ArticleNeuroImage. Clinical2022
Detection of lesions in the optic nerve with magnetic resonance imaging using a 3D convolutional neural network.
Article in NeuroImage. Clinical, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Whole anterior visual pathway segmentation from high-resolution MRI using artificial intelligence.European radiology experimental · 2026Article
- Multiple sclerosis clinical forms classification with graph convolutional networks based on brain morphological connectivity.Frontiers in neuroscience · 2023Article
- Explainable artificial intelligence (XAI) in radiology and nuclear medicine: a literature review.Frontiers in medicine · 2023Review
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12 authors.
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Abstract
backgroundOptic neuritis (ON) is one of the first manifestations of multiple sclerosis, a disabling disease with rising prevalence. Detecting optic nerve lesions could be a relevant diagnostic marker in patients with multiple sclerosis.
objectivesWe aim to create an automated, interpretable method for optic nerve lesion detection from MRI scans. MATERIALS AND
methodsWe present a 3D convolutional neural network (CNN) model that learns to detect optic nerve lesions based on T2-weighted fat-saturated MRI scans. We validated our system on two different datasets (N = 107 and 62) and interpreted the behaviour of the model using saliency maps.
resultsThe model showed good performance (68.11% balanced accuracy) that generalizes to unseen data (64.11%). The developed network focuses its attention to the areas that correspond to lesions in the optic nerve.
conclusionsThe method shows robustness and, when using only a single imaging sequence, its performance is not far from diagnosis by trained radiologists with the same constraint. Given its speed and performance, the developed methodology could serve as a first step to develop methods that could be translated into a clinical setting.
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