Development of Clinically Validated Artificial Intelligence Model for Detecting ST-segment Elevation Myocardial Infarction - 18/10/24
, Seung-Jun Lee, MD a, Sung-Jin Hong, MD a, Chul-Min Ahn, MD a, Jung-Sun Kim, MD a, Byeong-Keuk Kim, MD a, Young-Guk Ko, MD a, Donghoon Choi, MD a, Myeong-Ki Hong, MD aAbstract |
Study objective |
Although the importance of primary percutaneous coronary intervention has been emphasized for ST-segment elevation myocardial infarction (STEMI), the appropriateness of the cardiac catheterization laboratory activation remains suboptimal. This study aimed to develop a precise artificial intelligence (AI) model for the diagnosis of STEMI and accurate cardiac catheterization laboratory activation.
Methods |
We used electrocardiography (ECG) waveform data from a prospective percutaneous coronary intervention registry in Korea in this study. Two independent board-certified cardiologists established a criterion standard (STEMI or Not STEMI) for each ECG based on corresponding coronary angiography data. We developed a deep ensemble model by combining 5 convolutional neural networks. In addition, we performed clinical validation based on a symptom-based ECG data set, comparisons with clinical physicians, and external validation.
Results |
We used 18,697 ECGs for the model development data set, and 1,745 (9.3%) were STEMI. The AI model achieved an accuracy of 92.1%, sensitivity of 95.4%, and specificity of 91.8 %. The performances of the AI model were well balanced and outstanding in the clinical validation, comparison with clinical physicians, and the external validation.
Conclusion |
The deep ensemble AI model showed a well-balanced and outstanding performance. As visualized with gradient-weighted class activation mapping, the AI model has a reasonable explainability. Further studies with prospective validation regarding clinical benefit in a real-world setting should be warranted.
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| Supervising editor: David L. Schriger, MD, MPH. Specific detailed information about possible conflict of interest for individual editors is available at editors. |
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| Author contributions: SCY and Y-GK conceptualized and supervised the overall study protocol. KLJ conducted data acquisition, processing, and machine learning modeling. S-HL and Y-JL confirmed the clinical accuracy of the data. S-HL and KLJ wrote the first draft of the manuscript. Y-JL, SCY, and Y-GK revised the manuscript further. All authors critically revised the draft for important intellectual content, had full access to all data in the study, and shared final responsibility for the decision to submit the manuscript for publication. S-HL, KLJ, and Y-JL contributed equally to this work. SCY takes responsibility for the article as a whole. |
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| Data sharing statement: The deidentified data, data dictionary, and analytic code for this study are available on reasonable request from the date of publication by contacting the corresponding author at chandryou@yuhs.ac. |
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| Authorship: All authors attest to meeting the four ICMJE.org authorship criteria: (1) Substantial contributions to the conception or design of the work; or the acquisition, analysis, or interpretation of data for the work; AND (2) Drafting the work or revising it critically for important intellectual content; AND (3) Final approval of the version to be published; AND (4) Agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. |
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| Funding and support: By Annals’ policy, all authors are required to disclose any and all commercial, financial, and other relationships in any way related to the subject of this article as per ICMJE conflict of interest guidelines (see www.icmje.org/). This research was supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number: HI22C0452). SCY reports being a chief technology officer of PHI Digital Healthcare. The remaining authors have no conflict of interest relevant to this article to disclose. |
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| Please see page 541 for the Editor’s Capsule Summary of this article. |
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Vol 84 - N° 5
P. 540-548 - novembre 2024 Retour au numéro
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