[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119243-en":3,"doc-seo-119243-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119243,687197207639,"Asher","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Machine Learning Augmented Interpretation of Chest X-rays - A Systematic Review","Limitations of chest X-ray (CXR) interpretation have driven development of machine learning systems intended to support clinicians and enhance accuracy. This systematic review summarizes machine learning applications designed to facilitate CXR interpretation, focusing on algorithms that detect more than two radiographic findings and were published from January 2020 to September 2022. From an initial 2248 records, 46 studies were included and model details, risk of bias, and study quality were analyzed. Results indicate strong standalone performance, and multiple studies show improved clinician diagnostic and classification performance when models function as diagnostic assistance devices, with effects assessed through clinician comparisons and evaluation of clinical perception and diagnosis.","diagnostics  \nSystematic Review  \nMachine Learning Augmented Interpretation of Chest X-rays: A Systematic Review  \nHassan K. Ahmad 1,2,*, Michael R. Milne 1, Quinlan D. Buchlak 1,3,4, Nalan Ektas 1, Georgina Sanderson 1, Hadi Chamtie 1, Sajith Karunasena 1, Jason Chiang 1,5,6, Xavier Holt 1, Cyril H. M. Tang 1, Jarrel C. Y. Seah 1,7, Georgina Bottrell 1, Nazanin Esmaili 3,8, Peter Brotchie 1,9 and Catherine Jones 1,10,11,12  \nCitation: Ahmad, H.K.; Milne, M.R.; Buchlak, Q.D.; Ektas, N.; Sanderson, G.; Chamtie, H.; Karunasena, S.; Chiang, J.; Holt, X.; Tang, C.H.M.; et al. Machine Learning Augmented Interpretation of Chest X-rays: A Systematic Review. Diagnostics 2023, 13, 743. [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)diagnostics13040743  \nReceived: 19 January 2023  \nRevised: 13 February 2023  \nAccepted: 14 February 2023  \nPublished: 15 February 2023  \nCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n[1](1 Annalise.ai)[ Annalise.ai](1 Annalise.ai), Sydney, NSW 2000, Australia  \n2 Department of Emergency Medicine, Royal North Shore Hospital, Sydney, NSW 2065, Australia  \n3 School of Medicine, University of Notre Dame Australia, Sydney, NSW 2007, Australia  \n4 Department of Neurosurgery, Monash Health, Melbourne, VIC 3168, Australia  \n5 Department of General Practice, University of Melbourne, Melbourne, VIC 3010, Australia  \n6 Westmead Applied Research Centre, University of Sydney, Sydney, NSW 2006, Australia  \n7 Department of Radiology, Alfred Health, Melbourne, VIC 3004, Australia  \n8 Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia  \n9 Department of Radiology, St Vincent's Health Australia, Melbourne, VIC 3065, Australia  \n10 I-MED Radiology Network, Brisbane, QLD 4006, Australia  \n11 School of Public and Preventive Health, Monash University, Clayton, VIC 3800, Australia  \n12 Department of Clinical Imaging Science, University of Sydney, Sydney, NSW 2006, Australia  \n* [Correspondence: hassan.ahmad@annalise.ai](Correspondence: hassan.ahmad@annalise.ai)  \nAbstract: Limitations of the chest X-ray (CXR) have resulted in attempts to create machine learning systems to assist clinicians and improve interpretation accuracy. An understanding of the capabilities and limitations of modern machine learning systems is necessary for clinicians as these tools begin to permeate practice. This systematic review aimed to provide an overview of machine learning applications designed to facilitate CXR interpretation. A systematic search strategy was executed to identify research into machine learning algorithms capable of detecting >2 radiographic findings on CXRs published between January 2020 and September 2022 . Model details and study characteristics, including risk of bias and quality, were summarized. Initially, 2248 articles were retrieved, with 46 included in the final review. Published models demonstrated strong standalone performance and were typically as accurate, or more accurate, than radiologists or non-radiologist clinicians. Multiple studies demonstrated an improvement in the clinical finding classification performance of clinicians when models acted as a diagnostic assistance device. Device performance was compared with that of clinicians in 30% of studies, while effects on clinical perception and diagnosis were evaluated in 19% . Only one study was prospectively run. On average, 128,662 images were used to train and validate models. Most classified less than eight clinical findings, while the three most comprehensive models classified 54, 72, and 124 findings. This review suggests that machine learning devices designed to facilita","cbCaivgyLh1XtQ5w","https://ap.wps.com/l/cbCaivgyLh1XtQ5w","pdf",1605771,1,31,"English","en",105,"# Introduction\n## Chest X-ray limitations and need for decision support\n# Methods\n## Systematic search and study selection\n## Model and study characteristics, risk of bias\n# Results\n## Included studies and publication period\n## Standalone model performance\n## Clinical assistance effects\n# Discussion\n## Implementation considerations and limitations\n## Role of clinician involvement","[{\"question\":\"What problem does the review address in chest X-ray interpretation?\",\"answer\":\"It addresses the limitations of CXR interpretation, including reduced sensitivity for subtle findings and human factors that contribute to missed or inaccurate assessments.\"},{\"question\":\"How was the systematic review conducted and what time window was included?\",\"answer\":\"A systematic search identified machine learning research for detecting more than two radiographic findings on CXRs published between January 2020 and September 2022.\"},{\"question\":\"What overall performance and clinical impact do the reported models show?\",\"answer\":\"Published models showed strong standalone performance and, in several studies, improved clinicians’ clinical finding classification and diagnostic performance when used as diagnostic assistance devices.\"}]","Machine Learning Augmented Interpretation of Chest X-rays - 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