{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,24]],"date-time":"2026-10-24T15:53:06Z","timestamp":1792857186730,"version":"4.5.0"},"reference-count":29,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2025,10,19]],"date-time":"2025-10-19T00:00:00Z","timestamp":1760832000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National Science and Technology Council","award":["NSTC 112-2314-B-A49-063-MY3"],"award-info":[{"award-number":["NSTC 112-2314-B-A49-063-MY3"]}]},{"DOI":"10.13039\/501100011913","name":"Kaohsiung Veterans General Hospital","doi-asserted-by":"publisher","award":["KSVGH-113-115"],"award-info":[{"award-number":["KSVGH-113-115"]}],"id":[{"id":"10.13039\/501100011913","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Objectives: We aimed to investigate whether AI assistance could improve the performance of pneumothorax detection on chest radiographs (CXR) by readers with varying experience from radiologists to the frontline healthcare providers, and whether AI assistance could diminish the potential confounders for readers\u2019 detecting pneumothorax. Methods: In this retrospective, single-center, blinded, multi-reader diagnostic accuracy study, 125 CXRs were prepared from radiological information system (March 2024 to August 2024) for test. The 18 readers were composed of six groups, each had 3 persons: board-certified radiologists (Group-1), senior radiology residents (Group-2), junior radiology residents (Group-3), postgraduate year residents (Group-4), senior radiographers (Group-5), and junior radiographers (Group-6). They read the CXR independently twice, without and with AI assistance, at an interval of one month. We used receiver operating characteristic curve for performance analysis and generalized estimating equation (GEE) model for confounding factor analysis. Results: AI software alone achieved a high area under curve of 0.965 (95% CI: 0.926, 0.995). With AI assistance, the performance in all groups significantly improved (p &lt; 0.01) especially the junior readers (the frontline healthcare providers, Group-3, 4, 6) and diminished the difference among all groups except some related to Group-1. GEE model showed that AI assistance, reader\u2019s experience, and projection type interfere with the readers\u2019 performance (all p &lt; 0.05). Conclusions: AI assistance could improve the performance of pneumothorax detection by varying experience of readers, especially the frontline healthcare providers. The influence of confounders, such as reader\u2019s experience, also be diminished by AI assistance.<\/jats:p>","DOI":"10.3390\/diagnostics15202639","type":"journal-article","created":{"date-parts":[[2025,10,20]],"date-time":"2025-10-20T09:23:34Z","timestamp":1760952214000},"page":"2639","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Impact of AI Assistance in Pneumothorax Detection on Chest Radiographs Among Readers of Varying Experience"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-5296-4027","authenticated-orcid":false,"given":"Chen-Wei","family":"Ho","sequence":"first","affiliation":[{"name":"Department of Radiology, Kaohsiung Veterans General Hospital, No. 386, Dazhong 1st Rd., Zuoying Dist., Kaohsiung City 813414, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu-Lun","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, Kaohsiung Veterans General Hospital, No. 386, Dazhong 1st Rd., Zuoying Dist., Kaohsiung City 813414, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi-Chun","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Radiology, Kaohsiung Veterans General Hospital, No. 386, Dazhong 1st Rd., Zuoying Dist., Kaohsiung City 813414, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu-Jeng","family":"Ju","sequence":"additional","affiliation":[{"name":"School of Occupational Therapy, College of Medicine, National Taiwan University, No. 1, Sec. 1, Ren\u2019ai Rd., Zhongzheng Dist., Taipei City 100233, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2850-1400","authenticated-orcid":false,"given":"Ming-Ting","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Radiology, Kaohsiung Veterans General Hospital, No. 386, Dazhong 1st Rd., Zuoying Dist., Kaohsiung City 813414, Taiwan"},{"name":"School of Medicine, College of Medicine, National Yang Ming Chiao Tung University, No. 155, Sec. 2, Linong St., Beitou Dist., Taipei City 112304, Taiwan"},{"name":"Institute of Clinical Medicine, College of Medicine, National Yang Ming Chiao Tung University, No. 155, Sec. 2, Linong St., Beitou Dist., Taipei City 112304, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"650","DOI":"10.1016\/j.chest.2022.10.039","article-title":"Commonly Missed Findings on Chest Radiographs: Causes and Consequences","volume":"163","author":"Gefter","year":"2023","journal-title":"Chest"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Kaviani, P., Kalra, M.K., Digumarthy, S.R., Gupta, R.V., Dasegowda, G., Jagirdar, A., Gupta, S., Putha, P., Mahajan, V., and Reddy, B. (2022). Frequency of Missed Findings on Chest Radiographs (CXRs) in an International, Multicenter Study: Application of AI to Reduce Missed Findings. Diagnostics, 12.","DOI":"10.3390\/diagnostics12102382"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Kao, C.Y., Lin, C.Y., Chao, C.C., Huang, H.S., Lee, H.Y., Chang, C.M., Sung, K., Chen, T.R., Chiang, P.C., and Huang, L.T. (2021). Automated Radiology Alert System for Pneumothorax Detection on Chest Radiographs Improves Efficiency and Diagnostic Performance. Diagnostics, 11.","DOI":"10.3390\/diagnostics11071182"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Taylor, A.G., Mielke, C., and Mongan, J. (2018). Automated detection of moderate and large pneumothorax on frontal chest X-rays using deep convolutional neural networks: A retrospective study. PLoS Med., 15.","DOI":"10.1371\/journal.pmed.1002697"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"e231236","DOI":"10.1148\/radiol.231236","article-title":"Commercially Available Chest Radiograph AI Tools for Detecting Airspace Disease, Pneumothorax, and Pleural Effusion","volume":"308","author":"Muller","year":"2023","journal-title":"Radiology"},{"key":"ref_6","first-page":"161","article-title":"Artificial Intelligence in Radiology: Current Applications and Future Prospects","volume":"9","year":"2021","journal-title":"Kurd. Stud."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Lee, S., Kim, E.K., Han, K., Ryu, L., Lee, E.H., and Shin, H.J. (2024). Factors for increasing positive predictive value of pneumothorax detection on chest radiographs using artificial intelligence. Sci. Rep., 14.","DOI":"10.1038\/s41598-024-70780-1"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"792","DOI":"10.1097\/RLI.0000000000000707","article-title":"Impact of Confounding Thoracic Tubes and Pleural Dehiscence Extent on Artificial Intelligence Pneumothorax Detection in Chest Radiographs","volume":"55","author":"Rueckel","year":"2020","journal-title":"Investig. Radiol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"3469","DOI":"10.1007\/s00330-021-08397-5","article-title":"Diagnostic effect of artificial intelligence solution for referable thoracic abnormalities on chest radiography: A multicenter respiratory outpatient diagnostic cohort study","volume":"32","author":"Jin","year":"2022","journal-title":"Eur. Radiol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"P1165","DOI":"10.1016\/j.acra.2024.10.012","article-title":"Real-World Performance of Pneumothorax-Detecting Artificial Intelligence Algorithm and its Impact on Radiologist Reporting Times","volume":"32","author":"Hunter","year":"2024","journal-title":"Acad. Radiol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"e230860","DOI":"10.1148\/radiol.230860","article-title":"Using AI to Improve Radiologist Performance in Detection of Abnormalities on Chest Radiographs","volume":"309","author":"Bennani","year":"2023","journal-title":"Radiology"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"602","DOI":"10.1136\/emermed-2023-213620","article-title":"Evaluation of the impact of artificial intelligence-assisted image interpretation on the diagnostic performance of clinicians in identifying pneumothoraces on plain chest X-ray: A multi-case multi-reader study","volume":"41","author":"Novak","year":"2024","journal-title":"Emerg. Med. J."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"ii18","DOI":"10.1136\/thx.2010.136986","article-title":"Management of spontaneous pneumothorax: British Thoracic Society Pleural Disease Guideline 2010","volume":"65","author":"MacDuff","year":"2010","journal-title":"Thorax"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2003061","DOI":"10.1183\/13993003.03061-2020","article-title":"Development and validation of a deep learning algorithm detecting 10 common abnormalities on chest radiographs","volume":"57","author":"Nam","year":"2021","journal-title":"Eur. Respir. J."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"652","DOI":"10.1148\/radiol.2020200165","article-title":"Deep Learning-based Automatic Detection Algorithm for Reducing Overlooked Lung Cancers on Chest Radiographs","volume":"296","author":"Jang","year":"2020","journal-title":"Radiology"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"458","DOI":"10.1177\/08465371231225123","article-title":"Artificial Intelligence for the Detection of Pneumothorax on Chest Radiograph: Not yet the Panacea","volume":"75","author":"Chassagnon","year":"2024","journal-title":"Can. Assoc. Radiol. J."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"e2247172","DOI":"10.1001\/jamanetworkopen.2022.47172","article-title":"Evaluation of an Artificial Intelligence Model for Detection of Pneumothorax and Tension Pneumothorax in Chest Radiographs","volume":"5","author":"Hillis","year":"2022","journal-title":"JAMA Netw. Open"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"220259","DOI":"10.1183\/16000617.0259-2022","article-title":"Deep learning for pneumothorax diagnosis: A systematic review and meta-analysis","volume":"32","author":"Sugibayashi","year":"2023","journal-title":"Eur. Respir. Rev."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"e200190","DOI":"10.1148\/ryai.2021200190","article-title":"Deep Learning Systems for Pneumothorax Detection on Chest Radiographs: A Multicenter External Validation Study","volume":"3","author":"Thian","year":"2021","journal-title":"Radiol. Artif. Intell."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"145817","DOI":"10.1109\/ACCESS.2021.3122998","article-title":"Automatic Diagnosis of Pneumothorax from Chest Radiographs: A Systematic Literature Review","volume":"9","author":"Iqbal","year":"2021","journal-title":"IEEE Access"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"525","DOI":"10.1177\/08465371231220885","article-title":"Deep Learning for Pneumothorax Detection on Chest Radiograph: A Diagnostic Test Accuracy Systematic Review and Meta Analysis","volume":"75","author":"Katzman","year":"2024","journal-title":"Can. Assoc. Radiol. J."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"e2229289","DOI":"10.1001\/jamanetworkopen.2022.29289","article-title":"Association of Artificial Intelligence-Aided Chest Radiograph Interpretation with Reader Performance and Efficiency","volume":"5","author":"Ahn","year":"2022","journal-title":"JAMA Netw. Open"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.crad.2022.08.129","article-title":"Validation study of machine-learning chest radiograph software in primary and emergency medicine","volume":"78","author":"Ahn","year":"2023","journal-title":"Clin. Radiol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1016\/j.crad.2004.07.012","article-title":"Accuracy of radiographer plain radiograph reporting in clinical practice: A meta-analysis","volume":"60","author":"Brealey","year":"2005","journal-title":"Clin. Radiol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"864","DOI":"10.1111\/imj.13946","article-title":"Low accuracy and confidence in chest radiograph interpretation amongst junior doctors and medical students","volume":"48","author":"Cheung","year":"2018","journal-title":"Intern. Med. J."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"460","DOI":"10.1111\/j.1525-1497.2006.00427.x","article-title":"Competency in chest radiography. A comparison of medical students, residents, and fellows","volume":"21","author":"Eisen","year":"2006","journal-title":"J. Gen. Intern. Med."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"890","DOI":"10.1136\/thorax-2022-219210","article-title":"Impact of radiographer immediate reporting of X-rays of the chest from general practice on the lung cancer pathway (radioX): A randomised controlled trial","volume":"78","author":"Woznitza","year":"2023","journal-title":"Thorax"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"507.e1","DOI":"10.1016\/j.crad.2017.11.016","article-title":"Immediate reporting of chest X-rays referred from general practice by reporting radiographers: A single centre feasibility study","volume":"73","author":"Woznitza","year":"2018","journal-title":"Clin. Radiol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"e70465","DOI":"10.1002\/hsr2.70465","article-title":"Exploring Radiographers\u2019 Readiness for Artificial Intelligence in Kuwait: Insights and Applications","volume":"8","author":"Khalaf","year":"2025","journal-title":"Health Sci. Rep."}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/15\/20\/2639\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,20]],"date-time":"2025-10-20T09:38:56Z","timestamp":1760953136000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/15\/20\/2639"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,19]]},"references-count":29,"journal-issue":{"issue":"20","published-online":{"date-parts":[[2025,10]]}},"alternative-id":["diagnostics15202639"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics15202639","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,19]]}}}