AI-assisted Analysis to Facilitate Detection of Humeral Lesions on Chest Radiographs.

"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence . This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content . Purpose To develop an artificial intelligence (AI) system for humeral tumor detection on chest radiographs (CRs) and evaluate the impact on reader performance. Materials and Methods In this retrospective study, 14,709 CRs (January 2000-December 2021) were collected from 13,468 patients, including CT-proven normal ( n = 13,116) and humeral tumor ( n = 1,593) cases. The data were divided into training and test groups. A novel training method called false-positive activation area reduction (FPAR) was introduced to enhance the diagnostic performance by focusing on the humeral region. The AI program and ten radiologists were assessed using holdout test set 1, wherein the radiologists were tested twice (with and without AI test results). The performance of the AI system was evaluated using holdout test set 2, comprising 10,497 normal images. Receiver operating characteristic (ROC) analyses were conducted for evaluating model performance. Results FPAR application in the AI program improved its performance compared with a conventional model based on the area under the receiver operating characteristic curve (0.87 versus 0.82, P = 0.04). The proposed AI system also demonstrated improved tumor localization accuracy (80% versus 57%, P < .001). In holdout test set 2, the proposed AI system exhibited a false positive rate of 2%. AI assistance improved the radiologists' sensitivity, specificity, and accuracy by 8.9%, 1.2%, and 3.5%, respectively ( P < .05 for all). Conclusion The proposed AI tool incorporating FPAR improved humeral tumor detection on CRs and reduced false positives in tumor visualization. It may serve as a supportive diagnostic tool to alert radiologists about humeral abnormalities. ©RSNA, 2024.