Deep learning improves physician accuracy in the comprehensive detection of abnormalities on chest X-rays
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...
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Nature Portfolio
2024-10-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-76608-2 |
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| author | Pamela G. Anderson Hannah Tarder-Stoll Mehmet Alpaslan Nora Keathley David L. Levin Srivas Venkatesh Elliot Bartel Serge Sicular Scott Howell Robert V. Lindsey Rebecca M. Jones |
| author_facet | Pamela G. Anderson Hannah Tarder-Stoll Mehmet Alpaslan Nora Keathley David L. Levin Srivas Venkatesh Elliot Bartel Serge Sicular Scott Howell Robert V. Lindsey Rebecca M. Jones |
| author_sort | Pamela G. Anderson |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-8313e4581a2c4f59827727ee167903f3 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-8313e4581a2c4f59827727ee167903f32025-02-09T12:38:01ZengNature PortfolioScientific Reports2045-23222024-10-0114111310.1038/s41598-024-76608-2Deep learning improves physician accuracy in the comprehensive detection of abnormalities on chest X-raysPamela G. Anderson0Hannah Tarder-Stoll1Mehmet Alpaslan2Nora Keathley3David L. Levin4Srivas Venkatesh5Elliot Bartel6Serge Sicular7Scott Howell8Robert V. Lindsey9Rebecca M. Jones10Imagen TechnologiesImagen TechnologiesImagen TechnologiesImagen TechnologiesDepartment of Radiology, Stanford University School of MedicineImagen TechnologiesImagen TechnologiesImagen TechnologiesImagen TechnologiesImagen TechnologiesImagen TechnologiesAbstract 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.https://doi.org/10.1038/s41598-024-76608-2 |
| spellingShingle | Pamela G. Anderson Hannah Tarder-Stoll Mehmet Alpaslan Nora Keathley David L. Levin Srivas Venkatesh Elliot Bartel Serge Sicular Scott Howell Robert V. Lindsey Rebecca M. Jones Deep learning improves physician accuracy in the comprehensive detection of abnormalities on chest X-rays Scientific Reports |
| title | Deep learning improves physician accuracy in the comprehensive detection of abnormalities on chest X-rays |
| title_full | Deep learning improves physician accuracy in the comprehensive detection of abnormalities on chest X-rays |
| title_fullStr | Deep learning improves physician accuracy in the comprehensive detection of abnormalities on chest X-rays |
| title_full_unstemmed | Deep learning improves physician accuracy in the comprehensive detection of abnormalities on chest X-rays |
| title_short | Deep learning improves physician accuracy in the comprehensive detection of abnormalities on chest X-rays |
| title_sort | deep learning improves physician accuracy in the comprehensive detection of abnormalities on chest x rays |
| url | https://doi.org/10.1038/s41598-024-76608-2 |
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