Left ventricular systolic dysfunction screening in muscular dystrophies using deep learning-based electrocardiogram interpretation.

BACKGROUND

Routine echocardiographic monitoring is recommended in muscular dystrophy patients to detect left ventricular systolic dysfunction (LVSD) but is often challenging due to physical limitations. This study evaluates whether artificial intelligence-based electrocardiogram interpretation (AI-ECG) can detect and predict LVSD in muscular dystrophy patients.

METHODS

Patients aged >16 years who underwent an ECG and echocardiogram within 90 days at the University Medical Center Utrecht were included. Patients with Duchenne (DMD), Becker (BMD), limb-girdle muscular dystrophy (LGMD). myotonic dystrophy (MD), and female DMD/BMD carriers, were identified. A convolutional neural network (CNN) was trained on a derivation cohort of patients without muscular dystrophy to detect LVSD and tested on muscular dystrophy patients. A Cox proportional hazards model assessed AI-ECG's predictive value for new-onset LVSD.

RESULTS

The derivation cohort included 53,874 ECG-echocardiogram pairs from 30,978 patients, while the muscular dystrophy test set comprised 390 ECG-echo pairs from 390 patients. LVSD prevalence varied from 81.3 % in DMD to 13.4 % in MD. The model achieved an AUROC of 0.83 (0.79-0.87) in the muscular dystrophy test set, with sensitivity 0.87 (0.81-0.93), specificity 0.58 (0.52-0.63), NPV 0.91 (0.86-0.95), and PPV 0.49 (0.43-0.56). AI-ECG predicted new-onset LVSD with an AUROC of 0.72 (0.66-0.78), with AI-ECG probability being a significant predictor.

CONCLUSIONS

AI-ECG can detect LVSD in muscular dystrophy patients, offering a non-invasive, accessible tool for risk stratification and an alternative to routine echocardiography. It may also predict new-onset LVSD, enabling earlier intervention. Further research should explore external validation, pediatric application, and integration within the clinical care plan.