📇 Rhumatologue ·

MME Marie-Claude ROCCA

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Auteur de référence en rhumatologie
24 articles scientifiques publiés — un praticien à la pointe de la recherche

Rhumatologue

RPPS 10000870351

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5 publications sur 5 ans

📊 Reconnaissance scientifique : 2/100📝 11 articles publiés

✨ Génération du profil synthétique IA en cours…

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Indicateurs publics agrégés sur 250 M+ d'œuvres scientifiques (OpenAlex, PubMed). Traduits ici en langage patient.

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Données ANS publiques (Licence Ouverte 2.0) · Enrichissements MonRhumato 100 % opt-in · Toute personne référencée peut demander la suppression ou la rectification.

Top publications · les plus citées

Deep Learning on Conventional Magnetic Resonance Imaging Improves the Diagnosis of Multiple Sclerosis Mimics

Investigative radiology · 2021

📚 34 citations🎯 RCR 2.87Top 17% NIH

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Objectives The aims of this study were to present a deep learning approach for the automated classification of multiple sclerosis and its mimics and compare model performance with that of 2 expert neuroradiologists. Materials and Methods A total of 268 T2-weighted and T1-weighted brain magnetic resonance imagin scans were retrospectively collected from patients with migraine (n = 56), multiple sclerosis (n = 70), neuromyelitis optica spectrum disorders (n = 91), and central nervous system vasculitis (n = 51). The neural network architecture, trained on 178 scans, was based on a cascade of 4 three-dimensional convolutional layers, followed by a fully dense layer after feature extraction. The ability of the final algorithm to correctly classify the diseases in an independent test set of 90 scans was compared with that of the neuroradiologists. Results The interrater agreement was 84.9% (Cohen κ = 0.78, P < 0.001). In the test set, deep learning and expert raters reached the highest diagnostic accuracy in multiple sclerosis (98.8% vs 72.8%, P < 0.001, for rater 1; and 81.8%, P < 0.001, for rater 2) and the lowest in neuromyelitis optica spectrum disorders (88.6% vs 4.4%, P < 0.001, for both raters), whereas they achieved intermediate values for migraine (92.2% vs 53%, P = 0.03, for rater 1; and 64.8%, P = 0.01, for rater 2) and vasculitis (92.1% vs 54.6%, P = 0.3, for rater 1; and 45.5%, P = 0.2, for rater 2). The overall performance of the automated method exceeded that of expert raters, with the worst misdiagnosis when discriminating between neuromyelitis optica spectrum disorders and vasculitis or migraine. Conclusions A neural network performed better than expert raters in terms of accuracy in classifying white matter disorders from magnetic resonance imaging and may help in their diagnostic work-up.

Resting state network functional connectivity abnormalities in systemic lupus erythematosus: correlations with neuropsychiatric impairment

Molecular psychiatry · 2021

📚 33 citations🎯 RCR 2.79Top 18% NIH

An fMRI study of the motor system in patients with neuropsychiatric systemic lupus erythematosus

NeuroImage · 2006

📚 29 citations

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MME Marie-Claude ROCCA

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Publications scientifiques (24) — classées par pathologie

Transversal16

Lupus5

IA en rhumatologie1

IRM ostéo-articulaire1

Pédiatrie1

Santé mentale / fatigue1

Tendinopathies & SCC1

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