- 著者
- Katsushige Ono Yu-ki Iwasaki Masaharu Akao Takanori Ikeda Kuniaki Ishii Yasuya Inden Kengo Kusano Yoshinori Kobayashi Yukihiro Koretsune Tetsuo Sasano Naokata Sumitomo Naohiko Takahashi Shinichi Niwano Nobuhisa Hagiwara Ichiro Hisatome Tetsushi Furukawa Haruo Honjo Toru Maruyama Yuji Murakawa Masahiro Yasaka Eiichi Watanabe Takeshi Aiba Mari Amino Hideki Itoh Hisashi Ogawa Yasuo Okumura Chizuko Aoki-Kamiya Jun Kishihara Eitaro Kodani Takashi Komatsu Yusuke Sakamoto Kazuhiro Satomi Tsuyoshi Shiga Tetsuji Shinohara Atsushi Suzuki Shinya Suzuki Yukio Sekiguchi Satoshi Nagase Noriyuki Hayami Masahide Harada Tadashi Fujino Takeru Makiyama Mitsunori Maruyama Junichiro Miake Shota Muraji Hiroshige Murata Norishige Morita Hisashi Yokoshiki Koichiro Yoshioka Kenji Yodogawa Hiroshi Inoue Ken Okumura Takeshi Kimura Hiroyuki Tsutsui Wataru Shimizu on behalf of the Japanese Circulation Society and Japanese Heart Rhythm Society Joint Working Group
- 出版者
- The Japanese Circulation Society
- 雑誌
- Circulation Journal (ISSN:13469843)
- 巻号頁・発行日
- pp.CJ-20-1212, (Released:2022-03-11)
- 参考文献数
- 998
- 被引用文献数
- 41
- 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
- Yu Natsume Kasumi Oaku Kentaro Takahashi Wakana Nakamura Ai Oono Satomi Hamada Masahiro Yamazoe Kensuke Ihara Takeshi Sasaki Masahiko Goya Kenzo Hirao 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-17-1194, (Released:2018-02-05)
- 参考文献数
- 44
- 被引用文献数
- 33
Background:Recent experimental studies have demonstrated that several microRNAs (miRNAs) expressed in atrial tissue promote a substrate of atrial fibrillation (AF). However, because it has not been fully elucidated whether these experimental data contribute to identifying circulating miRNAs as biomarkers for AF, we used a combined analysis of human serum and murine atrial samples with the aim of identifying these biomarkers for predicting AF.Methods and Results:Comprehensive analyses were performed to screen 733 miRNAs in serum from 10 AF patients and 5 controls, and 672 miRNAs in atrial tissue from 6 inducible atrial tachycardia model mice and 3 controls. We selected miRNAs for which expression was detected in both analyses, and their expression levels were changed in the human analyses, the murine analyses, or both. This screening identified 11 candidate miRNAs. Next, we quantified the selected miRNAs using a quantitative RT-PCR in 50 AF and 50 non-AF subjects. The individual assessment revealed that 4 miRNAs (miR-99a-5p, miR-192-5p, miR-214-3p, and miR-342-5p) were significantly upregulated in AF patients. A receiver-operating characteristics curve indicated that miR-214-3p and miR-342-5p had the highest accuracy. The combination of the 4 miRNAs modestly improved the predictive accuracy for AF (76% sensitivity, 80% specificity).Conclusions:Novel circulating miRNAs were upregulated in the serum of AF patients and might be potential biomarkers of AF.