Hypertrophic cardiomyopathy detection with artificial intelligence electrocardiography in international cohorts: an external validation study
Open accessAbstract
Aims: Recently, deep learning artificial intelligence (AI) models have been trained to detect cardiovascular conditions, including hypertrophic cardiomyopathy (HCM), from the 12-lead electrocardiogram (ECG). In this external validation study, we sought to assess the performance of an AI-ECG algorithm for detecting HCM in diverse international cohorts. Methods and results: A convolutional neural network-based AI-ECG algorithm was developed previously in a single-centre North American HCM cohort (Mayo Clinic). This algorithm was applied to the raw 12-lead ECG data of patients with HCM and non-HCM controls from three external cohorts (Bern, Switzerland; Oxford, UK; and Seoul, South Korea). The algorithm’s ability to distinguish HCM vs. non-HCM status from the ECG alone was examined. A total of 773 patients with HCM and 3867 non-HCM controls were included across three sites in the merged external validation cohort. The HCM study sample comprised 54.6% East Asian, 43.2% White, and 2.2% Black patients. Median AI-ECG probabilities of HCM were 85% for patients with HCM and 0.3% for controls (P < 0.001). Overall, the AI-ECG algorithm had an area under the receiver operating characteristic curve (AUC) of 0.922 [95% confidence interval (CI) 0.910–0.934], with diagnostic accuracy 86.9%, sensitivity 82.8%, and specificity 87.7% for HCM detection. In age- and sex-matched analysis (case–control ratio 1:2), the AUC was 0.921 (95% CI 0.909–0.934) with accuracy 88.5%, sensitivity 82.8%, and specificity 90.4%. Conclusion: The AI-ECG algorithm determined HCM status from the 12-lead ECG with high accuracy in diverse international cohorts, providing evidence for external validity. The value of this algorithm in improving HCM detection in clinical practice and screening settings requires prospective evaluation.
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Hypertrophic cardiomyopathy, cardiac amyloidosis, and pulmonary hypertension AI-ECG applications remain earlier or pathway-incomplete.
“hypertrophic cardiomyopathy, cardiac amyloidosis, and pulmonary hypertension remain earlier or pathway-incomplete applications.”
From AI-ECG for Echocardiography Triage in Structural Heart Disease: Evidence, Implementation, and Future Directions (2026) · cites this paper as [28]
The Discussion (page unavailable) says the utility of AI-ECG screening for HCM in asymptomatic people is unknown and that proposed screening groups require further study, supporting the pathway-incomplete characterization for HCM. The paper does not address AI-ECG applications for cardiac amyloidosis or pulmonary hypertension, so it cannot support that part of the claim. (LLM, conf=0.88)
Experimental — accuracy not yet measuredHCM and cardiac amyloidosis should be regarded as clinically important but pathway-incomplete domains rather than deployable screening indications comparable to low-LVEF AI-ECG.
“Taken together, HCM and CA should therefore be regarded as clinically important but pathway-incomplete domains rather than deployable screening indications comparable to low-LVEF AI-ECG.26–30,43”
From AI-ECG for Echocardiography Triage in Structural Heart Disease: Evidence, Implementation, and Future Directions (2026) · cites this paper as [28]
The Discussion says HCM is clinically important, but the utility of screening asymptomatic people is unknown; it also raises concerns about false positives and says proposed screening groups require further study. That supports the HCM portion of the citing sentence. This paper does not address cardiac amyloidosis or compare HCM screening readiness with low-LVEF AI-ECG. Page numbers are unavailable in our copy. (LLM, conf=0.86)
Experimental — accuracy not yet measuredHCM has shown consistently high discrimination, international external validation, and early real-world implementation, making it one of the more reproducible structural AI-ECG targets.
“HCM has shown consistently high discrimination, international external validation, and early real-world implementation, making it one of the more reproducible structural AI-ECG targets.”
From Deep Learning Applied to 12-Lead ECGs for Detection of Structural, Metabolic and Systemic Disease (2026) · cites this paper as [27]
The Methods and results report high discrimination (AUC 0.922) across external cohorts in Switzerland, the UK, and South Korea; the Discussion also describes favourable performance across diverse cohorts. But the Conclusion says clinical implementation still needs prospective evaluation. An earlier internal validation in routine clinical practice is mentioned, not evidence that this algorithm had been implemented in real-world care. Page numbers are unavailable in our copy. (LLM, conf=0.88)
Experimental — accuracy not yet measuredAI-ECG for HCM detection had an AUC of 0.922 across cohorts from Bern, Oxford, and Seoul.
“Subsequent international external validation further supported the transportability of AI-ECG for HCM detection, with Siontis et al. reporting an AUC of 0.922 across cohorts from Bern, Oxford, and Seoul [27].”
From Deep Learning Applied to 12-Lead ECGs for Detection of Structural, Metabolic and Systemic Disease (2026) · cites this paper as [27]
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Cites
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Desai, M. Y., Jadam, S., Abusafia, M., Rutkowski, K., Ospina, S., Gaballa, A., Sultana, S., Thamilarasan, M., Xu, B., & Popovic, Z. B. (2025) · JACC: Clinical Electrophysiology · doi:10.1016/j.jacep.2025.02.024 · Cited by 19 · see citing works
Tang, Q., Deng, K., Cui, Y., Liu, H., & Qian, B. (2026) · International Journal of General Medicine · doi:10.2147/ijgm.s610298
“28 More recently, Sangha et al reported in a peer-reviewed study that a deep-learning model operating on ECG images—not raw waveform files—detected HCM with AUROCs of 0.95 in the internal test set and 0.94, 0.92, and 0.91 across three external datasets, supporting a potentially more deployable…”
InfluentialDeep Learning Applied to 12-Lead ECGs for Detection of Structural, Metabolic and Systemic Disease
Shaulian, S., Zeltser, R., & Makaryus, A. N. (2026) · Diagnostics · doi:10.3390/diagnostics16172834
- Authors
- Siontis, K. C., Wieczorek, M. A., Maanja, M., Hodge, D. O., Kim, H.-K., Lee, H.-J., Lee, H., Lim, J., Park, C. S., Ariga, R., Raman, B., Mahmod, M., Watkins, H., Neubauer, S., Windecker, S., Siontis, G. C. M., Gersh, B. J., Ackerman, M. J., Attia, Z. I., Friedman, P. A., & Noseworthy, P. A.
- Affiliations
- Mayo Clinic (US)
- Seoul National University (KR)
- John Radcliffe Hospital (GB)
- University of Bern (CH)
- Corresponding author
- Siontis, Konstantinos C
- ORCID
- Siontis, Konstantinos C
- Wieczorek, Mikolaj A
- Maanja, Maren
- Hodge, David O
- Kim, Hyung-Kwan
- Lee, Hyun-Jung
- Lee, Heesun
- Lim, Jaehyun
- Park, Chan Soon
- Raman, Betty
- Mahmod, Masliza
- Watkins, Hugh
- Neubauer, Stefan
- Windecker, Stephan
- Siontis, George C M
- Gersh, Bernard J
- Ackerman, Michael J
- Attia, Zachi I
- Friedman, Paul A
- Noseworthy, Peter A
- Venue
- European Heart Journal - Digital Health
- Publisher
- Oxford University Press (OUP)
- Published
- 2024-04-15
- Volume
- 5
- Issue
- 4
- Pages
- 416-426
- ISSN
- 2634-3916 (ISSN-L)
- PMC
- PMC11284003
- OpenAlex
- W4394855089
- Cited by
- 44
- References
- 2
- Citations by year
- 2026: 24 · 2025: 18 · 2024: 2
- FWCI
- 13.24
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- European Heart Journal - Digital Health (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Bern Open Repository and Information System (University of Bern) (submittedVersion, cc-by-nc)
- Open Access CRIS of the University of Bern
- Funders
- Mayo Clinic
- Topics
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiomyopathy and Myosin Studies
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- hypertrophic cardiomyopathy, artificial intelligence, electrocardiogram, deep learning, convolutional neural networks, 12-lead ECG, external validation, diagnostic accuracy, cardiovascular screening, international cohort
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