{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T08:45:16Z","timestamp":1780389916351,"version":"3.54.1"},"reference-count":62,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2023,11,29]],"date-time":"2023-11-29T00:00:00Z","timestamp":1701216000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"funder":[{"DOI":"10.13039\/100000054","name":"National Cancer Institute","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000054","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01 CA225585"],"award-info":[{"award-number":["R01 CA225585"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01 CA172343"],"award-info":[{"award-number":["R01 CA172343"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01 CA140560"],"award-info":[{"award-number":["R01 CA140560"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,2,16]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Objective<\/jats:title>\n                  <jats:p>This study explores the feasibility of using machine learning to predict accurate versus inaccurate diagnoses made by pathologists based on their spatiotemporal viewing behavior when evaluating digital breast biopsy images.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Materials and Methods<\/jats:title>\n                  <jats:p>The study gathered data from 140 pathologists of varying experience levels who each reviewed a set of 14 digital whole slide images of breast biopsy tissue. Pathologists\u2019 viewing behavior, including zooming and panning actions, was recorded during image evaluation. A total of 30 features were extracted from the viewing behavior data, and 4 machine learning algorithms were used to build classifiers for predicting diagnostic accuracy.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>The Random Forest classifier demonstrated the best overall performance, achieving a test accuracy of 0.81 and area under the receiver-operator characteristic curve of 0.86. Features related to attention distribution and focus on critical regions of interest were found to be important predictors of diagnostic accuracy. Further including case-level and pathologist-level information incrementally improved classifier performance.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Discussion<\/jats:title>\n                  <jats:p>Results suggest that pathologists\u2019 viewing behavior during digital image evaluation can be leveraged to predict diagnostic accuracy, affording automated feedback and decision support systems based on viewing behavior to aid in training and, ultimately, clinical practice. They also carry implications for basic research examining the interplay between perception, thought, and action in diagnostic decision-making.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusion<\/jats:title>\n                  <jats:p>The classifiers developed herein have potential applications in training and clinical settings to provide timely feedback and support to pathologists during diagnostic decision-making. Further research could explore the generalizability of these findings to other medical domains and varied levels of expertise.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/jamia\/ocad232","type":"journal-article","created":{"date-parts":[[2023,11,30]],"date-time":"2023-11-30T08:00:25Z","timestamp":1701331225000},"page":"552-562","source":"Crossref","is-referenced-by-count":11,"title":["Machine learning classification of diagnostic accuracy in pathologists interpreting breast biopsies"],"prefix":"10.1093","volume":"31","author":[{"given":"Tad T","family":"Bruny\u00e9","sequence":"first","affiliation":[{"name":"Center for Applied Brain and Cognitive Sciences, Tufts University , Medford, MA 02155, United States"},{"name":"Department of Psychology, Tufts University , Medford, MA 02155, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kelsey","family":"Booth","sequence":"additional","affiliation":[{"name":"Center for Applied Brain and Cognitive Sciences, Tufts University , Medford, MA 02155, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dalit","family":"Hendel","sequence":"additional","affiliation":[{"name":"Center for Applied Brain and Cognitive Sciences, Tufts University , Medford, MA 02155, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6438-9583","authenticated-orcid":false,"given":"Kathleen F","family":"Kerr","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, University of Washington , Seattle, WA 98105, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hannah","family":"Shucard","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, University of Washington , Seattle, WA 98105, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Donald L","family":"Weaver","sequence":"additional","affiliation":[{"name":"Department of Pathology and Laboratory Medicine, Larner College of Medicine, University of Vermont and Vermont Cancer Center , Burlington, VT 05405, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joann G","family":"Elmore","sequence":"additional","affiliation":[{"name":"Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles , Los Angeles, CA 90095, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2023,11,29]]},"reference":[{"issue":"8","key":"2024021710220071000_ocad232-B1","doi-asserted-by":"crossref","first-page":"481","DOI":"10.7326\/0003-4819-155-8-201110180-00004","article-title":"Cumulative probability of false-positive recall or biopsy recommendation after 10 years of screening mammography: a cohort study","volume":"155","author":"Hubbard","year":"2011","journal-title":"Ann Intern Med"},{"issue":"16","key":"2024021710220071000_ocad232-B2","doi-asserted-by":"crossref","first-page":"1089","DOI":"10.1056\/NEJM199804163381601","article-title":"Ten-year risk of false positive screening mammograms and clinical breast examinations","volume":"338","author":"Elmore","year":"1998","journal-title":"N Engl J Med"},{"key":"2024021710220071000_ocad232-B3","author":"Dahabreh","year":"2014"},{"issue":"2","key":"2024021710220071000_ocad232-B4","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1038\/35101087","article-title":"Microarray and histopathological analysis of tumours: the future and the past?","volume":"1","author":"Lakhani","year":"2001","journal-title":"Nat Rev Cancer"},{"key":"2024021710220071000_ocad232-B5","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1007\/978-1-59259-664-5_6","volume-title":"Principles of Molecular Oncology","author":"Jones","year":"2004"},{"issue":"7","key":"2024021710220071000_ocad232-B6","first-page":"1","article-title":"A review of eye tracking for understanding and improving diagnostic interpretation","volume":"4","author":"Bruny\u00e9","year":"2019","journal-title":"Cogn Res Princ Implic"},{"issue":"5","key":"2024021710220071000_ocad232-B7","doi-asserted-by":"crossref","first-page":"1205","DOI":"10.3758\/APP.72.5.1205","article-title":"Current perspectives in medical image perception","volume":"72","author":"Krupinski","year":"2010","journal-title":"Atten Percept Psychophys"},{"issue":"12","key":"2024021710220071000_ocad232-B8","doi-asserted-by":"crossref","first-page":"1543","DOI":"10.1016\/j.humpath.2006.08.024","article-title":"Eye-movement study and human performance using telepathology virtual slides. 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