{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T12:08:19Z","timestamp":1790770099475,"version":"4.1.0"},"reference-count":34,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2022,11,7]],"date-time":"2022-11-07T00:00:00Z","timestamp":1667779200000},"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>In medical practice, chest X-rays are the most ubiquitous diagnostic imaging tests. However, the current workload in extensive health care facilities and lack of well-trained radiologists is a significant challenge in the patient care pathway. Therefore, an accurate, reliable, and fast computer-aided diagnosis (CAD) system capable of detecting abnormalities in chest X-rays is crucial in improving the radiological workflow. In this prospective multicenter quality-improvement study, we have evaluated whether artificial intelligence (AI) can be used as a chest X-ray screening tool in real clinical settings. Methods: A team of radiologists used the AI-based chest X-ray screening tool (qXR) as a part of their daily reporting routine to report consecutive chest X-rays for this prospective multicentre study. This study took place in a large radiology network in India between June 2021 and March 2022. Results: A total of 65,604 chest X-rays were processed during the study period. The overall performance of AI achieved in detecting normal and abnormal chest X-rays was good. The high negatively predicted value (NPV) of 98.9% was achieved. The AI performance in terms of area under the curve (AUC), NPV for the corresponding subabnormalities obtained were blunted CP angle (0.97, 99.5%), hilar dysmorphism (0.86, 99.9%), cardiomegaly (0.96, 99.7%), reticulonodular pattern (0.91, 99.9%), rib fracture (0.98, 99.9%), scoliosis (0.98, 99.9%), atelectasis (0.96, 99.9%), calcification (0.96, 99.7%), consolidation (0.95, 99.6%), emphysema (0.96, 99.9%), fibrosis (0.95, 99.7%), nodule (0.91, 99.8%), opacity (0.92, 99.2%), pleural effusion (0.97, 99.7%), and pneumothorax (0.99, 99.9%). Additionally, the turnaround time (TAT) decreased by about 40.63% from pre-qXR period to post-qXR period. Conclusions: The AI-based chest X-ray solution (qXR) screened chest X-rays and assisted in ruling out normal patients with high confidence, thus allowing the radiologists to focus more on assessing pathology on abnormal chest X-rays and treatment pathways.<\/jats:p>","DOI":"10.3390\/diagnostics12112724","type":"journal-article","created":{"date-parts":[[2022,11,8]],"date-time":"2022-11-08T07:00:42Z","timestamp":1667890842000},"page":"2724","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Role of an Automated Deep Learning Algorithm for Reliable Screening of Abnormality in Chest Radiographs: A Prospective Multicenter Quality Improvement Study"],"prefix":"10.3390","volume":"12","author":[{"given":"Arunkumar","family":"Govindarajan","sequence":"first","affiliation":[{"name":"Aarthi Scans & Labs, Chennai 600026, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aarthi","family":"Govindarajan","sequence":"additional","affiliation":[{"name":"Aarthi Scans & Labs, Chennai 600026, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Swetha","family":"Tanamala","sequence":"additional","affiliation":[{"name":"Qure.ai, Mumbai 400063, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1218-0769","authenticated-orcid":false,"given":"Subhankar","family":"Chattoraj","sequence":"additional","affiliation":[{"name":"Qure.ai, Mumbai 400063, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7643-7346","authenticated-orcid":false,"given":"Bhargava","family":"Reddy","sequence":"additional","affiliation":[{"name":"Qure.ai, Mumbai 400063, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rohitashva","family":"Agrawal","sequence":"additional","affiliation":[{"name":"Qure.ai, Mumbai 400063, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Divya","family":"Iyer","sequence":"additional","affiliation":[{"name":"Qure.ai, Mumbai 400063, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anumeha","family":"Srivastava","sequence":"additional","affiliation":[{"name":"Qure.ai, Mumbai 400063, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pradeep","family":"Kumar","sequence":"additional","affiliation":[{"name":"Qure.ai, Mumbai 400063, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Preetham","family":"Putha","sequence":"additional","affiliation":[{"name":"Qure.ai, Mumbai 400063, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"545","DOI":"10.1378\/chest.10-1302","article-title":"Interpretation of plain chest roentgenogram","volume":"141","author":"Raoof","year":"2012","journal-title":"Chest"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Mathers, C.D., and Loncar, D. 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