural and political boundaries for generating multidisciplinary teams and integrated diagnostic algorithms.

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Übeyli, Elif Derya. "Combining Neural Network Models for Automated Diagnostic Systems." Journal of Medical Systems 30, no. 6 (November 3, 2006): 483–88. http://dx.doi.org/10.1007/s10916-006-9034-z.

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