Training Together, Diagnosing Better: Federated Learning for Collagen VI-Related Dystrophies

TL;DR

This paper demonstrates that federated learning can improve the accuracy and generalizability of diagnosing collagen VI-related dystrophies from microscopy images across multiple institutions, while preserving patient privacy.

Contribution

The study introduces a novel federated learning approach for diagnosing rare diseases using decentralized datasets, achieving higher accuracy than traditional models.

Findings

F1-score of 0.82 for federated model

Outperforms single-organization models (0.57-0.75)

Enables privacy-preserving collaborative diagnosis

Abstract

The application of Machine Learning (ML) to the diagnosis of rare diseases, such as collagen VI-related dystrophies (COL6-RD), is fundamentally limited by the scarcity and fragmentation of available data. Attempts to expand sampling across hospitals, institutions, or countries with differing regulations face severe privacy, regulatory, and logistical obstacles that are often difficult to overcome. The Federated Learning (FL) provides a promising solution by enabling collaborative model training across decentralized datasets while keeping patient data local and private. Here, we report a novel global FL initiative using the Sherpa.ai FL platform, which leverages FL across distributed datasets in two international organizations for the diagnosis of COL6-RD, using collagen VI immunofluorescence microscopy images from patient-derived fibroblast cultures. Our solution resulted in an ML model capable of classifying collagen VI patient images into the three primary pathogenic mechanism groups associated with COL6-RD: exon skipping, glycine substitution, and pseudoexon insertion. This new approach achieved an F1-score of 0.82, outperforming single-organization models (0.57-0.75). These results demonstrate that FL substantially improves diagnostic utility and generalizability compared to isolated institutional models. Beyond enabling more accurate diagnosis, we anticipate that this approach will support the interpretation of variants of uncertain significance and guide the prioritization of sequencing strategies to identify novel pathogenic variants.