- 著者
- Tomofumi Nakamura Takeshi Aiba Wataru Shimizu Tetsushi Furukawa Tetsuo Sasano
- 出版者
- The Japanese Circulation Society
- 雑誌
- Circulation Journal (ISSN:13469843)
- 巻号頁・発行日
- pp.CJ-22-0496, (Released:2022-11-12)
- 参考文献数
- 23
- 被引用文献数
- 8
Background: Brugada syndrome is a potential cause of sudden cardiac death (SCD) and is characterized by a distinct ECG, but not all patients with A Brugada ECG develop SCD. In this study we sought to examine if an artificial intelligence (AI) model can predict a previous or future ventricular fibrillation (VF) episode from a Brugada ECG.Methods and Results: We developed an AI-enabled algorithm using a convolutional neural network. From 157 patients with suspected Brugada syndrome, 2,053 ECGs were obtained, and the dataset was divided into 5 datasets for cross-validation. In the ECG-based evaluation, the precision, recall, and F1score were 0.79±0.09, 0.73±0.09, and 0.75±0.09, respectively. The average area under the receiver-operating characteristic curve (AUROC) was 0.81±0.09. On per-patient evaluation, the AUROC was 0.80±0.07. This model predicted the presence of VF with a precision of 0.93±0.02, recall of 0.77±0.14, and F1score of 0.81±0.11. The negative predictive value was 0.94±0.11 while its positive predictive value was 0.44±0.29.Conclusions: This proof-of-concept study showed that an AI-enabled algorithm can predict the presence of VF with a substantial performance. It implies that the AI model may detect a subtle ECG change that is undetectable by humans.
- 著者
- Ryoichi Miyazaki Keita Watanabe Masakazu Kaneko Sho Nagamine Nobuhiro Hara Tomofumi Nakamura Yasutoshi Nagata Toshihiro Nozato Takashi Ashikaga
- 出版者
- The Japanese Society of Internal Medicine
- 雑誌
- Internal Medicine (ISSN:09182918)
- 巻号頁・発行日
- pp.5502-20, (Released:2020-12-07)
- 参考文献数
- 11
- 被引用文献数
- 2
An 80-year-old woman with acute posterolateralmyocardial infarction, cardiogenic shock, and acute heart failure was admitted to our hospital. Transthoracic echocardiography (TTE) showed dysfunction of the left ventricular inferolateral wall motion and severe mitral valve regurgitation (MR). Emergency coronary angiography revealed triple-vessel stenosis. We performed transesophageal echocardiography in the catheter room to diagnose the cause of MR. Severe tenting of the mitral valve and no rupture of the papillary muscles were revealed. We considered ischemic MR likely to improve with revascularization and performed percutaneous coronary intervention. Subsequently, the patient's circulatory dynamics rapidly stabilized, and MR was significantly improved on follow-up TTE.