Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Harvard Institutes of Medicine, 77 Avenue Louis Pasteur, Boston, MA, 02115, USA.
Department of Radiation Oncology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, 75 Francis Street and 450 Brookline Avenue, Boston, MA, 02115, USA.
Nat Commun. 2023 May 16;14(1):2797. doi: 10.1038/s41467-023-37758-5.
Prevention and management of chronic lung diseases (asthma, lung cancer, etc.) are of great importance. While tests are available for reliable diagnosis, accurate identification of those who will develop severe morbidity/mortality is currently limited. Here, we developed a deep learning model, CXR Lung-Risk, to predict the risk of lung disease mortality from a chest x-ray. The model was trained using 147,497 x-ray images of 40,643 individuals and tested in three independent cohorts comprising 15,976 individuals. We found that CXR Lung-Risk showed a graded association with lung disease mortality after adjustment for risk factors, including age, smoking, and radiologic findings (Hazard ratios up to 11.86 [8.64-16.27]; p < 0.001). Adding CXR Lung-Risk to a multivariable model improved estimates of lung disease mortality in all cohorts. Our results demonstrate that deep learning can identify individuals at risk of lung disease mortality on easily obtainable x-rays, which may improve personalized prevention and treatment strategies.
预防和管理慢性肺部疾病（如哮喘、肺癌等）非常重要。虽然有可靠的诊断测试，但目前能够准确识别出那些会出现严重发病率/死亡率的人还很有限。在这里，我们开发了一种深度学习模型 CXR Lung-Risk，用于从胸部 X 光片中预测肺部疾病死亡率的风险。该模型使用了 40643 名个体的 147497 张 X 光图像进行训练，并在三个独立的队列中进行了测试，这些队列包含 15976 名个体。我们发现，在调整了年龄、吸烟和影像学发现等危险因素后，CXR Lung-Risk 与肺部疾病死亡率呈梯度相关（风险比高达 11.86[8.64-16.27]；p<0.001）。在所有队列中，将 CXR Lung-Risk 添加到多变量模型中可以提高对肺部疾病死亡率的估计。我们的研究结果表明，深度学习可以从易于获得的 X 光片中识别出有肺部疾病死亡率风险的个体，这可能会改善个性化的预防和治疗策略。
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