Myo‐Guide: A Machine Learning‐Based Web Application for Neuromuscular Disease Diagnosis With MRI
ABSTRACT Background 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. Musc...
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2025-06-01
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| Series: | Journal of Cachexia, Sarcopenia and Muscle |
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| Online Access: | https://doi.org/10.1002/jcsm.13815 |
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| author | Jose Verdu‐Diaz Carla Bolano‐Díaz 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 Domínguez‐González Laura Bermejo‐Guerrero David Gomez‐Andres Javier Sotoca Anna Pichiecchio Silvia Nicolosi Mauro Monforte Claudia Brogna Eugenio Mercuri Jorge Alfredo Bevilacqua Jorge Díaz‐Jara Benjamín Pizarro‐Galleguillos Peter Krkoska Jorge Alonso‐Pérez Montse Olivé 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‐Jiménez Robert Carlier Ali Asghar Okhovat Shahriar Nafissi Atchayaram Nalini Seena Vengalil Kieren Hollingsworth Chiara Marini‐Bettolo Volker Straub Giorgio Tasca Jaume Bacardit Jordi Díaz‐Manera the Myo‐Guide Consortium |
| author_facet | Jose Verdu‐Diaz Carla Bolano‐Díaz 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 Domínguez‐González Laura Bermejo‐Guerrero David Gomez‐Andres Javier Sotoca Anna Pichiecchio Silvia Nicolosi Mauro Monforte Claudia Brogna Eugenio Mercuri Jorge Alfredo Bevilacqua Jorge Díaz‐Jara Benjamín Pizarro‐Galleguillos Peter Krkoska Jorge Alonso‐Pérez Montse Olivé 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‐Jiménez Robert Carlier Ali Asghar Okhovat Shahriar Nafissi Atchayaram Nalini Seena Vengalil Kieren Hollingsworth Chiara Marini‐Bettolo Volker Straub Giorgio Tasca Jaume Bacardit Jordi Díaz‐Manera the Myo‐Guide Consortium |
| author_sort | Jose Verdu‐Diaz |
| collection | DOAJ |
| description | ABSTRACT Background 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. Methods 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. Results 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% ± 3.4%, with a weighted top‐3 accuracy of 84.7% ± 1.8% and top‐5 accuracy of 90.2% ± 2.4%. 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% ± 4.8%). The diagnostic tool has been implemented as a free web platform, providing global access to the medical community. Conclusions 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. |
| format | Article |
| id | doaj-art-95deb5d860de446d8fab8bf7e02bf760 |
| institution | OA Journals |
| issn | 2190-5991 2190-6009 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Cachexia, Sarcopenia and Muscle |
| spelling | doaj-art-95deb5d860de446d8fab8bf7e02bf7602025-08-20T02:20:39ZengWileyJournal of Cachexia, Sarcopenia and Muscle2190-59912190-60092025-06-01163n/an/a10.1002/jcsm.13815Myo‐Guide: A Machine Learning‐Based Web Application for Neuromuscular Disease Diagnosis With MRIJose Verdu‐Diaz0Carla Bolano‐Díaz1Alejandro Gonzalez‐Chamorro2Sam Fitzsimmons3Jodi Warman‐Chardon4Goknur Selen Kocak5Debora Mucida‐Alvim6Ian C. Smith7John Vissing8Nanna Scharff Poulsen9Sushan Luo10Cristina Domínguez‐González11Laura Bermejo‐Guerrero12David Gomez‐Andres13Javier Sotoca14Anna Pichiecchio15Silvia Nicolosi16Mauro Monforte17Claudia Brogna18Eugenio Mercuri19Jorge Alfredo Bevilacqua20Jorge Díaz‐Jara21Benjamín Pizarro‐Galleguillos22Peter Krkoska23Jorge Alonso‐Pérez24Montse Olivé25Erik H. Niks26Hermien E. Kan27James Lilleker28Mark Roberts29Bianca Buchignani30Jinhong Shin31Florence Esselin32Emmanuelle Le Bars33Anne Marie Childs34Edoardo Malfatti35Anna Sarkozy36Luke Perry37Sniya Sudhakar38Edmar Zanoteli39Filipe Tupinamba Di Pace40Emma Matthews41Shahram Attarian42David Bendahan43Matteo Garibaldi44Laura Fionda45Alicia Alonso‐Jiménez46Robert Carlier47Ali Asghar Okhovat48Shahriar Nafissi49Atchayaram Nalini50Seena Vengalil51Kieren Hollingsworth52Chiara Marini‐Bettolo53Volker Straub54Giorgio Tasca55Jaume Bacardit56Jordi Díaz‐Manera57the Myo‐Guide ConsortiumJohn Walton Muscular Dystrophy Research Centre Newcastle University Newcastle upon Tyne UKJohn Walton Muscular Dystrophy Research Centre Newcastle University Newcastle upon Tyne UKJohn Walton Muscular Dystrophy Research Centre Newcastle University Newcastle upon Tyne UKJohn Walton Muscular Dystrophy Research Centre Newcastle University Newcastle upon Tyne UKDepartment of Medicine (Neurology) The Ottawa Hospital Ottawa CanadaJohn Walton Muscular Dystrophy Research Centre Newcastle University Newcastle upon Tyne UKJohn Walton Muscular Dystrophy Research Centre Newcastle University Newcastle upon Tyne UKOttawa Hospital Research Institute Ottawa CanadaCopenhagen Neuromuscular Centre, Rigshospitalet Copenhagen University Hospital Copenhagen DenmarkCopenhagen Neuromuscular Centre, Rigshospitalet Copenhagen University Hospital Copenhagen DenmarkDepartment of Neurology, Huashan Hospital Fudan University Shanghai ChinaNeuromuscular Disorders Unit, Neurology Department Hospital 12 de Octubre Madrid SpainNeuromuscular Disorders Unit, Neurology Department Hospital 12 de Octubre Madrid SpainHospital Universitari Vall d'Hebron Barcelona SpainNeuromuscular Disorders Unit, Neurology Department Hospital Universitari Vall d'Hebron Barcelona SpainDepartment of Brain and Behavioural Sciences University of Pavia Pavia ItalyUniversity of Pavia; Mondino IRCCS Foundation Pavia ItalyUOC di Neurologia Fondazione Policlinico Universitario Agostino Gemelli IRCCS Rome ItalyFondazione Policlinico Universitario Agostino Gemelli Rome ItalyPediatric Neurology, Department of Woman and Child Health and Public Health, Child Health Area Università Cattolica del Sacro Cuore Rome ItalyHospital Clínico Universidad de Chile Santiago de Chile ChileHospital Clínico Universidad de Chile Santiago de Chile ChilePrograma de Doctorado en Ciencias Médicas y Especialidad Escuela de Postgrado Facultad de Medicina Universidad de Chile Santiago ChileUniversity Hospital Brno Brno Czech RepublicNeuromuscular Disease Unit, Neurology Department Hospital Universitario Nuestra Señora de Candelaria Tenerife SpainNeuromuscular Disorders Unit, Department of Neurology Hospital de la Santa Creu i Sant Pau Barcelona SpainDepartment of Neurology Leiden University Medical Center Leiden The NetherlandsC.J. Gorter MRI Center, Department of Radiology Leiden University Medical Center Leiden The NetherlandsNorthern Care Alliance NHS Foundation Trust Manchester UKNorthern Care Alliance NHS Foundation Trust Manchester UKDepartment of Translational Research and of New Surgical and Medical Technologies University of Pisa Pisa ItalyDepartment of Neurology Pusan National University School of Medicine Busan Republic of KoreaCentre de Référence des Maladies du Motoneurone, Department of Neurology Montpellier University Hospital Montpellier FranceDepartment of Neuroradiology, I2FH Platform Montpellier University Hospital Montpellier FranceLeeds Teaching Hospitals NHS Trust Leeds UKParis Est University, APHP Henri‐Mondor University Hospital Créteil FranceDubowitz Neuromuscular Centre UCL Great Ormond Street Institute of Child Health & Great Ormond Street Hospital London UKDubowitz Neuromuscular Centre UCL Great Ormond Street Institute of Child Health & Great Ormond Street Hospital London UKDepartment of Neuroradiology Great Ormond Street Hospital for Children NHS Foundation Trust London UKDepartment of Neurology Faculdade de Medicina da Universidade de São Paulo (FMUSP) São Paulo BrazilDepartment of Neurology Faculdade de Medicina da Universidade de São Paulo (FMUSP) São Paulo BrazilSt George's University and St George's University Hospitals NHS Foundation Trust London UKReference Center for Neuromuscular Disorders CHU La Timone, Aix‐Marseille University Marseille FranceAix‐Marseille University, CRMBM, CNRS UMR 7339 Marseille FranceDepartment of Neuroscience, Mental Health and Sensory Organs (NESMOS) SAPIENZA University of Rome Rome ItalyNeuromuscular and Rare Disease Centre, Neurology Unit, Sant'Andrea Hospital Rome ItalyNeuromuscular Reference Center, Department of Neurology, Universitair Ziekenhuis van Antwerpen Universiteit Antwerpen Antwerp BelgiumUniversity Hospital Raymond‐Poincaré Garches FranceNeurology Department, Shariati Hospital, Neuromuscular Research Center Tehran University of Medical Sciences Tehran IranNeurology Department, Shariati Hospital, Neuromuscular Research Center Tehran University of Medical Sciences Tehran IranNational Institute of Mental Health and Neurosciences (NIMHANS) Bengaluru IndiaNational Institute of Mental Health and Neurosciences (NIMHANS) Bengaluru IndiaTranslational and Clinical Research Institute Newcastle University Newcastle upon Tyne UKJohn Walton Muscular Dystrophy Research Centre Newcastle University Newcastle upon Tyne UKJohn Walton Muscular Dystrophy Research Centre Newcastle University Newcastle upon Tyne UKJohn Walton Muscular Dystrophy Research Centre Newcastle University Newcastle upon Tyne UKInterdisciplinary Computing and Complex BioSystems (ICOS) Research Group, School of Computing Newcastle University Newcastle upon Tyne UKJohn Walton Muscular Dystrophy Research Centre Newcastle University Newcastle upon Tyne UKABSTRACT Background 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. Methods 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. Results 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% ± 3.4%, with a weighted top‐3 accuracy of 84.7% ± 1.8% and top‐5 accuracy of 90.2% ± 2.4%. 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% ± 4.8%). The diagnostic tool has been implemented as a free web platform, providing global access to the medical community. Conclusions 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.https://doi.org/10.1002/jcsm.13815artificial intelligencedifferential diagnosismachine learningMRIneuromuscular diseases |
| spellingShingle | Jose Verdu‐Diaz Carla Bolano‐Díaz 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 Domínguez‐González Laura Bermejo‐Guerrero David Gomez‐Andres Javier Sotoca Anna Pichiecchio Silvia Nicolosi Mauro Monforte Claudia Brogna Eugenio Mercuri Jorge Alfredo Bevilacqua Jorge Díaz‐Jara Benjamín Pizarro‐Galleguillos Peter Krkoska Jorge Alonso‐Pérez Montse Olivé 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‐Jiménez Robert Carlier Ali Asghar Okhovat Shahriar Nafissi Atchayaram Nalini Seena Vengalil Kieren Hollingsworth Chiara Marini‐Bettolo Volker Straub Giorgio Tasca Jaume Bacardit Jordi Díaz‐Manera the Myo‐Guide Consortium Myo‐Guide: A Machine Learning‐Based Web Application for Neuromuscular Disease Diagnosis With MRI Journal of Cachexia, Sarcopenia and Muscle artificial intelligence differential diagnosis machine learning MRI neuromuscular diseases |
| title | Myo‐Guide: A Machine Learning‐Based Web Application for Neuromuscular Disease Diagnosis With MRI |
| title_full | Myo‐Guide: A Machine Learning‐Based Web Application for Neuromuscular Disease Diagnosis With MRI |
| title_fullStr | Myo‐Guide: A Machine Learning‐Based Web Application for Neuromuscular Disease Diagnosis With MRI |
| title_full_unstemmed | Myo‐Guide: A Machine Learning‐Based Web Application for Neuromuscular Disease Diagnosis With MRI |
| title_short | Myo‐Guide: A Machine Learning‐Based Web Application for Neuromuscular Disease Diagnosis With MRI |
| title_sort | myo guide a machine learning based web application for neuromuscular disease diagnosis with mri |
| topic | artificial intelligence differential diagnosis machine learning MRI neuromuscular diseases |
| url | https://doi.org/10.1002/jcsm.13815 |
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