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Evaluating convolutional neural network-enhanced electrocardiography for hypertrophic cardiomyopathy detection in a specialized cardiovascular setting

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Abstract

The efficacy of convolutional neural network (CNN)-enhanced electrocardiography (ECG) in detecting hypertrophic cardiomyopathy (HCM) and dilated HCM (dHCM) remains uncertain in real-world applications. This retrospective study analyzed data from 19,170 patients (including 140 HCM or dHCM) in the Shinken Database (2010–2017). We evaluated the sensitivity, positive predictive rate (PPR), and F1 score of CNN-enhanced ECG in a ‘‘basic diagnosis’’ model (total disease label) and a ‘‘comprehensive diagnosis’’ model (including disease subtypes). Using all-lead ECG in the "basic diagnosis" model, we observed a sensitivity of 76%, PPR of 2.9%, and F1 score of 0.056. These metrics improved in cases with a diagnostic probability of ≥ 0.9 and left ventricular hypertrophy (LVH) on ECG: 100% sensitivity, 8.6% PPR, and 0.158 F1 score. The ‘‘comprehensive diagnosis’’ model further enhanced these figures to 100%, 13.0%, and 0.230, respectively. Performance was broadly consistent across CNN models using different lead configurations, particularly when including leads viewing the lateral walls. While the precision of CNN models in detecting HCM or dHCM in real-world settings is initially low, it improves by targeting specific patient groups and integrating disease subtype models. The use of ECGs with fewer leads, especially those involving the lateral walls, appears comparably effective.

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Data availability

Data cannot be shared publicly because of a lack of such a description in the study protocol and informed consent. Data are available from the Ethics Review Committee at the Cardiovascular Institute for researchers who meet the criteria for access to confidential data (contact via the corresponding author).

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Acknowledgements

We thank Shiro Ueda and Nobuko Ueda at Medical Edge Company, Ltd., for assembling the database by the Clinical Study Supporting System, and Yurika Hashiguchi, Hiroaki Arai, and Takashi Osada for data management and system administration.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Author information

Authors and Affiliations

  1. Department of Cardiovascular Medicine, The Cardiovascular Institute, 3-2-19 Nishiazabu, Minato-Ku, Tokyo, 106-0031, Japan

    Naomi Hirota, Shinya Suzuki, Takuto Arita, Naoharu Yagi, Mikio Kishi, Hiroaki Semba, Hiroto Kano, Shunsuke Matsuno, Yuko Kato, Takayuki Otsuka, Tokuhisa Uejima, Yuji Oikawa, Junji Yajima & Takeshi Yamashita

  2. Nihon Kohden Corporation, Tokyo, Japan

    Jun Motogi, Takuya Umemoto, Wataru Matsuzawa, Tsuneo Takayanagi, Akira Hyodo & Keiichi Satoh

  3. Information System Division, The Cardiovascular Institute, Tokyo, Japan

    Hiroshi Nakai

  4. Department of Cardiovascular Surgery, The Cardiovascular Institute, Tokyo, Japan

    Takayuki Hori, Minoru Matsuhama & Mitsuru Iida

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  1. Naomi Hirota
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  2. Shinya Suzuki
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Contributions

NH: Conceptualization, Validation, Writing—original draft. SS: Conceptualization, Validation, Writing—review and editing. JM: Conceptualization, Validation. TU: Data curation, Formal analysis, Writing—original draft, Writing—review and editing. HN: Conceptualization, Data curation, Validation. WM: Data curation, Formal analysis. TT: Data curation, Formal analysis. AH: Formal analysis, Validation. KS: Formal analysis, Validation. TA: Writing—review and editing. NY: Writing—review and editing. MK: Writing—review and editing. HS: Writing—review and editing. HK: Writing—review and editing. SM: Writing—review and editing. YK: Writing—review and editing. TO: Writing—review and editing. YO: Writing—review and editing. TH: Writing—review and editing. MM: Writing—review and editing. MI: Writing—review and editing. JY: Writing—review and editing. TY: Writing—review and editing. All authors take responsibility for all aspects of the reliability and freedom from bias of the data presented and their discussed interpretation.

Corresponding author

Correspondence to Naomi Hirota.

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Conflict of interests

Dr. Suzuki received lecture fees from Daiichi Sankyo and Bristol-Myers Squibb. Dr. Yamashita received research funds and/or lecture fees from Daiichi Sankyo, Bayer Yakuhin, Bristol-Myers Squibb, Pfizer, Nippon Boehringer Ingelheim, Eisai, Mitsubishi Tanabe Pharm, Ono Pharmaceutical, and Toa Eiyo. J Motogi, T Umemoto, W Matsuzawa, T Takayanagi, A Hyodo, and K Satoh are employees at Nihon Kohden Corporation.

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Hirota, N., Suzuki, S., Motogi, J. et al. Evaluating convolutional neural network-enhanced electrocardiography for hypertrophic cardiomyopathy detection in a specialized cardiovascular setting. Heart Vessels (2024). https://doi.org/10.1007/s00380-024-02367-9

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