{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T23:36:27Z","timestamp":1780443387969,"version":"3.54.1"},"reference-count":23,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,2,2]],"date-time":"2023-02-02T00:00:00Z","timestamp":1675296000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Purpose: Manual interpretation of chest radiographs is a challenging task and is prone to errors. An automated system capable of categorizing chest radiographs based on the pathologies identified could aid in the timely and efficient diagnosis of chest pathologies. Method: For this retrospective study, 4476 chest radiographs were collected between January and April 2021 from two tertiary care hospitals. Three expert radiologists established the ground truth, and all radiographs were analyzed using a deep-learning AI model to detect suspicious ROIs in the lungs, pleura, and cardiac regions. Three test readers (different from the radiologists who established the ground truth) independently reviewed all radiographs in two sessions (unaided and AI-aided mode) with a washout period of one month. Results: The model demonstrated an aggregate AUROC of 91.2% and a sensitivity of 88.4% in detecting suspicious ROIs in the lungs, pleura, and cardiac regions. These results outperform unaided human readers, who achieved an aggregate AUROC of 84.2% and sensitivity of 74.5% for the same task. When using AI, the aided readers obtained an aggregate AUROC of 87.9% and a sensitivity of 85.1%. The average time taken by the test readers to read a chest radiograph decreased by 21% (p &lt; 0.01) when using AI. Conclusion: The model outperformed all three human readers and demonstrated high AUROC and sensitivity across two independent datasets. When compared to unaided interpretations, AI-aided interpretations were associated with significant improvements in reader performance and chest radiograph interpretation time.<\/jats:p>","DOI":"10.3390\/diagnostics13030557","type":"journal-article","created":{"date-parts":[[2023,2,3]],"date-time":"2023-02-03T03:04:23Z","timestamp":1675393463000},"page":"557","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Validation of a Deep Learning Model for Detecting Chest Pathologies from Digital Chest Radiographs"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8801-0235","authenticated-orcid":false,"given":"Pranav","family":"Ajmera","sequence":"first","affiliation":[{"name":"Dr. D.Y. Patil Hospital, D.Y. Patil University, Pune 411018, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Prashant","family":"Onkar","sequence":"additional","affiliation":[{"name":"Lata Mangeshkar Hospital, Nagpur 440012, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sanjay","family":"Desai","sequence":"additional","affiliation":[{"name":"Deenanath Mangeshkar Hospital and Research Centre, Pune 411004, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8134-0423","authenticated-orcid":false,"given":"Richa","family":"Pant","sequence":"additional","affiliation":[{"name":"DeepTek Medical Imaging Pvt Ltd., Pune 411045, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jitesh","family":"Seth","sequence":"additional","affiliation":[{"name":"DeepTek Medical Imaging Pvt Ltd., Pune 411045, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tanveer","family":"Gupte","sequence":"additional","affiliation":[{"name":"DeepTek Medical Imaging Pvt Ltd., Pune 411045, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Viraj","family":"Kulkarni","sequence":"additional","affiliation":[{"name":"DeepTek Medical Imaging Pvt Ltd., Pune 411045, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6891-2044","authenticated-orcid":false,"given":"Amit","family":"Kharat","sequence":"additional","affiliation":[{"name":"DeepTek Medical Imaging Pvt Ltd., Pune 411045, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nandini","family":"Passi","sequence":"additional","affiliation":[{"name":"Post Graduate Institute of Medical Education and Research, Chandigarh 160012, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sanjay","family":"Khaladkar","sequence":"additional","affiliation":[{"name":"Dr. D.Y. Patil Hospital, D.Y. Patil University, Pune 411018, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"V. M.","family":"Kulkarni","sequence":"additional","affiliation":[{"name":"Dr. D.Y. Patil Hospital, D.Y. Patil University, Pune 411018, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,2]]},"reference":[{"key":"ref_1","unstructured":"GBD 2015 mortality and causes of death collaborators (2016). Global, regional, and national life expectancy, all-cause mortality, and cause-specific mortality for 249 causes of death, 1980\u20132015: A systematic analysis for the global burden of disease study 2015. Lancet, 388, 1459\u20131544."},{"key":"ref_2","unstructured":"(2022, November 02). ACR\u2013SPR\u2013STR Practice Parameter for the Performance of Chest Radiography. 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