# Artificial Intelligence Applications in Hypertrophic Cardiomyopathy: turns and loopholes

Giorgia Panichella, Manuel Garofalo, Laura Sasso, Alessandra Milazzo, Alessandra Fornaro, Josè-Manuel Pioner, Alfonso Bueno-Orovio, Mark van Gils, Annariina Koivu, Luca Mainardi, et al.

► To cite this version:

Giorgia Panichella, Manuel Garofalo, Laura Sasso, Alessandra Milazzo, Alessandra Fornaro, et al. Artificial Intelligence Applications in Hypertrophic Cardiomyopathy: turns and loopholes. European Heart Journal - Digital Health, 2025, 6 (5), pp.853-867. 10.1093/ehjdh/ztaf086. hal-05239352

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<figure><img src="image_1.png" /></figure>
# Artificial intelligence applications in hypertrophic cardiomyopathy: turns and loopholes

Giorgia Panichella ID <fnref n="1" />,²,*, Manuel Garofalo<fnref n="1" />,², Laura Sasso²,
Alessandra Milazzo ID <fnref n="1" />,², Alessandra Fornaro², Josè Manuel Pioner ID ³,
Alfonso Bueno-Orovio ID ⁴, Mark van Gils⁵, Annariina Koivu⁵, Luca Mainardi⁶,
Virginie Le Rolle ID ⁷, Felix Agakov⁸, Maurizio Pieroni ID <fnref n="1" />,², Katriina Aalto-Setälä⁵,⁹,
Jari Hyttinen⁵,⁹, Iacopo Olivotto ID <fnref n="1" />,²,¹⁰, and Annamaria Del Franco²

Received 25 February 2025; revised 28 May 2025; accepted 25 June 2025; online publish-ahead-of-print 25 July 2025

Hypertrophic cardiomyopathy (HCM) is a heterogeneous disease where, despite recent advances, accurate diagnosis, risk stratification, and personalized treatment remain challenging. Artificial intelligence (AI) offers a transformative approach to HCM by enabling rapid, precise analysis of complex data. This article reviews the current and potential applications of AI in HCM. AI enhances diagnostic accuracy by analysing electrocardiograms, echocardiography, and cardiac magnetic resonance images, differentiating HCM from other forms of left ventricular hypertrophy, identifying subtle phenotypic variations, and standardizing myocardial fibrosis assessment. Multimodal AI-driven approaches improve risk stratification, therapeutic decision-making, and monitoring of both established and novel therapies, such as cardiac myosin inhibitors. Emerging AI-driven in silico trials and digital twin platforms highlight the potential of combining data-driven and knowledge-based AI with biophysical models to simulate patient-specific disease trajectories, supporting preclinical evaluation and personalized care. As a multidisciplinary case study, the SMASH-HCM consortium is presented to illustrate how digital twin technologies and hybrid modelling can bring AI into clinical practice. Integration of genetic data further enhances AI's ability to identify at-risk individuals and predict disease progression. However, widespread AI applications raise concerns regarding data privacy, ethical considerations, and the risk of biases. Guidelines for researchers and developers—e.g. on trustworthy AI, regulatory frameworks, and transparent policies—are essential to address these possible pitfalls. As AI rapidly evolves, it has the potential to revolutionize drug discovery, disease management, and the patient journey in HCM, making interventions more precise, timely, and patient-centred.

<footnote n="1">¹Department of Experimental and Clinical Medicine, University of Florence, Viale Morgagni, 63, Florence 50134, Italy; ²Cardiomyopathy Unit, Careggi University Hospital, Largo Giovanni Alessandro Brambilla, 1, Florence 50134, Italy; ³Department of Biology, University of Florence, Florence, Italy; ⁴Department of Computer Science, University of Oxford, Oxford, United Kingdom; ⁵Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland; ⁶Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy; ⁷Department of Cardiology, University of Rennes, CHU Rennes, Inserm, LTSI—UMR 1099 Rennes F-35000, France; ⁸Pharmatics Limited, Edinburgh, United Kingdom; ⁹Heart Hospital, Tampere University Hospital, Finland; and ¹⁰Cardiology Unit, Meyer University Hospital, Florence, Italy</footnote>
**Graphical Abstract**

Artificial intelligence applications in hypertrophic cardiomyopathy

DATA

Biomarkers

<figure><img src="image_2.png" /></figure>

Early detection
of HCM

<figure><img src="image_3.png" /></figure>

Differential
diagnosis of LVH

<figure><img src="image_4.png" /></figure>

AIMS

Clinical data

<figure><img src="image_5.png" /></figure>

Genetics

<figure><img src="image_6.png" /></figure>

Pathology

<figure><img src="image_7.png" /></figure>

AI-driven in silico trials
Digital-twin based platforms

<figure><img src="image_8.png" /></figure>

Evaluation of
treatment efficacy

<figure><img src="image_9.png" /></figure>

Hypertrophic cardiomyopathy

<figure><img src="image_10.png" /></figure>

Deeper genetic
insights

<figure><img src="image_11.png" /></figure>

Better HCM risk
stratification

<figure><img src="image_12.png" /></figure>

**Keywords**

artificial intelligence • Hypertrophic cardiomyopathy • Machine learning • Left ventricular hypertrophy • Deep learning • Digital-twin

**Introduction**

Hypertrophic cardiomyopathy (HCM) is a genetically determined heart disease characterized by unexplained left ventricular hypertrophy (LVH) that is not attributable to conditions such as hypertension or aortic stenosis.¹ It is often associated with allelic variants in sarcomere proteins, leading to disorganized myocardial architecture, increased myocardial stiffness, and impaired ventricular relaxation.¹ Given its phenotypic heterogeneity and variability in treatment response, the management of HCM requires a patient-tailored approach encompassing symptom control, risk stratification for sudden cardiac death (SCD), and definition of therapeutic strategies ranging from pharmacological management, septal reduction therapies, and implantable cardioverter-defibrillators (ICDs).¹

In recent years, advances in artificial intelligence (AI), enabled by sig-
nificant increases in computational power, the availability of large and
diverse databases, and the development of new theories and algo-
rithms, have added a new dimension to the diagnosis, risk stratification,
and management of HCM. AI-based algorithms hold the potential to
significantly improve diagnostic accuracy in HCM by evaluating multiple

data, like electrocardiogram (ECG), echocardiogram, and cardiac mag-
netic resonance (CMR) imaging (Graphical Abstract).

We here review novel, AI-driven approaches aimed at improving the detection of subtle phenotypic variations, predicting clinical outcomes, and evaluating response to treatment. We conducted a non-systematic search of the PubMed database using the keywords 'artificial intelligence AND hypertrophic cardiomyopathy'; studies were selected based on their clinical relevance, methodological quality, and impact on the field. The SMASH-HCM project is also introduced as a multidisciplinary implementation initiative, integrating AI research, clinical data, and biophysical modelling to create real-world digital health solutions in HCM.

**Overview of artificial intelligence**

An AI system refers to a machine-based system that is designed to op-
erate with varying levels of autonomy, that may exhibit adaptiveness,
and that infers, from the input it receives, how to generate predictions,
content, or recommendations.² The various branches of AI have many
applications in cardiology and are expected to change the landscape of
disease diagnosis, treatment planning, and patient management.
<figure><img src="image_13.png" /><figcaption>**Figure 1** Diagram illustrating AI taxonomy and the different applications in HCM. The figure illustrates the hierarchy of AI models, with ML encompassing DL, which includes CNNs, and NLP as a separate approach. AI applications in HCM, such as risk stratification, diagnosis, imaging analysis, genotype prediction, and patient management, are linked to their respective models using colour-coded arrows: blue for ML, yellow for DL, red for CNNs, and green for NLP. Overlapping connections indicate that multiple models can contribute to the same application, highlighting AI's versatility in HCM management. CNN, convolutional neural network; DL, deep learning; ECG, electrocardiogram; AI, artificial intelligence; CMI, cardiac myosin inhibitor; LVH, left ventricular hypertrophy; CMR, cardiac magnetic resonance; HCM, hypertrophic cardiomyopathy; ML, machine learning; NLP, natural language processing; oHCM, obstructive hypertrophic cardiomyopathy.</figcaption></figure>

Machine learning (ML) is a branch of AI that focuses on creating algorithms that can learn from data and make predictions or decisions without being explicitly programmed to perform those tasks.³ Deep learning (DL) is a specialized subset of ML that uses multi-layer artificial neural networks to model complex patterns in large datasets. These deep neural networks are particularly effective when trained on extensive, well-annotated datasets.³ Convolutional neural networks (CNNs) are a type of DL model specifically designed to process structured grid data, such as ECG and images. ML and DL, by combining large datasets of signals with other sources of patient information—such as genetic profiles, clinical history, biophysical computer models, and unstructured data—may enhance phenotyping, improve arrhythmic risk prediction, and uncover the complex mechanisms linking genetic backgrounds, structural abnormalities, and functional phenotypes.⁴

Natural language processing (NLP) is an AI discipline that focuses on the interaction between computers and human (natural) language.³ NLP techniques enable machines to understand, interpret, and generate human language, which is particularly useful for extracting information from unstructured text data in electronic health records.³

By integrating AI into clinical workflows, healthcare providers can access a more concise and comprehensive synthesis of available data, facilitating a holistic understanding of HCM (Figure 1). This can lead to improved patient outcomes through personalized treatment approaches and targeted risk management. Table 1 summarizes key studies on AI in HCM, whereas Table 2 details their methodological features and performance metrics. To illustrate

the evolution of AI applications in this field, we have also included a graphical timeline (Figure 2).

# Applications of artificial intelligence in hypertrophic cardiomyopathy

## Early detection of HCM

AI algorithms analyse cardiac signals by learning abstract data representations associated with different features of disease, such as on electric vectors, wall thickness, distribution of hypertrophy, myocardial texture, and ventricular morphology.⁴ The ECG is a non-invasive, primary, inexpensive tool to diagnose cardiac abnormalities, and it is therefore used as an early screening tool for cardiovascular diseases.

In 1998, Ouyang et al. conducted one of the earliest AI-ECG studies on 79 patients with HCM, using supervised ML for diagnosing the hypertrophic portions of HCM.⁵ In 2019, Tison et al. developed an AI model combining ML and DL to analyse 36 186 raw ECG recordings, aiming to estimate cardiac structure parameters like LVH and detect different diseases. Among these, HCM was identified with good discrimination [area under the curve (AUC) 0.91], mainly driven by ST-T alterations in lead V1, prolonged P wave, QT and PR intervals, and QRS features in lead aVR.⁶

In 2020, the Mayo Clinic developed an AI-ECG model for HCM detection from standard 12-lead ECG alone.⁷ Raw ECG data from 2448 HCM patients and 51 153 non-HCM age- and sex-matched controls were used to test and train a CNN. ECGs with the presence of ventricular pacing or left bundle branch block were not included.
<table><caption>**Table 1 Summary of main studies investigating the application of artificial intelligence to different diagnostic techniques in HCM**</caption><thead><tr><th>Data source</th><th>Early detection</th><th>Differential diagnosis</th><th>Deeper genetic insights</th><th>Better risk stratification</th><th>Evaluation of treatment efficacy</th></tr></thead><tbody><tr><td>ECG and related techniques</td><td><ul><li>Diagnosis of HCM at ECG<sup>7-9,43</sup> or wearable biosensors<sup>11</sup></li><li>ECG-based identification of hypertrophic segments<sup>5</sup></li></ul></td><td></td><td></td><td></td><td>Tracking of treatment response in HCM patients receiving mavacamten<sup>43</sup></td></tr><tr><td>Echocardiography</td><td>HCM diagnosis among individuals with LVH<sup>13</sup></td><td>Differentiation of common aetiologies of LVH (i.e. HHD, HCM, and CA)<sup>20-23</sup></td><td>Prediction of positive genotype<sup>27</sup></td><td></td><td></td></tr><tr><td>CMR</td><td><ul><li>Automated quantification of LV volumes and function<sup>14</sup></li><li>Automated measurement of T1, ECV,<sup>15</sup> and MWT<sup>16</sup></li><li>Automated myocardial segmentation and quantification of myocardial fibrosis<sup>17</sup></li><li>Contrast-sparing diagnosis of HCM<sup>18,19</sup></li></ul></td><td>Distinguishing different cardiomyopathies or causes of LVH<sup>24,25</sup></td><td><ul><li>Prediction of positive genotype<sup>29</sup></li><li>Better genotype—phenotype correlations<sup>30</sup></li></ul></td><td>LGE-based arrhythmic risk stratification<sup>41</sup></td><td></td></tr><tr><td>Multimodal evaluation</td><td></td><td>Determination of LVH aetiology based on joint interpretation of 12-lead ECG and echocardiogram<sup>26</sup></td><td><ul><li>Prediction of positive genotype<sup>28</sup></li><li>VUS pathogenicity prediction<sup>32</sup></li></ul></td><td><ul><li>Improvement of existing HCM risk-stratification tools<sup>34</sup></li><li>Development of new HCM risk-stratification tools in terms of AF,<sup>35</sup> VAs,<sup>38</sup> HF and disease progression,<sup>36,39</sup> and MACE<sup>33,37,40,42</sup></li></ul></td><td>Multiparametric evaluation of mavacamten effects<sup>44</sup> and therapeutic response<sup>45</sup></td></tr></tbody></table>

CA, cardiac amyloidosis; CMR, cardiac magnetic resonance; CVD, cardiovascular disease; ECV, extracellular volume; HF, heart failure; HHD, hypertensive heart disease; HCM, hypertrophic cardiomyopathy; LGE, late gadolinium enhancement; LVH, left ventricular hypertrophy; MACE, major adverse cardiovascular events; MWT, maximum wall thickness; SCD, sudden cardiac death; VUS, variant of unknown significance.

The AUC in the testing dataset was 0.96 [95% confidence interval (CI) 0.95–0.96], performing particularly well in younger patients (sensitivity, 95%; specificity, 92%). This model was later tested as a screening tool in a paediatric HCM population, demonstrating accurate detection of HCM regardless of sex or genotype.⁸ However, a key limitation in early detection algorithms is the high false-positive rates and, notably, the challenge of distinguishing true early HCM from other causes of LVH, such as aortic stenosis or hypertensive heart disease. Ko *et al.*⁷ specifically trained their CNN model to differentiate HCM from aortic stenosis and LVH, partially addressing this critical diagnostic step. This distinction is essential for clinical accuracy, particularly when applying AI to general population screening in terms of healthcare costs. Given the potential high false-positive rates of AI-ECG diagnosis of HCM, Maanja *et al.* developed a clinical score (HCM-DETECT) incorporating variables such as age, coronary artery disease, and prior

pacemaker or valve surgery to refine prediction.⁹ Their findings highlight that many false-positive results are driven by non-HCM-related LVH, QRS widening, or ST-segment deviations—features that reduce specificity and risk over-diagnosis. The tandem use of AI with clinical filtering helps mitigate this issue, but the risk of overburdening healthcare systems with unnecessary follow-up remains a concern.

Additionally, these studies have several limitations that restrict their immediate generalizability. First, these AI-ECG models have been tested in single-centre, retrospective cohorts, raising concerns about their overfitting. To address this, Siontis *et al.* recently conducted an external validation study applying the Mayo Clinic AI-ECG algorithm to three independent international cohorts.¹⁰ This study included 773 patients with HCM and 3867 controls across diverse ethnic backgrounds (54.6% East Asian, 43.2% White, 2.2% Black) and demonstrated that the algorithm maintained high performance (AUC, 0.92;
<table><caption>Table 2 Summary of key studies on AI applications in HCM: methodological features and performance metrics</caption><thead><tr><th>Study (1st author, year)</th><th>Sample size</th><th>N° centres</th><th>External validation</th><th>Input modalities</th><th>Aim</th><th>Quantitative measures</th><th>Reproducibility disclosed/Public data available</th><th>Clinical validation</th></tr></thead><tbody><tr><td>Ouyang, 1998<sup>5</sup></td><td>79 pts with HCM</td><td>Single-centre</td><td>No</td><td>ECG</td><td>Using ECGs to diagnose the hypertrophic portions of HCM</td><td>—</td><td>No</td><td>Retrospective</td></tr><tr><td>Green, 2019<sup>1</sup></td><td>19 HCM pts vs. 64 healthy volunteers</td><td>Single-centre</td><td>No</td><td>PPG signals</td><td>oHCM detection</td><td>C-statistics 0.99 (95% CI 0.99–1.0)</td><td>No</td><td>Retrospective</td></tr><tr><td>Tison, 2019<sup>6</sup></td><td>36 186 ECGs from general population</td><td>Single-centre</td><td>No</td><td>ECG</td><td>ECG-based detection of HCM</td><td>AUC 0.91 (95% CI 0.90–0.92)</td><td>No</td><td>Retrospective</td></tr><tr><td>Ko, 2020<sup>7</sup></td><td>2448 HCM pts vs. 51 153 non-HCM age- and sex-matched controls</td><td>Single-centre</td><td>Yes (Siontis, 2024*)</td><td>ECG</td><td>ECG-based detection of HCM</td><td>AUC 0.96 (95% CI: 0.95–0.96); sensitivity of 87%; specificity of 91%</td><td>No</td><td>Retrospective</td></tr><tr><td>Siontis, 2021<sup>8</sup></td><td>300 HCM pts ≤18 years vs. 18 439 age- and sex-matched non-HCM controls</td><td>Single-centre</td><td>No</td><td>ECG</td><td>ECG-based detection of HCM</td><td>AUC 0.98 (95% CI: 0.98–0.99); sensitivity of 92%; specificity of 95%</td><td>No</td><td>Retrospective</td></tr><tr><td>Maanja, 2022<sup>9</sup></td><td>20 677 pts with at least one 12-lead ECG in January 2021 (derivation) and 15 147 pts with an ECG in January 2022 (testing)</td><td>Multicentre</td><td>No (internal testing)</td><td>ECG</td><td>Identify clinical markers distinguishing true and false positives to refine AI-ECG application for HCM</td><td>The clinical HCM-DETECT score had an AUC of 0.81 (95% CI: 0.73–0.87) for differentiating true- vs. false-positive results</td><td>No</td><td>Retrospective</td></tr><tr><td>*Siontis, 2024<sup>10</sup></td><td>773 HCM pts vs. 3867 non-HCM controls</td><td>Multicentre</td><td>—</td><td>ECG</td><td>ECG-based detection of HCM</td><td>AUC 0.92 (95% CI: 0.91–0.93); diagnostic accuracy of 87%, sensitivity of 83%, and specificity of 88%</td><td>No</td><td>Retrospective</td></tr><tr><td>Hillis, 2025<sup>12</sup></td><td>293 HCM vs. 2912 non-HCM controls</td><td>Multicentre</td><td>No</td><td>ECG</td><td>ECG-based detection of HCM</td><td>AUC 0.97 (95% CI: 0.96–0.98); sensitivity of 68%; specificity of 99%</td><td>No</td><td>Retrospective</td></tr><tr><td>Karra, 2024<sup>13</sup></td><td>12 281 pts (of which 1535 HCM pts)</td><td>Single centre</td><td>No</td><td>Echo</td><td>Identify HCM among individuals with LVH</td><td>AUC 0.85; sensitivity 68%; specificity 99%</td><td>No</td><td>Retrospective</td></tr><tr><td>Guo, 2022<sup>14</sup></td><td>123 HCM pts</td><td>Single-centre</td><td>No</td><td>CMR</td><td>Evaluate the performance of a DL-based method to automatically quantify LV function</td><td>EF from automatic segmentation identified HCM pts with sensitivity of 78% and specificity of 54%</td><td>No</td><td>Retrospective</td></tr></tbody></table>

Continued
<table><caption>Table 2 Continued</caption><thead><tr><th>Study (1st author, year)</th><th>Sample size</th><th>N° centres</th><th>External validation</th><th>Input modalities</th><th>Aim</th><th>Quantitative measures</th><th>Reproducibility disclosed/Public data available</th><th>Clinical validation</th></tr></thead><tbody><tr><td>Chang, 2022<sup>15</sup></td><td>95 pts (of which 12 HCM)</td><td>Single centre</td><td>No</td><td>CMR</td><td>Test a DL algorithm for the automated measurement of native T1 and ECV</td><td>Agreement between DL and the reference: T1, r = 0.97 (95% CI: 0.95–0.98); ECV, r = 0.99 (95% CI: 0.98–0.99)</td><td>No</td><td>Retrospective</td></tr><tr><td>Augusto, 2021<sup>16</sup></td><td>60 HCM pts</td><td>Multicentre</td><td>No</td><td>CMR</td><td>Test an ML algorithm to automatically quantify MWT</td><td>Test–retest difference (mean ± SD): ML 0.7 ± 0.6 mm vs. experts 1.1 ± 0.9 mm to 3.7 ± 2.0 mm (P < 0.01)</td><td>No</td><td>Retrospective</td></tr><tr><td>Fahmy, 2021<sup>17</sup></td><td>191 HCM pts</td><td>Multicentre</td><td>Yes</td><td>CMR</td><td>To accurately quantify LGE scar</td><td>%Scar<sub>LGE-cine</sub> = 0.82 × %Scar<sub>manual</sub>, r = 0.84</td><td>No</td><td>Retrospective</td></tr><tr><td>Baeßler, 2018<sup>18</sup></td><td>32 HCM pts and 32 controls</td><td>Single centre</td><td>No</td><td>CMR</td><td>To assess myocardial texture alterations in HCM on non-contrast T1-weighted images</td><td>Grey-level non-uniformity differentiated HCM pts from controls with sensitivity of 91%, specificity of 93%</td><td>No</td><td>Retrospective</td></tr><tr><td>Zhang, 2021<sup>19</sup></td><td>1348 HCM pts</td><td>Multicentre (HCMR)</td><td>No (only independent testing)</td><td>CMR</td><td>To test a contrast agent–free technology to replace LGE</td><td>VNE showed strong agreement with LGE (ICC: 0.77–0.87; 95% CI, ≈20%)</td><td>Yes</td><td>Retrospective</td></tr><tr><td>Yu, 2021<sup>20</sup></td><td>50 pts with HCM, 50 with HHD, 50 with UCM</td><td>Single centre</td><td>No</td><td>Echo</td><td>To differentiate multiple LVH aetiologies</td><td>HCM is significantly different in terms of brightness, SD, CoV, Skew, contrast7, and homogeneity5 (all P ≤ 0.005)</td><td>No</td><td>Retrospective</td></tr><tr><td>Hwang, 2022<sup>21</sup></td><td>112 pts with HHD, 191 with HCM, 81 with AL-CA and 546 healthy subjects</td><td>Two centres</td><td>No</td><td>Echo</td><td>To differentiate multiple LVH aetiologies</td><td>AUC for HCM: 0.98 in test set, 0.99 in validation set</td><td>No</td><td>Retrospective</td></tr><tr><td>Wu, 2022<sup>22</sup></td><td>74 AL-CA and 64 HCM pts</td><td>Single-centre</td><td>No</td><td>Echo</td><td>To differentiate CA from HCM</td><td>ML such as support vector machine (AUC 0.95, P = 0.477), random forest (AUC 0.97, P = 0.301), and gradient boosting machine (AUC, 0.98, P = 0.230) differentiated CA and HCM</td><td>No</td><td>Retrospective</td></tr><tr><td>Duffy, 2022<sup>23</sup></td><td>23 745 pts</td><td>Multicentre</td><td>Yes</td><td>Echo</td><td>To quantify LVH and predict the aetiology of LVH</td><td>Accurate detection of CA (AUC, 0.79) and HCM (AUC, 0.89)</td><td>No</td><td>Retrospective</td></tr><tr><td>Antonopoulos, 2021<sup>24</sup></td><td>149 pts 30 LVH, 61 HCM, 28 CA, and 30 healthy</td><td>Single centre</td><td>No</td><td>CMR</td><td>Radiomics-based ML to classify LVH phenotypes</td><td>AUC 0.753 for multinomial classification of disease phenotype (normal vs. LVH vs. HCM vs. CA)</td><td>No</td><td>Retrospective</td></tr></tbody></table>

Continued
<table><caption>**Table 2** Continued</caption><thead><tr><th>Study (1st author, year)</th><th>Sample size</th><th>N° centres</th><th>External validation</th><th>Input modalities</th><th>Aim</th><th>Quantitative measures</th><th>Reproducibility disclosed/Public data available</th><th>Clinical validation</th></tr></thead><tbody><tr><td>Izquierdo, 2021<sup>25</sup></td><td>118 pts (35 LVNC, 25 HCM, 37 DCM, and 21 healthy)</td><td>Single centre</td><td>No</td><td>CMR</td><td>Radiomics-based ML to classify different cardiomyopathies</td><td>HCM vs. Rest AUC: 0.99</td><td>No</td><td>Retrospective</td></tr><tr><td>Soto, 2022<sup>26</sup></td><td>2728 pts</td><td>Single centre</td><td>No</td><td>ECG<br>Echo</td><td>To augment physician interpretation of LVH aetiology</td><td>High discriminatory ability in distinguishing HCM from hypertension with an AUC of 0.91, AUPRC of 0.78</td><td>Yes</td><td>Retrospective</td></tr><tr><td>Wang, 2020<sup>20</sup></td><td>102 HCM pts with P/LP variants in MYBPC3 and MYH7</td><td>Single-centre</td><td>No</td><td>CMR</td><td>To distinguish MYBPC3 and MYH7 HCM</td><td>Accuracy 92%;<br>AUC 0.97 (95% CI: 0.96–0.97)</td><td>No</td><td>Prospective observational</td></tr><tr><td>Morita, 2021<sup>27</sup></td><td>99 HCM pts</td><td>Single-centre</td><td>No</td><td>Echo</td><td>To predict genotype positivity</td><td>Mayo score + DCNN-derived probability vs. Mayo score alone (AUC, 0.86 vs. 0.72; P < 0.001). Toronto score + DCNN-derived probability vs. Toronto score alone (AUC 0.84 vs. 0.75; P = 0.03)</td><td>No</td><td>Retrospective</td></tr><tr><td>Liang, 2021<sup>28</sup></td><td>102 HCM pts (training set) and 76 HCM pts (test set)</td><td>Two centres</td><td>Yes</td><td>Clinical<br>Echo<br>CMR</td><td>To predict genotype positivity</td><td>AUC of 0.92 (95% CI 0.85–0.99) in predicting genotype positivity in the test set</td><td>Yes (upon request)</td><td>Retrospective</td></tr><tr><td>Zhou, 2021<sup>29</sup></td><td>198 HCM pts</td><td>Single-centre</td><td>No</td><td>CMR</td><td>To predict genotype positivity</td><td>NRI: P < 0.001 for the Toronto and Mayo score</td><td>No</td><td>Retrospective</td></tr><tr><td>Liu, 2024<sup>31</sup></td><td>385 HCM tissue samples</td><td>Multicentre</td><td>No</td><td>Genetics</td><td>To identify HCM-related hub genes</td><td>AUC, 0.80; sensitivity of 86%, specificity of 70%</td><td>No</td><td>Observational</td></tr><tr><td>Lyon, 2019<sup>34</sup></td><td>85 HCM pts and 38 healthy volunteers</td><td>Single-centre</td><td>No</td><td>12-lead<br>Holter ECG<br>Echo<br>CMR</td><td>To better stratify HCM pts risk</td><td>—</td><td>No</td><td>Retrospective</td></tr><tr><td>Alis, 2020<sup>41</sup></td><td>64 HCM pts</td><td>Single-centre</td><td>No</td><td>CMR</td><td>To predict VTs</td><td>95% sensitivity;<br>93% specificity;<br>94% accuracy</td><td>No</td><td>Retrospective</td></tr></tbody></table>

Continued
<table><caption>Table 2 Continued</caption><thead><tr><th>Study (1st author, year)</th><th>Sample size</th><th>N° centres</th><th>External validation</th><th>Input modalities</th><th>Aim</th><th>Quantitative measures</th><th>Reproducibility disclosed/Public data available</th><th>Clinical validation</th></tr></thead><tbody><tr><td>Kochav, 2021<sup>40</sup></td><td>183 HCM pts</td><td>Single-centre</td><td>No</td><td>Clinical Echo Stress test Genotype positivity</td><td>To predict MACE (death due to HF, heart transplant, and SCD)</td><td>ML model accuracy, 85%; sensitivity, 88%; specificity, 84%</td><td>No</td><td>Prospective</td></tr><tr><td>Bhattacharya, 2021<sup>35</sup></td><td>831 HCM pts</td><td>Single-centre</td><td>No</td><td>Clinical Echo Stress test CMR</td><td>To distinguish AF and non-AF pts</td><td>Sensitivity, 74%; specificity, 70%; C-index of 0.80</td><td>No</td><td>Retrospective</td></tr><tr><td>Smole, 2021<sup>37</sup></td><td>2302 HCM pts</td><td>Single-centre</td><td>No</td><td>Clinical Echo ECG Holter Genetics</td><td>To predict MACE (sustained VT, HF, ICD activation, SCD, cardiac death, and all-cause death)</td><td>The new ML model outperformed existing risk-stratification models for SCD, cardiac death, and all-cause death (higher AUC by 17%, 9%, and 1%, respectively)</td><td>Data may be available upon request</td><td>Retrospective</td></tr><tr><td>Fahmy, 2021<sup>39</sup></td><td>2732 HCM pts</td><td>Single-centre</td><td>No</td><td>Clinical Echo</td><td>To predict HF progression at 5 years</td><td>AUC, 0.81 (95% CI, 0.76–0.86); accuracy, 74%; sensitivity, 80%; specificity, 72%</td><td>Yes<sup>a</sup></td><td>Retrospective</td></tr><tr><td>Pičulin, 2022<sup>36</sup></td><td>1860 HCM pts</td><td>Single-centre</td><td>No</td><td>Clinical ECG Echo CMR</td><td>To model the patient's clinical status up to 10 years ahead</td><td>The best-performing random forest model improved R<sup>2</sup> from 0.3 to 0.6</td><td>No</td><td>Retrospective</td></tr><tr><td>Rhee, 2024<sup>33</sup></td><td>2111 HCM pts</td><td>Two centres</td><td>Yes</td><td>Clinical Echo</td><td>To predict MACE (all-cause death, HF-adm, and stroke)</td><td>External validation cohort: AUROC for MACE of 0.77</td><td>No</td><td>Retrospective</td></tr><tr><td>Al Wazzan, 2024<sup>38</sup></td><td>434 HCM pts</td><td>Two centres</td><td>No</td><td>LV longitudinal strain</td><td>To predict VAs</td><td>4 clusters identified</td><td>No</td><td>Retrospective</td></tr><tr><td>Zhao, 2024<sup>42</sup></td><td>758 HCM pts (533 internal and 225 external cohort)</td><td>Multicentre</td><td>Yes</td><td>Clinical CMR</td><td>To predict MACE (VAs, SCD, HF, and AF-related stroke)</td><td>AUCs of 0.83 (internal) and 0.81 (external)</td><td>No</td><td>Retrospective</td></tr></tbody></table>

Continued
<table><caption>Table 2 Continued</caption><thead><tr><th>Study (1st author, year)</th><th>Sample size</th><th>N° centres</th><th>External validation</th><th>Input modalities</th><th>Aim</th><th>Quantitative measures</th><th>Reproducibility disclosed/Public data available</th><th>Clinical validation</th></tr></thead><tbody><tr><td>Abraham, 2022<sup>44</sup></td><td>EXPLORER-HCM data at week 30 (NCT03470545)</td><td>Multicentre</td><td>—</td><td>ECG</td><td>To characterize mavacamten effects beyond pVO2 increase and NYHA class reduction</td><td>The cluster analysis resulted in four main groups</td><td>No</td><td>Retrospective</td></tr><tr><td>Siontis, 2023<sup>43</sup></td><td>13 HCM pts (216 ECGs) from PIONEER-OLE 2600 age- and sex-matched controls</td><td>Multicentre</td><td>—</td><td>ECG</td><td>To evaluate response to mavacamten</td><td>Mean HCM score decreases during mavacamten treatment: 0.80–0.45 for Mayo and 0.70–0.35 for USCF algorithms</td><td>No</td><td>Longitudinal</td></tr><tr><td>Suppah, 2025<sup>45</sup></td><td>ECG: 27 HCM pts Echo: 58 HCM pts</td><td>Single-centre</td><td>No</td><td>ECG Echo</td><td>To evaluate response to mavacamten</td><td>Notable reductions in HCM phenotype severity (median, 29.7% to 0.4%, P = 0.001) and diastolic dysfunction (median, 2 to 0, P = 0.004)</td><td>No</td><td>Longitudinal</td></tr></tbody></table>

AF, atrial fibrillation; AL-CA, light-chain cardiac amyloidosis; AUC, area under the curve; CA, cardiac amyloidosis; CI, confidence interval; CMR, cardiac magnetic resonance; CoV, coefficient of variation; DCNN, deep convolutional neural network; DL, deep learning; DCM, dilated cardiomyopathy; Echo, echocardiography; ECG, electrocardiogram; EF, ejection fraction; ECV, extracellular volume; HF, heart failure; HHD, hypertensive heart disease; HCM, hypertrophic cardiomyopathy; ICC, intraclass correlation coefficient; ICD, implantable cardioverter-defibrillator; LGE, late gadolinium enhancement; LV, left ventricle; LVH, left ventricular hypertrophy; LVNC, left ventricular non-compaction cardiomyopathy; LVOT, left ventricular outflow tract; MACE, major adverse cardiovascular events; ML, machine learning; MWT, maximum wall thickness; MYBPC3, myosin-binding protein C; MYH7, β-myosin heavy chain; NYHA, New York Heart Association; oHCM, obstructive hypertrophic cardiomyopathy; P/LP, pathogenic or likely pathogenic; PPG, photoplethysmography; pts, patients; pVO2, peak oxygen consumption; RF, random forest; SCD, sudden cardiac death; SD, standard deviation; UCM, uraemic cardiomyopathy; UCSF, University of California, San Francisco; VAs, ventricular arrhythmias; VNE, virtual native enhancement; VTs, ventricular tachyarrhythmias; VUS, variant of uncertain significance.

<footnote n="4">⁴https://dataverse.harvard.edu/dataset.xhtml? persistentId=doi:10.7910/DVN/FFNLPE.</footnote>
<figure><img src="image_14.png" /><figcaption>**Figure 2** Timeline of key milestones in AI applications for HCM. This timeline illustrates selected landmark studies showcasing the evolution of artificial intelligence (AI) in the diagnosis, risk stratification, genetic profiling, and treatment evaluation of hypertrophic cardiomyopathy (HCM). Studies were included based on their innovation (e.g. first to introduce a novel AI application) or relevance (e.g. large-scale, multicentre, or externally validated work). The timeline is organized across thematic domains—early detection, differential diagnosis, genetic insights, risk stratification, treatment efficacy, and in silico trials/digital twin technologies—highlighting the progressive integration of AI into various aspects of HCM management. AI, artificial intelligence; CNN, convolutional neural network; ECG, electrocardiogram; LVH, left ventricular hypertrophy; HCM, hypertrophic cardiomyopathy.</figcaption></figure>

sensitivity, 83%; specificity, 88%), providing reassuring evidence of external validity. Nevertheless, prospective clinical studies are still needed to confirm its utility in real-world screening and to evaluate cost-effectiveness and clinical impact.

Wearable devices are non-invasive technologies designed to monitor physiological parameters continuously or intermittently. These include commercially available devices such as smartwatches with ECG functionality (e.g. Apple Watch®), adhesive ECG patches (e.g. Zio® ECG monitors), chest strap monitors, and wrist-worn fitness bands. Such devices are increasingly used in clinical practice for remote monitoring of cardiac rhythm. Research is currently exploring the use of wearable biosensors for structural heart disease screening. For example, Green et al. developed an ML model using photoplethysmography recordings from a smartwatch-like device to detect obstructive HCM in a cohort of 19 patients and 64 healthy controls.¹¹ These findings are still at the research stage and require further validation in larger, real-world populations before being adopted as clinical screening tools.¹²

While transthoracic echocardiography (TTE) is a cornerstone for the initial evaluation of patients with LVH, the application of AI to echocardiographic imaging for HCM detection remains extremely limited. To date, only one study has explored this approach by training a CNN on standard TTE views (parasternal long axis and apical

four-chamber) from over 12 000 patients.¹³ The model achieved an AUC of 0.85, with high specificity (99%) but moderate sensitivity (68%) for identifying HCM among patients with septal hypertrophy.

AI-enhanced CMR provides valuable insights into HCM diagnosis by enabling more precise and automated image interpretation. There are already commercially available DL algorithms that allow automated assessment of left ventricle (LV) volume and function¹⁴ as well as T1 or extracellular volume measurement in HCM patients.¹⁵ In a preliminary study, ML-based maximum wall thickness (MWT) measurement in HCM was superior to human experts; interestingly, this has direct implications, as it has been shown that a clinical trial using ML would need 2.3 times (1.6–4.6) fewer patients to detect a 2 mm MWT change.¹⁶

Myocardial scar quantified by late gadolinium enhancement (LGE) has a diagnostic and prognostic role in HCM; however, manual segmentation of myocardial borders and scarred regions is highly prone to reader variability. Automated image segmentation using deep CNNs can offer standardized LGE analysis and reduce the time and effort required for manual contouring.¹⁷ One of the most promising applications of AI is the possibility of contrast-sparing HCM diagnosis. This may lead to the development of a contrast-free technology to replace LGE for faster and cheaper CMR scans.¹⁸ ML models can indeed be used as a scar screening tool prior to gadolinium administration. Virtual native enhancement, a new DL-driven CMR technology, can generate images that closely
resemble conventional LGE without the need for gadolinium-based contrast agents, achieving high agreement with LGE itself.¹⁹

## Differential diagnosis

Using pattern recognition, AI can distinguish HCM from other forms of LVH.²⁰ A DL algorithm has been developed to help differentiate common causes of LVH, including hypertensive heart disease (HHD), HCM, and light-chain cardiac amyloidosis (CA), using standard TTE images.²¹ Similarly, ML combined with 2D-speckle tracking echocardiography has performed well by integrating plentiful variables to identify the most discriminative predictors.²² A study by Duffy *et al.* assessed the accuracy of a DL algorithm in quantifying LVH and differentiating its aetiology between CA and HCM.²³ The algorithm was trained and tested on video echocardiograms. In 23 745 patients, the DL model accurately measured LV dimensions and classified CA and HCM. The algorithm also demonstrated high accuracy in external validation sets, distinguishing HCM from other causes of LVH with an AUC of 0.98.²³ However, these studies are constrained by limited sample sizes, restricted clinical settings, and a lack of publicly available datasets; most rely on retrospective analyses without prospective external validation, making their translation to routine clinical practice still premature.

CMR is often superior to echocardiography in identifying areas of segmental hypertrophy, not reliably visualized by echocardiography. Myocardial radiomic phenotyping has proved successful in distinguishing healthy from hypertrophic myocardium and can also differentiate LVH aetiology, including HCM or CA.²⁴,²⁵

Unlike previous studies, Soto and colleagues proposed the first DL model that integrates both ECG and echocardiogram data to differentiate HCM from HHD.²⁶ By comparing various fusion strategies for combining multimodal data with single-modality models, the authors demonstrated that integrating spatio-temporal information from both modalities significantly improves classification performance, achieving an AUC of 0.91.²⁶ Although the study lacked external validation on independent cohorts, the authors addressed this limitation by open-sourcing their code and releasing the trained models, promoting reproducibility and supporting further research.

## Deeper genetic insights

Genetic testing is essential for guiding family screening strategies and has significant prognostic and diagnostic value in HCM. However, traditional genetic testing is time- and resource-consuming, limiting its widespread application. In this context, AI can dramatically speed up genome sequencing, enhancing accuracy and reducing errors in identifying genetic variants. Few studies have explored DL/ML algorithms for mutation-risk prediction and genotype positivity in HCM.²⁷⁻²⁹ In a cohort of 99 HCM patients, deep CNN analysis of TTE images outperformed the Mayo and the Toronto genotype score predictors, and combining both models further improved predictive accuracy.²⁷ Similarly, an ML model constructed with clinical and cardiac imaging data was trained to predict genotype positivity in a cohort of 102 (training set) and 76 (test set) HCM patients.²⁸ The ML model demonstrated an AUC of 0.92 (95% CI, 0.85–0.99) in predicting genotype positivity in the test set, significantly outperforming the Toronto and the Mayo scores.²⁸ Radiomic CMR analysis may play a role not only in predicting genotype positivity,²⁹ but also in effectively distinguishing between different genetic subtypes of HCM, such as β-myosin heavy chain (MYH7) and β-myosin-binding protein C (MYBPC3).³⁰

Finally, a recent study applied an ML-driven multi-cohort analysis to identify a diagnostic gene signature for HCM, focusing on early-stage disease.³¹ The Authors identified 27 hub genes and developed a stable diagnostic model. Moreover, the study revealed immune-related mechanisms, including differential immune-cell infiltration patterns

between high- and low-risk groups, highlighting novel potential therapeutic targets.³¹

Several limitations should be acknowledged, including the lack of external validation in most cases, potential selection bias towards patients already undergoing genetic testing, and limited diversity in terms of race and clinical presentation. Additionally, genotype classifications are based on current knowledge and may evolve over time, raising questions about long-term model reliability and generalizability.

In parallel, AI-based bioinformatics approaches have emerged to address the growing number of variants of uncertain significance identified through large-scale sequencing. For example, Burghardt and Ajtai developed a neural/Bayes framework that models the structure-function relationships of missense mutations in key sarcomeric proteins, such as MYH7 and MYBPC3.³² Such methods hold promise for improving genetic interpretation, accelerating risk stratification, and informing early clinical decision-making as genomic data continue to expand.

## Risk stratification

Most patients with HCM remain asymptomatic throughout their lives, and only a small proportion of patients develop serious adverse outcomes such as end-stage heart failure (HF), cardiovascular death, and SCD. Current risk-stratification strategies are based on a limited number of elements and offer incomplete prediction (focusing mainly on arrhythmic risk). In this context, by thoroughly incorporating multidimensional data and factors that interact in linear and nonlinear manners, the ML-based methodology may provide a model with significantly enhanced prediction performance.³³

In a study by Lyon *et al.*, by applying ML on 12-lead Holter ECG, the Authors were able to classify HCM patients into four distinct phenotypes of ventricular remodelling, each associated with different levels of arrhythmic risk.³⁴ Importantly, the study identified two potential mechanisms—one related to conduction abnormalities and the other to ion channel remodelling—that could explain HCM heterogenic manifestations.³⁴ Similarly, other ML-based models have proved to be effective in identifying atrial fibrillation (AF)³⁵ and in predicting HCM disease progression in terms of adverse remodelling,³⁶ risk of ventricular arrhythmias (VAs),³⁷,³⁸ and progression to HF.³⁷,³⁹ In a prospective study by Kochav *et al.*, ML models were applied to a cohort of 183 patients with HCM.⁴⁰ During a median follow-up of 2.2 years, 33 subjects (18%) developed the primary outcome (a composite of heart transplantation, death due to HF, and SCD), the majority of whom (n = 28) underwent heart transplantation. The authors determined 20 predictive features (clinical, imaging, and genetics) based on random forest classification and a priori knowledge and developed 4 ML models, all significantly outperforming the reference model in predicting outcomes.⁴⁰

Pičulin *et al.* were the first to develop an ML-based model aimed at predicting long-term disease progression.³⁶ Their approach integrates six independent regression models to forecast key clinical parameters, including ventricular function and chamber dimensions, up to ten years ahead. Notably, their models demonstrated superior predictive performance compared to expert estimations in five out of six targets, highlighting the potential of AI to support longitudinal management in HCM.

CMR is useful in identifying high-risk HCM subgroups with extensive fibrosis, thin-walled scarred LV apical aneurysms, and end-stage systolic dysfunction. Radiomic characterization of CMR data offers deeper insights into myocardial tissue properties, thereby enhancing risk stratification. ML-based texture analysis of LGE has shown strong performance in distinguishing HCM patients with and without VTs.⁴¹ In a recent multicentre study involving 758 patients with HCM, Zhao *et al.* developed an ML-based framework integrating LGE and CMR myocardial strain with clinical parameters to predict major adverse cardiovascular events, including VAs, HF, AF-related stroke, and SCD.⁴² The model
achieved robust performance with AUCs of 0.83 in internal validation and 0.81 in an external multicentre cohort, significantly outperforming the traditional HCM Risk-SCD model AUC by 23%. Importantly, the study highlighted nonlinear associations between the extent of LGE, impaired myocardial strain, and elevated event risk.⁴²

However, many of these models were developed using retrospective, single-centre, or demographically homogeneous cohorts. Small event numbers, short follow-up durations, and limited external validation further reduce clinical applicability. Reproducibility remains a major concern, as most models do not share their code or use publicly available datasets. Finally, the “black box” nature of many AI models, along with reliance on data preprocessing or synthetic augmentation, raises questions about interpretability and real-world implementation. Prospective, multicentre validation and standardized methodologies are essential next steps to ensure clinical translation.

## Evaluation of treatment efficacy

Recent advances in the treatment of HCM have significantly broadened the therapeutic options available, particularly with the introduction of cardiac myosin inhibitors (CMIs), mavacamten, and aficamten. These novel agents directly address myocardial hypercontractility, a hallmark of the disease, delivering both clinical and symptomatic benefits. As these therapies gain traction, AI emerges as a key tool for patient selection and treatment response evaluation.

In a study by Siontis et al., two AI-ECG algorithms (University of California-San Francisco [UCSF] and Mayo Clinic) were developed and trained independently at two institutions to format 216 serial ECG data from the PIONEER-OLE trial (i.e. the open-label extension of the mavacamten phase 2 trial).⁴³ In the validation cohorts, both algorithms exhibited similar performance for HCM diagnosis, and exhibited mean HCM score decreases during mavacamten treatment: 0.80–0.45 for Mayo and 0.70–0.35 for USCF algorithms.⁴³ Interestingly, AI-ECG HCM scores correlated with disease status, haemodynamic changes in left ventricular outflow tract (LVOT) gradients, and serum N-terminal pro-B-type natriuretic peptide levels in patients with obstructive HCM on mavacamten treatment, even when ECG changes were less apparent.⁴³

An additional post-hoc ML analysis of the EXPLORER-HCM trial grouped patients according to their improvement status during mavacamten treatment, using unsupervised hierarchical clustering. The cluster analysis revealed four distinct patient groups based on their responses to treatment.⁴⁴ Two clusters (Groups 1 and 2), which included the majority of mavacamten-treated patients (88% and 85%, respectively), showed improvements in several clinical endpoints: Group 1 met both primary and secondary endpoints, while Group 2 showed meaningful benefits in secondary outcomes despite not meeting the primary endpoint. In contrast, Groups 3 and 4 were predominantly placebo-treated patients (95% and 90%, respectively), with Group 3 meeting only the primary endpoint and Group 4 showing no significant improvement across measures. These findings highlight that mavacamten may provide broader clinical benefits than captured by the primary endpoint alone, reinforcing the value of multidimensional treatment assessment in HCM.⁴⁴

In addition to post-hoc analyses of randomized trials, real-world studies have further supported the value of AI tools in treatment monitoring. In a recent single-centre analysis, AI-ECG and TTE parameters were evaluated in obstructive HCM patients treated with mavacamten for at least six months.⁴⁵ AI-ECG detected a reduction in HCM phenotype severity (from 29.7% to 0.4%, P = 0.001) and diastolic dysfunction grade (from 2 to 0, P = 0.004), while TTE showed parallel improvements in LVOT gradients (from 71 to 0 mmHg, P = 0.001), left atrial volume index, and diastolic function parameters (P = 0.001 for both).⁴⁵ Interestingly, these benefits appeared independent of LVOT gradient reduction, suggesting that mavacamten's disease-modifying effects extend beyond simple haemodynamic unloading.

In summary, AI-enhanced ECG analysis has demonstrated potential in assessing treatment response to myosin inhibitors like mavacamten, establishing correlations with disease progression, biomarker profiles, and clinical outcomes in HCM.

## AI-driven in silico trials and digital twin solutions

While AI applications have shown encouraging results in HCM, these tools often operate on isolated datasets and lack integration across different biological levels. This limits their ability to fully capture the complexity of HCM, which spans diverse genetic backgrounds, phenotypes, and clinical trajectories.

To overcome these limitations, in silico trials and digital twin technologies have emerged as promising innovations. In silico trials are computer-based simulations that model disease progression or treatment outcomes using patient-specific data. They offer a cost-effective and ethical alternative to certain stages of clinical research, enabling virtual testing of therapies before their application in patients. Digital twins expand on this concept by creating a comprehensive, virtual tool that integrates coherently and dynamically the clinical data acquired over time for an individual using mechanistic and statistical models.⁴⁶ In cardiovascular medicine, digital twin platforms have been explored for applications such as arrhythmia risk prediction, procedural planning for atrial and VA ablation, simulation of HF progression, and non-invasive assessment of cardiovascular dynamics through wearable sensors.⁴⁶ These technologies offer significant advantages, including the ability to test interventions in silico before applying them to real patients, optimize treatment strategies, and reduce costs. However, they also face limitations, such as the need for large, high-quality datasets, computational complexity, regulatory challenges, and the risk of over-reliance on models that may not fully capture biological variability.

An important example of this approach in HCM is the SMASH-HCM project (Stratification, Management, and Guidance of Hypertrophic Cardiomyopathy Patients using Hybrid Digital Twin Solutions) (https://smash-hcm.eu). SMASH-HCM aims to build a digital twin platform that combines AI models with mechanistic simulations to improve risk stratification, treatment personalization, and patient self-management. The project is structured into three levels of deep phenotyping (Figure 3). At the first level, AI/ML models will be developed to analyse clinical data, including ECGs and imaging data, to provide initial stratification and lifestyle recommendations. Secondly, AI/ML models will integrate advanced diagnostics such as CMR, genetic profiling, and patient-specific whole-heart and systemic models to refine risk stratification and personalize treatment recommendations. The third level will incorporate human-induced pluripotent stem cell-derived cardiomyocytes or other patient-specific in vitro data, along with computational models of cells and in vitro mimicking tissues, enabling detailed mechanistic insights into cardiac function, disease progression, and treatment response.

Importantly, SMASH-HCM goes beyond technical development by embedding patient engagement and self-management tools into its design, including health literacy strategies, behaviour change frameworks, and gamification elements to enhance patient adherence and empowerment. While still under development, the PILOT clinical trial will be crucial in validating SMASH-HCM's real-world impact, testing its feasibility across diverse patient profiles and clinical settings. If successful, SMASH-HCM could set a new standard for integrated, AI-driven care in HCM, potentially extending this model to other cardiovascular diseases.

## Clinical implementation and regulatory hurdles

Despite the increasing momentum around AI applications in HCM, real-world clinical implementation remains limited. A major barrier
<figure><img src="image_15.png" /></figure>

is the lack of regulatory approval for many Al-based tools, particularly those tailored to HCM. As of May 2025, the U.S. Food and Drug Administration (FDA) has authorized over 1000 Al-enabled medical devices, the majority of which are focused on radiology and are not HCM-specific (e.g. us2.v1). To date, Viz HCM is the only FDA-cleared algorithm specifically approved for the detection of HCM. It analyses 12-lead ECGs in real time to flag suspected HCM cases and facilitates timely referral to specialist care.

To support broader clinical adoption, Al systems must comply with evolving regulatory and ethical standards designed to ensure safety, transparency, and fairness in healthcare. In the United States, the FDA has shown growing engagement with Al across domains such as digital health and drug development. In 2025, the FDA's Center for Drug Evaluation and Research released draft guidance titled 'Considerations for the Use of Artificial Intelligence to Support Regulatory Decision Making for Drug and Biological Products.' This guidance outlines a risk-based, trustworthy, and transparent framework for integrating Al into the drug life cycle and regulatory submissions.

Meanwhile, in the European Union (EU), the regulatory landscape has been reshaped by the EU Artificial Intelligence Act (https://artificialintelligenceact.eu), which introduces a tiered, risk-based classification for Al systems, including those in healthcare. High-risk Al

tools—such as those involved in diagnosis, treatment planning, or prognosis—will be required to meet strict criteria around transparency, human oversight, robustness, and accountability. Complementing this, the Ethics Guidelines for Trustworthy Al published in 2019 by the EU High-Level Expert Group on AI articulate seven core principles for ethical Al: human agency and oversight, technical robustness and safety, privacy and data governance, transparency, non-discrimination and fairness, societal and environmental well-being, and accountability. These guidelines serve as a blueprint for the responsible design, deployment, and auditing of Al systems in clinical practice.

## Drawbacks of Al models

While the majority of published Al studies report promising results, it is important to recognize that several limitations continue to hinder their clinical translation. Moreover, negative or failed Al models are rarely published, potentially introducing a publication bias. A primary challenge is the need for large, high-quality datasets to effectively train Al models, which can be particularly difficult to obtain in HCM. To address this, collaborative efforts among research institutions, hospitals, and patient registries can facilitate data pooling. Even when datasets are available, another critical limitation is the lack of model transparency and
reproducibility. Many published studies do not disclose their trained models, making it impossible to replicate or further exploit their results. As a result, researchers must retrain models from scratch, which requires access to the original data. For example, to address this, SMASH-HCM will generate a library of models, making them fully disclosed whenever possible, and will ensure that all shared data and tools comply with the FAIR principles (Findable, Accessible, Interoperable, and Reusable), promoting transparency, interoperability, and long-term usability across the research community. Additionally, it will develop a structured and accessible “surfable” encyclopaedia of current AI models, serving as a foundational knowledge base for researchers in the field.

An additional concern is data leakage,⁴⁷ where unintended information sharing between training and test sets, the use of unavailable predictors at inference time, or the inclusion of future data in training artificially inflates model performance. Biases—including selection bias, cherry-picking of results, overtraining, HARKing (hypothesizing after results are known), and confirmation bias—are common pitfalls in AI research. Ensuring transparent reporting, pre-registered study protocols, and independent validation on external datasets can help mitigate these issues. Overfitting remains another major limitation, where AI models perform well on training data but struggle to generalize to new, unseen cases. Implementing robust validation techniques, such as cross-validation or validation in external cohorts, can help mitigate this issue.

A practical drawback, particularly relevant for screening applications such as AI-ECG, is the high rate of false positives when applied in real-world or low-prevalence populations.⁹ This can lead to over-referral, increased healthcare workload, and unnecessary diagnostic procedures. AI models should be accompanied by appropriate clinical filtering tools or context-specific thresholds to reduce this unintended burden.

Finally, the real-world application of AI raises ethical questions, questions about trustworthiness (including fairness, bias, and robustness), and privacy concerns, necessitating the development of regulatory frameworks and guidelines to govern its use.⁴⁸,⁴⁹

## Future developments

Looking ahead, the integration of AI holds great potential to address many of the current limitations in HCM management.⁵⁰ Most AI models are still built on retrospective, single-centre data. This limits their generalizability, especially in underrepresented patient populations such as younger individuals, ethnic minorities, and those with atypical or non-sarcomeric forms of HCM. As shown in recent work,¹⁰ subgroup-specific performances should be explicitly evaluated to ensure fairness and avoid perpetuating healthcare disparities. Future efforts must focus on training and validating AI models in larger, multicentre, and globally diverse cohorts, with subgroup-specific performance reporting.

Importantly, the current dynamic landscape of HCM—particularly with the advent of CMIs—poses both challenges and opportunities for risk stratification. Although CMIs have demonstrated benefits in obstructive HCM, management of the non-obstructive form remains difficult. AI models could play a transformative role by accelerating the identification of responder subgroups, optimizing inclusion criteria, and informing the design of adaptive clinical trials. Furthermore, models trained on large, longitudinal cohorts may help prioritize surrogate endpoints for regulatory validation, improving the speed and precision of therapeutic development. AI could also improve our ability to identify early biomarkers, clinical features, or genetic profiles associated with progression to severe disease. For example, distinguishing between asymptomatic gene mutation carriers who may never develop clinical HCM and those at higher risk for symptomatic progression remains a major unmet need. Currently, all mutation carriers are monitored throughout life, regardless of risk. AI-based tools could enable

personalized follow-up strategies by stratifying carriers based on their predicted lifetime risk.

Beyond genomics, several other domains are rapidly evolving. AI-based imaging tools are being developed to detect subtle phenotypic markers, automate quantification, and track disease progression over time. ML approaches to genotype-phenotype correlations are beginning to uncover complex, nonlinear relationships that could inform prognosis, family screening, and personalized therapy. Meanwhile, wearable technologies and remote monitoring systems powered by AI offer a promising avenue for continuous, real-time data collection, enabling earlier identification of arrhythmic risk or decompensation in outpatient settings. At the frontier of these developments are digital twin technologies and in silico trials, which represent a novel paradigm for precision medicine. Although clinical validation is still ongoing, SMASH-HCM exemplifies the potential of hybrid models that merge data-driven and mechanistic approaches to develop a digital twin platform for HCM.

Crucially, real-world implementation of AI tools depends not only on technical performance but also on clinical trust, usability, and integration into existing workflows. Many healthcare professionals remain hesitant to adopt AI unless its outputs are interpretable and aligned with clinical needs. Therefore, clinicians should be actively involved in designing user interfaces, and close, iterative collaboration between AI developers and frontline physicians is essential to ensure adoption.

## Conclusions and learning points

The integration of AI into HCM management may represent a significant advancement in precision and personalized medicine, enhancing diagnostic accuracy, risk stratification, and treatment efficacy. AI-driven in silico trials, such as the SMASH-HCM digital twin platform, exemplify the potential of AI to integrate multiscale data, and biophysical simulations, for deep phenotyping, personalized risk assessment, and developing patient-specific treatment strategies. Advancing AI for HCM will require a shift from retrospective, one-size-fits-all models to dynamic, explainable, and equitable solutions that leverage both real-world data and computational simulation. These advancements, combined with an increasing number of disease-modifying therapies, may revolutionize the landscape of HCM management.

## Funding

The SMASH-HCM project receives funding from the Horizon Europe Health Programme under grant agreement number 101137115, by Innovate UK grants 10110728 and 10109598. This work was also supported by the Research Council of Finland.

**Conflict of interest:** F.A. is a significant shareholder in Pharmatics Limited. I.O. reported receiving research grants and personal fees from Cytokinetics, BMS, Tenaya, Lexeo, Rocket Pharma, Edgewise, and Sanofi Genzyme and grants from Menarini International, Amicus, and Chiesi. M.P. reported receiving personal fees from Bristol Myers Squibb.

## Data availability

No new data were generated or analysed in support of this research.

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