Machine learning outperforms human experts in MRI pattern analysis of muscular dystrophies

Affiliations

¹ From the Department of Neuromuscular Diseases (J.M.M.), Queen Square UCL Institute of Neurology, London, UK; and Department of Health Sciences (DISSAL) (M.P.S.), University of Genoa, and IRCCS Ospedale Policlinico San Martino, Italy. j.morrow@ucl.ac.uk.
² From the Department of Neuromuscular Diseases (J.M.M.), Queen Square UCL Institute of Neurology, London, UK; and Department of Health Sciences (DISSAL) (M.P.S.), University of Genoa, and IRCCS Ospedale Policlinico San Martino, Italy.

Editorial

Jasper M Morrow et al. Neurology. 2020.

. 2020 Mar 10;94(10):421-422.

doi: 10.1212/WNL.0000000000009053. Epub 2020 Feb 6.

¹ From the Department of Neuromuscular Diseases (J.M.M.), Queen Square UCL Institute of Neurology, London, UK; and Department of Health Sciences (DISSAL) (M.P.S.), University of Genoa, and IRCCS Ospedale Policlinico San Martino, Italy. j.morrow@ucl.ac.uk.
² From the Department of Neuromuscular Diseases (J.M.M.), Queen Square UCL Institute of Neurology, London, UK; and Department of Health Sciences (DISSAL) (M.P.S.), University of Genoa, and IRCCS Ospedale Policlinico San Martino, Italy.

No abstract available

Comment on

Accuracy of a machine learning muscle MRI-based tool for the diagnosis of muscular dystrophies.
Verdú-Díaz J, Alonso-Pérez J, Nuñez-Peralta C, Tasca G, Vissing J, Straub V, Fernández-Torrón R, Llauger J, Illa I, Díaz-Manera J. Verdú-Díaz J, et al. Neurology. 2020 Mar 10;94(10):e1094-e1102. doi: 10.1212/WNL.0000000000009068. Epub 2020 Feb 6. Neurology. 2020. PMID: 32029545