{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T16:47:48Z","timestamp":1784134068379,"version":"3.55.0"},"reference-count":31,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2025,4,6]],"date-time":"2025-04-06T00:00:00Z","timestamp":1743897600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["American J Industrial Med"],"published-print":{"date-parts":[[2025,5]]},"abstract":"<jats:title>ABSTRACT<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>Computer\u2010aided detection (CAD) is emerging as an adjunct to the use of the chest X\u2010ray (CXR) in screening for pulmonary tuberculosis (TB). CAD for silicosis, a fibrotic lung disease due to silica dust and a strong risk factor for TB, is at an earlier stage of development and, unlike TB, depends on expert human reading for validation. For all CAD systems, an important step is the choice of threshold for classifying images as positive or negative for the disease in question. The objective of this article is to present an analytic approach to the choice of threshold in using CAD systems for silicosis.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Drawing on receiver operating curve data from a published study on agreement between CAD and two expert readings of silicosis, two criteria for choosing the sensitivity\/specificity combination were compared\u2014the Youden Index and a minimum sensitivity of 90%. We explore the impact of criterion selection, silicosis definition, and reader on the choice and interpretation of threshold, as well as the influence of positive predictive value (PPV) derived from screen prevalence. We present a novel technique for using two CAD thresholds to distinguish images with a high likelihood of being of positive or negative from those characterized by uncertainty.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The sample was 501 CXR images from ex\u2010gold miners. Derived thresholds varied across the two criteria, as well as across silicosis definition and expert reader. Varying the notional disease prevalence produced large differences in PPV and, therefore, proportions of false positives. The implications of these variations affecting threshold choice are described for three use cases\u2014annual screening of active miners, outreach screening of former miners, and adjudication of claims for silicosis compensation.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>In applying CAD to silicosis, users need to establish the use case, their preference for the sensitivity\/specificity trade\u2010off, and the silicosis definition, as well as considering the effect of disease prevalence. System developers need to take inter\u2010reader variation in validation exercises into account and present this information transparently. A two\u2010threshold model has potential utility in situations of high screening volume where there is a significant cost associated with referral for confirmation of diagnosis.<\/jats:p><\/jats:sec>","DOI":"10.1002\/ajim.23720","type":"journal-article","created":{"date-parts":[[2025,4,6]],"date-time":"2025-04-06T10:47:10Z","timestamp":1743936430000},"page":"464-472","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Determining Thresholds for Computer\u2010Aided Detection for Silicosis\u2014An Analytic Approach"],"prefix":"10.1002","volume":"68","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9958-9772","authenticated-orcid":false,"given":"Stephen","family":"Barker","sequence":"first","affiliation":[{"name":"School of Population and Public Health University of British Columbia Vancouver Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9103-4051","authenticated-orcid":false,"given":"Annalee","family":"Yassi","sequence":"additional","affiliation":[{"name":"School of Population and Public Health University of British Columbia Vancouver Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jerry","family":"Spiegel","sequence":"additional","affiliation":[{"name":"School of Population and Public Health University of British Columbia Vancouver Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Barry","family":"Kistnasamy","sequence":"additional","affiliation":[{"name":"Office of the Compensation Commissioner for Occupational Diseases Johannesburg South Africa"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5736-9237","authenticated-orcid":false,"given":"Rodney","family":"Ehrlich","sequence":"additional","affiliation":[{"name":"Division of Occupational Medicine, School of Public Health University of Cape Town Cape Town South Africa"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,4,6]]},"reference":[{"key":"e_1_2_12_2_1","first-page":"1","article-title":"The ILO\/WHO Global Programme for the Elimination of Silicosis (GPES)","volume":"12","author":"Fedotov I. 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Module 2: Screening\u2014Systematic Screening for Tuberculosis Disease \u201d World Health Organization accessed October 30 2024 https:\/\/www.who.int\/publications\/i\/item\/9789240022676."},{"key":"e_1_2_12_9_1","doi-asserted-by":"publisher","DOI":"10.1093\/cid\/ciaa1827"},{"key":"e_1_2_12_10_1","doi-asserted-by":"publisher","DOI":"10.3390\/ijerph18179091"},{"key":"e_1_2_12_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/S2589-7500(21)00116-3"},{"key":"e_1_2_12_12_1","doi-asserted-by":"publisher","DOI":"10.5588\/ijtld.22.0687"},{"key":"e_1_2_12_13_1","doi-asserted-by":"publisher","DOI":"10.1186\/s44263-023-00033-2"},{"key":"e_1_2_12_14_1","doi-asserted-by":"publisher","DOI":"10.5588\/ijtld.22.0437"},{"key":"e_1_2_12_15_1","unstructured":"\u201cDetermining the Local Calibration of Computer\u2010Assisted Detection (CAD) Thresholds and Other Parameters: A Toolkit to Support the Effective Use of CAD for TB Screening \u201d World Health Organization 2021 https:\/\/iris.who.int\/bitstream\/handle\/10665\/345925\/9789240028616-eng.pdf."},{"key":"e_1_2_12_16_1","unstructured":"\u201cStop TB Partnership. 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