Tehran University of Medical Sciences

Science Communicator Platform

Stay connected! Follow us on X network (Twitter):
Share this content! On (X network) By
Myo-Guide: A Machine Learning-Based Web Application for Neuromuscular Disease Diagnosis With Mri Publisher Pubmed



Verdudiaz J1 ; Bolanodiaz C1 ; Gonzalezchamorro A1 ; Fitzsimmons S1 ; Warmanchardon J2, 3 ; Kocak G1 ; Mucidaalvim D1 ; Smith I4 ; Vissing J5 ; Poulsen N5 ; Luo S6 ; Dominguezgonzalez C7 ; Bermejoguerrero L7 ; Gomezandres D8 Show All Authors
Authors
  1. Verdudiaz J1
  2. Bolanodiaz C1
  3. Gonzalezchamorro A1
  4. Fitzsimmons S1
  5. Warmanchardon J2, 3
  6. Kocak G1
  7. Mucidaalvim D1
  8. Smith I4
  9. Vissing J5
  10. Poulsen N5
  11. Luo S6
  12. Dominguezgonzalez C7
  13. Bermejoguerrero L7
  14. Gomezandres D8
  15. Sotoca J9
  16. Pichiecchio A10, 11
  17. Nicolosi S12
  18. Monforte M14
  19. Brogna C15
  20. Mercuri E16
  21. Bevilacqua J17
  22. Diazjara J17
  23. Pizarrogalleguillos B18
  24. Krkoska P19
  25. Alonsoperez J20
  26. Olive M21, 22, 23
  27. Niks E24
  28. Kan H25
  29. Lilleker J26
  30. Roberts M26
  31. Buchignani B27
  32. Shin J28
  33. Esselin F29
  34. Lebars E30
  35. Childs A31
  36. Malfatti E32
  37. Sarkozy A33
  38. Perry L33
  39. Sudhakar S34
  40. Zanoteli E35
  41. Dipace F35
  42. Matthews E36
  43. Attarian S37
  44. Bendahan D38
  45. Garibaldi M39
  46. Fionda L40
  47. Alonsojimenez A41
  48. Carlier R42
  49. Okhovat A43
  50. Nafissi S43
  51. Nalini A44
  52. Vengalil S44
  53. Hollingsworth K45
  54. Marinibettolo C1
  55. Straub V1
  56. Tasca G1
  57. Bacardit J46
  58. Diazmanera J1
Show Affiliations
Authors Affiliations
  1. 1. John Walton Muscular Dystrophy Research Centre, Newcastle University, Newcastle upon Tyne, United Kingdom
  2. 2. Department of Medicine (Neurology), The Ottawa Hospital, Ottawa, Canada
  3. 3. Department of Genetics, Children's Hospital of Eastern Ontario, Ottawa, Canada
  4. 4. Ottawa Hospital Research Institute, Ottawa, Canada
  5. 5. Copenhagen Neuromuscular Centre, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
  6. 6. Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China
  7. 7. Neuromuscular Disorders Unit, Neurology Department, Hospital 12 de Octubre, Madrid, Spain
  8. 8. Hospital Universitari Vall d'Hebron, Barcelona, Spain
  9. 9. Neuromuscular Disorders Unit, Neurology Department, Hospital Universitari Vall d'Hebron, Barcelona, Spain
  10. 10. Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy
  11. 11. Advanced Imaging and AI Center, Mondino IRCCS Foundation, Pavia, Italy
  12. 12. University of Pavia
  13. 13. Mondino IRCCS Foundation, Pavia, Italy
  14. 14. UOC di Neurologia, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
  15. 15. Fondazione Policlinico Universitario Agostino Gemelli, Rome, Italy
  16. 16. Pediatric Neurology, Department of Woman and Child Health and Public Health, Child Health Area, Universita Cattolica del Sacro Cuore, Rome, Italy
  17. 17. Hospital Clinico Universidad de Chile, Santiago de Chile, Chile
  18. 18. Programa de Doctorado en Ciencias Medicas y Especialidad, Escuela de Postgrado Facultad de Medicina Universidad de Chile, Santiago, Chile
  19. 19. University Hospital Brno, Brno, Czech Republic
  20. 20. Neuromuscular Disease Unit, Neurology Department, Hospital Universitario Nuestra Senora de Candelaria, Tenerife, Spain
  21. 21. Neuromuscular Disorders Unit, Department of Neurology, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain
  22. 22. Biomedical Research Institute Sant Pau (IIB Sant Pau), Barcelona, Spain
  23. 23. Centro de Investigaciones Biomedicas en Red en Enfermedades Raras (CIBERER), Madrid, Spain
  24. 24. Department of Neurology, Leiden University Medical Center, Leiden, Netherlands
  25. 25. C.J. Gorter MRI Center, Department of Radiology, Leiden University Medical Center, Leiden, Netherlands
  26. 26. Northern Care Alliance NHS Foundation Trust, Manchester, United Kingdom
  27. 27. Department of Translational Research and of New Surgical and Medical Technologies, University of Pisa, Pisa, Italy
  28. 28. Department of Neurology, Pusan National University School of Medicine, Busan, South Korea
  29. 29. Centre de Reference des Maladies du Motoneurone, Department of Neurology, Montpellier University Hospital, Montpellier, France
  30. 30. Department of Neuroradiology, I2FH Platform, Montpellier University Hospital, Montpellier, France
  31. 31. Leeds Teaching Hospitals NHS Trust, Leeds, United Kingdom
  32. 32. Paris Est University, APHP Henri-Mondor University Hospital, Creteil, France
  33. 33. Dubowitz Neuromuscular Centre, UCL Great Ormond Street Institute of Child Health & Great Ormond Street Hospital, London, United Kingdom
  34. 34. Department of Neuroradiology, Great Ormond Street Hospital for Children NHS Foundation Trust, London, United Kingdom
  35. 35. Department of Neurology, Faculdade de Medicina da Universidade de Sao Paulo (FMUSP), Sao Paulo, Brazil
  36. 36. St George's University and St George's University Hospitals NHS Foundation Trust, London, United Kingdom
  37. 37. Reference Center for Neuromuscular Disorders CHU La Timone, Aix-Marseille University, Marseille, France
  38. 38. Aix-Marseille University, CRMBM, CNRS UMR 7339, Marseille, France
  39. 39. Department of Neuroscience, Mental Health and Sensory Organs (NESMOS), SAPIENZA University of Rome, Rome, Italy
  40. 40. Neuromuscular and Rare Disease Centre, Neurology Unit, Sant'Andrea Hospital, Rome, Italy
  41. 41. Neuromuscular Reference Center, Department of Neurology, Universitair Ziekenhuis van Antwerpen, Universiteit Antwerpen, Antwerp, Belgium
  42. 42. University Hospital Raymond-Poincare, Garches, France
  43. 43. Neurology Department, Shariati Hospital, Neuromuscular Research Center, Tehran University of Medical Sciences, Tehran, Iran
  44. 44. National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, India
  45. 45. Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, United Kingdom
  46. 46. Interdisciplinary Computing and Complex BioSystems (ICOS) Research Group, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom

Source: Journal of Cachexia, Sarcopenia and Muscle Published:2025


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. © 2025 The Author(s). Journal of Cachexia, Sarcopenia and Muscle published by Wiley Periodicals LLC.
Related Docs
1. Mri Biomarkers for Memory-Related Impairment in Amyotrophic Lateral Sclerosis: A Systematic Review, Amyotrophic Lateral Sclerosis and Frontotemporal Degeneration (2023)
2. Thigh and Leg Muscle Mri Findings in Gne Myopathy, Journal of Neuromuscular Diseases (2021)
Experts (# of related papers)