Deep learning-based age estimation from chest X-rays indicates cardiovascular prognosis

作者：Hirotaka Ieki, Kaoru Ito, Mike Saji, Rei Kawakami, Yuji Nagatomo, Kaori Takada, Toshiya Kariyasu, Haruhiko Machida, Satoshi Koyama, Hiroki Yoshida, Ryo Kurosawa, Hiroshi Matsunaga, Kazuo Miyazawa, Kouichi Ozaki, Yoshihiro Onouchi, Susumu Katsushika, Ryo Matsuoka, Hiroki Shinohara, Toshihiro Yamaguchi, Satoshi Kodera, Yasutomi Higashikuni, Katsuhito Fujiu, Hiroshi Akazawa, Nobuo Iguchi, Mitsuaki Isobe, Tsutomu Yoshikawa, Issei Komuro · 发表于：Communications Medicine · 年份：2022 · DOI：10.1038/s43856-022-00220-6 · 被引用次数：49 · 研究领域：Artificial Intelligence in Healthcare and Education、Cardiovascular Function and Risk Factors、Machine Learning in Healthcare

BACKGROUND: In recent years, there has been considerable research on the use of artificial intelligence to estimate age and disease status from medical images. However, age estimation from chest X-ray (CXR) images has not been well studied and the clinical significance of estimated age has not been fully determined. METHODS: To address this, we trained a deep neural network (DNN) model using more than 100,000 CXRs to estimate the patients' age solely from CXRs. We applied our DNN to CXRs of 1562 consecutive hospitalized heart failure patients, and 3586 patients admitted to the intensive care unit with cardiovascular disease. RESULTS: The DNN's estimated age (X-ray age) showed a strong significant correlation with chronological age on the hold-out test data and independent test data. Elevated X-ray age is associated with worse clinical outcomes (heart failure readmission and all-cause death) for heart failure. Additionally, elevated X-ray age was associated with a worse prognosis in 3586 patients admitted to the intensive care unit with cardiovascular disease. CONCLUSIONS: Our results suggest that X-ray age can serve as a useful indicator of cardiovascular abnormalities, which will help clinicians to predict, prevent and manage cardiovascular diseases.