๐ค Authors: Jose Verdu-Diaz, Carla Bolano-Dรญaz, Alejandro Gonzalez-Chamorro, Sam Fitzsimmons, Jodi Warman-Chardon, Goknur Selen Kocak, Debora Mucida-Alvim, Ian C Smith, John Vissing, Nanna Scharff Poulsen, Sushan Luo, Cristina Domรญnguez-Gonzรกlez, Laura Bermejo-Guerrero, David Gomez-Andres, Javier Sotoca, Anna Pichiecchio, Silvia Nicolosi, Mauro Monforte, Claudia Brogna, Eugenio Mercuri, Jorge Alfredo Bevilacqua, Jorge Dรญaz-Jara, Benjamรญn Pizarro-Galleguillos, Peter Krkoska, Jorge Alonso-Pรฉrez, Montse Olivรฉ, Erik H Niks, Hermien E Kan, James Lilleker, Mark Roberts, Bianca Buchignani, Jinhong Shin, Florence Esselin, Emmanuelle Le Bars, Anne Marie Childs, Edoardo Malfatti, Anna Sarkozy, Luke Perry, Sniya Sudhakar, Edmar Zanoteli, Filipe Tupinamba Di Pace, Emma Matthews, Shahram Attarian, David Bendahan, Matteo Garibaldi, Laura Fionda, Alicia Alonso-Jimรฉnez, Robert Carlier, Ali Asghar Okhovat, Shahriar Nafissi, Atchayaram Nalini, Seena Vengalil, Kieren Hollingsworth, Chiara Marini-Bettolo, Volker Straub, Giorgio Tasca, Jaume Bacardit, Jordi Dรญaz-Manera
Myo-Guide: A Machine Learning-Based Web Application for Neuromuscular Disease Diagnosis With MRI.
Neuromuscular diseases (NMDs) are rare disorders characterized by progressive muscle fibre loss, leading to replacement by fibrotic and fatty tissue, muscle weakness and disability. Early diagnosis is critical for therapeutic decisions, care planning and genetic counselling.
Muscle magnetic resonance imaging (MRI) has emerged as a valuable diagnostic tool by identifying characteristic patterns of muscle involvement. However, the increasing complexity of these patterns complicates their interpretation, limiting their clinical utility.
Additionally, multi-study data aggregation introduces heterogeneity challenges. This study presents a novel multi-study harmonization pipeline for muscle MRI and an AI-driven diagnostic tool to assist clinicians in identifying disease-specific muscle involvement patterns.
We developed a preprocessing pipeline to standardize MRI fat content across datasets, minimizing source bias. An ensemble of XGBoost models was trained to classify patients based on intramuscular fat replacement, age at MRI and sex.
The SHapley Additive exPlanations (SHAP) framework was adapted to analyse model predictions and identify disease-specific muscle involvement patterns. To address class imbalance, training and evaluation were conducted using class-balanced metrics.
The model's performance was compared against four expert clinicians using 14 previously unseen MRI scans. Using our harmonization approach, we curated a dataset of 2961 MRI samples from genetically confirmed cases of 20 paediatric and adult NMDs.
The model achieved a balanced accuracy of 64.8%โยฑโ3.4%, with a weighted top-3 accuracy of 84.7%โยฑโ1.8% and top-5 accuracy of 90.2%โยฑโ2.4%. It also identified key features relevant for differential diagnosis, aiding clinical decision-making.
Compared to four expert clinicians, the model obtained the highest top-3 accuracy (75.0%โยฑโ4.8%). The diagnostic tool has been implemented as a free web platform, providing global access to the medical community.
The application of AI in muscle MRI for NMD diagnosis remains underexplored due to data scarcity. This study introduces a framework for dataset harmonization, enabling advanced computational techniques.
Our findings demonstrate the potential of AI-based approaches to enhance differential diagnosis by identifying disease-specific muscle involvement patterns. The developed tool surpasses expert performance in diagnostic ranking and is accessible to clinicians worldwide via the Myo-Guide online platform.
