Matsumoto Takuya, Kodera Satoshi, Shinohara Hiroki, Ieki Hirotaka, Yamaguchi Toshihiro, Higashikuni Yasutomi, Kiyosue Arihiro, Ito Kaoru, Ando Jiro, Takimoto Eiki, Akazawa Hiroshi, Morita Hiroyuki, Komuro Issei
School of Medicine, Graduate School of Medicine, The University of Tokyo.
Department of Cardiovascular Medicine, Graduate School of Medicine, The University of Tokyo.
Int Heart J. 2020 Jul 30;61(4):781-786. doi: 10.1536/ihj.19-714. Epub 2020 Jul 18.
The development of deep learning technology has enabled machines to achieve high-level accuracy in interpreting medical images. While many previous studies have examined the detection of pulmonary nodules in chest X-rays using deep learning, the application of this technology to heart failure remains rare. In this paper, we investigated the performance of a deep learning algorithm in terms of diagnosing heart failure using images obtained from chest X-rays. We used 952 chest X-ray images from a labeled database published by the National Institutes of Health. Two cardiologists verified and relabeled a total of 260 "normal" and 378 "heart failure" images, with the remainder being discarded because they had been incorrectly labeled. Data augmentation and transfer learning were used to obtain an accuracy of 82% in diagnosing heart failure using the chest X-ray images. Furthermore, heatmap imaging allowed us to visualize decisions made by the machine. Deep learning can thus help support the diagnosis of heart failure using chest X-ray images.
深度学习技术的发展使机器在解读医学图像方面能够达到高水平的准确性。虽然之前有许多研究使用深度学习检测胸部X光片中的肺结节，但这项技术在心力衰竭方面的应用仍然很少。在本文中，我们研究了一种深度学习算法利用胸部X光图像诊断心力衰竭的性能。我们使用了美国国立卫生研究院发布的一个带标签数据库中的952张胸部X光图像。两位心脏病专家对总共260张“正常”图像和378张“心力衰竭”图像进行了验证和重新标注，其余图像因标注错误而被丢弃。通过数据增强和迁移学习，利用胸部X光图像诊断心力衰竭的准确率达到了82%。此外，热成像图使我们能够直观地看到机器做出的决策。因此，深度学习有助于利用胸部X光图像辅助心力衰竭的诊断。
