1. Deep learning improves physician accuracy in the comprehensive detection of abnormalities on chest X-rays.
- Author
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Anderson, Pamela G., Tarder-Stoll, Hannah, Alpaslan, Mehmet, Keathley, Nora, Levin, David L., Venkatesh, Srivas, Bartel, Elliot, Sicular, Serge, Howell, Scott, Lindsey, Robert V., and Jones, Rebecca M.
- Subjects
MACHINE learning ,ARTIFICIAL intelligence ,PHYSICIANS ,X-rays ,RADIOLOGISTS ,CHEST X rays ,DEEP learning - Abstract
Chest X-rays are the most commonly performed medical imaging exam, yet they are often misinterpreted by physicians. Here, we present an FDA-cleared, artificial intelligence (AI) system which uses a deep learning algorithm to assist physicians in the comprehensive detection and localization of abnormalities on chest X-rays. We trained and tested the AI system on a large dataset, assessed generalizability on publicly available data, and evaluated radiologist and non-radiologist physician accuracy when unaided and aided by the AI system. The AI system accurately detected chest X-ray abnormalities (AUC: 0.976, 95% bootstrap CI: 0.975, 0.976) and generalized to a publicly available dataset (AUC: 0.975, 95% bootstrap CI: 0.971, 0.978). Physicians showed significant improvements in detecting abnormalities on chest X-rays when aided by the AI system compared to when unaided (difference in AUC: 0.101, p < 0.001). Non-radiologist physicians detected abnormalities on chest X-ray exams as accurately as radiologists when aided by the AI system and were faster at evaluating chest X-rays when aided compared to unaided. Together, these results show that the AI system is accurate and reduces physician errors in chest X-ray evaluation, which highlights the potential of AI systems to improve access to fast, high-quality radiograph interpretation. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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