- 著者
- Susumu Katsushika Satoshi Kodera Mitsuhiko Nakamoto Kota Ninomiya Nobutaka Kakuda Hiroki Shinohara Ryo Matsuoka Hirotaka Ieki Masae Uehara Yasutomi Higashikuni Koki Nakanishi Tomoko Nakao Norifumi Takeda Katsuhito Fujiu Masao Daimon Jiro Ando Hiroshi Akazawa Hiroyuki Morita Issei Komuro
- 出版者
- The Japanese Circulation Society
- 雑誌
- Circulation Journal (ISSN:13469843)
- 巻号頁・発行日
- vol.86, no.1, pp.87-95, 2021-12-24 (Released:2021-12-24)
- 参考文献数
- 27
- 被引用文献数
- 2 16
Background:Because the early diagnosis of subclinical cardiac sarcoidosis (CS) remains difficult, we developed a deep learning algorithm to distinguish CS patients from healthy subjects using echocardiographic movies.Methods and Results:Among the patients who underwent echocardiography from January 2015 to December 2019, we chose 151 echocardiographic movies from 50 CS patients and 151 from 149 healthy subjects. We trained two 3D convolutional neural networks (3D-CNN) to identify CS patients using a dataset of 212 echocardiographic movies with and without a transfer learning method (Pretrained algorithm and Non-pretrained algorithm). On an independent set of 41 echocardiographic movies, the area under the receiver-operating characteristic curve (AUC) of the Pretrained algorithm was greater than that of Non-pretrained algorithm (0.842, 95% confidence interval (CI): 0.722–0.962 vs. 0.724, 95% CI: 0.566–0.882, P=0.253). The AUC from the interpretation of the same set of 41 echocardiographic movies by 5 cardiologists was not significantly different from that of the Pretrained algorithm (0.855, 95% CI: 0.735–0.975 vs. 0.842, 95% CI: 0.722–0.962, P=0.885). A sensitivity map demonstrated that the Pretrained algorithm focused on the area of the mitral valve.Conclusions:A 3D-CNN with a transfer learning method may be a promising tool for detecting CS using an echocardiographic movie.
- 著者
- Takuya Matsumoto Satoshi Kodera Hiroki Shinohara Hirotaka Ieki Toshihiro Yamaguchi Yasutomi Higashikuni Arihiro Kiyosue Kaoru Ito Jiro Ando Eiki Takimoto Hiroshi Akazawa Hiroyuki Morita Issei Komuro
- 出版者
- International Heart Journal Association
- 雑誌
- International Heart Journal (ISSN:13492365)
- 巻号頁・発行日
- vol.61, no.4, pp.781-786, 2020-07-30 (Released:2020-07-30)
- 参考文献数
- 13
- 被引用文献数
- 26
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.
- 著者
- Susumu Katsushika Satoshi Kodera Mitsuhiko Nakamoto Kota Ninomiya Nobutaka Kakuda Hiroki Shinohara Ryo Matsuoka Hirotaka Ieki Masae Uehara Yasutomi Higashikuni Koki Nakanishi Tomoko Nakao Norifumi Takeda Katsuhito Fujiu Masao Daimon Jiro Ando Hiroshi Akazawa Hiroyuki Morita Issei Komuro
- 出版者
- The Japanese Circulation Society
- 雑誌
- Circulation Journal (ISSN:13469843)
- 巻号頁・発行日
- pp.CJ-21-0265, (Released:2021-06-26)
- 参考文献数
- 27
- 被引用文献数
- 16
Background:Because the early diagnosis of subclinical cardiac sarcoidosis (CS) remains difficult, we developed a deep learning algorithm to distinguish CS patients from healthy subjects using echocardiographic movies.Methods and Results:Among the patients who underwent echocardiography from January 2015 to December 2019, we chose 151 echocardiographic movies from 50 CS patients and 151 from 149 healthy subjects. We trained two 3D convolutional neural networks (3D-CNN) to identify CS patients using a dataset of 212 echocardiographic movies with and without a transfer learning method (Pretrained algorithm and Non-pretrained algorithm). On an independent set of 41 echocardiographic movies, the area under the receiver-operating characteristic curve (AUC) of the Pretrained algorithm was greater than that of Non-pretrained algorithm (0.842, 95% confidence interval (CI): 0.722–0.962 vs. 0.724, 95% CI: 0.566–0.882, P=0.253). The AUC from the interpretation of the same set of 41 echocardiographic movies by 5 cardiologists was not significantly different from that of the Pretrained algorithm (0.855, 95% CI: 0.735–0.975 vs. 0.842, 95% CI: 0.722–0.962, P=0.885). A sensitivity map demonstrated that the Pretrained algorithm focused on the area of the mitral valve.Conclusions:A 3D-CNN with a transfer learning method may be a promising tool for detecting CS using an echocardiographic movie.
- 著者
- Masataka Sato Satoshi Kodera Naoto Setoguchi Kengo Tanabe Shunichi Kushida Junji Kanda Mike Saji Mamoru Nanasato Hisataka Maki Hideo Fujita Nahoko Kato Hiroyuki Watanabe Minami Suzuki Masao Takahashi Naoko Sawada Masao Yamasaki Shinnosuke Sawano Susumu Katsushika Hiroki Shinohara Norifumi Takeda Katsuhito Fujiu Masao Daimon Hiroshi Akazawa Hiroyuki Morita Issei Komuro
- 出版者
- The Japanese Circulation Society
- 雑誌
- Circulation Journal (ISSN:13469843)
- 巻号頁・発行日
- pp.CJ-23-0216, (Released:2023-11-14)
- 参考文献数
- 41
- 被引用文献数
- 2
Background: Left heart abnormalities are risk factors for heart failure. However, echocardiography is not always available. Electrocardiograms (ECGs), which are now available from wearable devices, have the potential to detect these abnormalities. Nevertheless, whether a model can detect left heart abnormalities from single Lead I ECG data remains unclear.Methods and Results: We developed Lead I ECG models to detect low ejection fraction (EF), wall motion abnormality, left ventricular hypertrophy (LVH), left ventricular dilatation, and left atrial dilatation. We used a dataset comprising 229,439 paired sets of ECG and echocardiography data from 8 facilities, and validated the model using external verification with data from 2 facilities. The area under the receiver operating characteristic curves of our model was 0.913 for low EF, 0.832 for wall motion abnormality, 0.797 for LVH, 0.838 for left ventricular dilatation, and 0.802 for left atrial dilatation. In interpretation tests with 12 cardiologists, the accuracy of the model was 78.3% for low EF and 68.3% for LVH. Compared with cardiologists who read the 12-lead ECGs, the model’s performance was superior for LVH and similar for low EF.Conclusions: From a multicenter study dataset, we developed models to predict left heart abnormalities using Lead I on the ECG. The Lead I ECG models show superior or equivalent performance to cardiologists using 12-lead ECGs.