José Verdú-Díaz1, Jorge Alonso-Pérez1, Claudia Nuñez-Peralta1, Giorgio Tasca1, John Vissing1, Volker Straub1, Roberto Fernández-Torrón1, Jaume Llauger1, Isabel Illa1, Jordi Díaz-Manera2. 1. From the Neuromuscular Disorders Unit, Neurology Department (J.V.-D., J.A.-P., I.I., J.D.-M.), and Radiology Department (C.N.-P., J.L.), Hospital de la Santa Creu I Sant Pau, Barcelona, Spain; UOC di Neurologia (G.T.), Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy; Copenhagen Neuromuscular Center, Department of Neurology (J.V.), Rigshospitalet, University of Copenhagen, Denmark; John Walton Muscular Dystrophy Research Centre (V.S., J.D.-M.), University of Newcastle, Newcastle Upon Tyne, UK; Hospital Universitario Donostia (R.F.-T.); and Centro de Investigación Biomédica en Red en Enfermedades Raras (CIBERER) (I.I., J.D.-M.), Madrid, Spain. 2. From the Neuromuscular Disorders Unit, Neurology Department (J.V.-D., J.A.-P., I.I., J.D.-M.), and Radiology Department (C.N.-P., J.L.), Hospital de la Santa Creu I Sant Pau, Barcelona, Spain; UOC di Neurologia (G.T.), Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy; Copenhagen Neuromuscular Center, Department of Neurology (J.V.), Rigshospitalet, University of Copenhagen, Denmark; John Walton Muscular Dystrophy Research Centre (V.S., J.D.-M.), University of Newcastle, Newcastle Upon Tyne, UK; Hospital Universitario Donostia (R.F.-T.); and Centro de Investigación Biomédica en Red en Enfermedades Raras (CIBERER) (I.I., J.D.-M.), Madrid, Spain. jdiazm@santpau.cat.
Abstract
OBJECTIVE: Genetic diagnosis of muscular dystrophies (MDs) has classically been guided by clinical presentation, muscle biopsy, and muscle MRI data. Muscle MRI suggests diagnosis based on the pattern of muscle fatty replacement. However, patterns overlap between different disorders and knowledge about disease-specific patterns is limited. Our aim was to develop a software-based tool that can recognize muscle MRI patterns and thus aid diagnosis of MDs. METHODS: We collected 976 pelvic and lower limbs T1-weighted muscle MRIs from 10 different MDs. Fatty replacement was quantified using Mercuri score and files containing the numeric data were generated. Random forest supervised machine learning was applied to develop a model useful to identify the correct diagnosis. Two thousand different models were generated and the one with highest accuracy was selected. A new set of 20 MRIs was used to test the accuracy of the model, and the results were compared with diagnoses proposed by 4 specialists in the field. RESULTS: A total of 976 lower limbs MRIs from 10 different MDs were used. The best model obtained had 95.7% accuracy, with 92.1% sensitivity and 99.4% specificity. When compared with experts on the field, the diagnostic accuracy of the model generated was significantly higher in a new set of 20 MRIs. CONCLUSION: Machine learning can help doctors in the diagnosis of muscle dystrophies by analyzing patterns of muscle fatty replacement in muscle MRI. This tool can be helpful in daily clinics and in the interpretation of the results of next-generation sequencing tests. CLASSIFICATION OF EVIDENCE: This study provides Class II evidence that a muscle MRI-based artificial intelligence tool accurately diagnoses muscular dystrophies.
OBJECTIVE: Genetic diagnosis of muscular dystrophies (MDs) has classically been guided by clinical presentation, muscle biopsy, and muscle MRI data. Muscle MRI suggests diagnosis based on the pattern of muscle fatty replacement. However, patterns overlap between different disorders and knowledge about disease-specific patterns is limited. Our aim was to develop a software-based tool that can recognize muscle MRI patterns and thus aid diagnosis of MDs. METHODS: We collected 976 pelvic and lower limbs T1-weighted muscle MRIs from 10 different MDs. Fatty replacement was quantified using Mercuri score and files containing the numeric data were generated. Random forest supervised machine learning was applied to develop a model useful to identify the correct diagnosis. Two thousand different models were generated and the one with highest accuracy was selected. A new set of 20 MRIs was used to test the accuracy of the model, and the results were compared with diagnoses proposed by 4 specialists in the field. RESULTS: A total of 976 lower limbs MRIs from 10 different MDs were used. The best model obtained had 95.7% accuracy, with 92.1% sensitivity and 99.4% specificity. When compared with experts on the field, the diagnostic accuracy of the model generated was significantly higher in a new set of 20 MRIs. CONCLUSION: Machine learning can help doctors in the diagnosis of muscle dystrophies by analyzing patterns of muscle fatty replacement in muscle MRI. This tool can be helpful in daily clinics and in the interpretation of the results of next-generation sequencing tests. CLASSIFICATION OF EVIDENCE: This study provides Class II evidence that a muscle MRI-based artificial intelligence tool accurately diagnoses muscular dystrophies.
Authors: Laís Uyeda Aivazoglou; Julio Brandão Guimarães; Thomas M Link; Maria Alice Freitas Costa; Fabiano Nassar Cardoso; Bruno de Mattos Lombardi Badia; Igor Braga Farias; Wladimir Bocca Vieira de Rezende Pinto; Paulo Victor Sgobbi de Souza; Acary Souza Bulle Oliveira; Alzira Alves de Siqueira Carvalho; André Yui Aihara; Artur da Rocha Corrêa Fernandes Journal: Eur Radiol Date: 2021-04-21 Impact factor: 5.315
Authors: Cristina Domínguez-González; Roberto Fernández-Torrón; Ursula Moore; Carlos Pablo de Fuenmayor-Fernández de la Hoz; Beatriz Vélez-Gómez; Juan Antonio Cabezas; Jorge Alonso-Pérez; Laura González-Mera; Montse Olivé; Jorge García-García; Germán Moris; Juan Carlos León Hernández; Nuria Muelas; Emilia Servian-Morilla; Miguel A Martin; Jordi Díaz-Manera; Carmen Paradas Journal: J Neurol Date: 2022-03-14 Impact factor: 6.682