Genotype-Based Prediction of Functional Disease Progression in Duchenne Muscular Dystrophy Using AI-Supported Analysis

Background/Purpose: Duchenne muscular dystrophy (DMD) is a severe X-linked neuromuscular disorder with progressive muscle degeneration. Although its genetic basis is well characterized, the functional disease progression varies widely between individuals. This project aims to analyze the relationship between genotype (e.g., mutation type, location, and affected dystrophin isoforms) and functional decline. Based on these patterns, an artificial intelligence (AI)-based model will be developed to predict individual disease trajectories and support personalized care.

Methods: The study follows a two-phase approach. In phase one, pseudonymized clinical and genetic data from pediatric DMD patients at the Children's Hospital of Eastern Switzerland (St. Gallen) will be analyzed. Functional outcome measures include the North Star Ambulatory assessment, 6-minute walk test, performance of the upper limb, and time to rise. These will be linked to specific genotypic features. Machine learning models will be developed using Python-based tools such as scikit-learn, XGBoost, and PyTorch. Both supervised and unsupervised methods will be used to identify genotype-function associations and to predict individual disease trajectories. In phase two, the model will be validated using independent data from the Swiss Neuromuscular Disease Registry (Muskelregister Schweiz).

Results: Data analysis is currently ongoing. The aim is to identify predictive genotype-function associations and develop a prototype model to forecast functional progression based on genetic profiles.

Conclusion: This study highlights the potential of combining clinical data and genetic information to enhance personalized prognosis in DMD. It contributes to data-driven stratification and exemplifies the clinical relevance of AI-supported modeling in paediatric neurology and rare disease research.

Publication History

Article published online:
26 September 2025