ArticleCommunications medicine2024
Convolutional neural network-based classification of glaucoma using optic radiation tissue properties.
Article in Communications medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Application of advanced diffusion MRI based tractometry of the visual pathway in glaucoma: a systematic review.Frontiers in neuroscience · 2025Pooled it
- Deep learning and eye tracking: Convolutional neural networks provide converging evidence for experience-driven attention within visual search.Behavior research methods · 2026Article
- Article
- A software ecosystem for brain tractometry processing, analysis, and insight.PLoS computational biology · 2025Article
- Change in Mechanical Property of Rat Brain Suffering from Chronic High Intraocular Pressure.Bioengineering (Basel, Switzerland) · 2025Article
- Article
- Tractometry of Human Visual White Matter Pathways in Health and Disease.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2024Review
- Contributions of Brain Microstructures and Metabolism to Visual Field Loss Patterns in Glaucoma Using Archetypal and Information Gain Analyses.Investigative ophthalmology & visual science · 2024Article
- Convolutional neural network-based classification of glaucoma using optic radiation tissue properties.Communications medicine · 2024Article
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
backgroundSensory changes due to aging or disease can impact brain tissue. This study aims to investigate the link between glaucoma, a leading cause of blindness, and alterations in brain connections.
methodsWe analyzed diffusion MRI measurements of white matter tissue in a large group, consisting of 905 glaucoma patients (aged 49-80) and 5292 healthy individuals (aged 45-80) from the UK Biobank. Confounds due to group differences were mitigated by matching a sub-sample of controls to glaucoma subjects. We compared classification of glaucoma using convolutional neural networks (CNNs) focusing on the optic radiations, which are the primary visual connection to the cortex, against those analyzing non-visual brain connections. As a control, we evaluated the performance of regularized linear regression models.
resultsWe showed that CNNs using information from the optic radiations exhibited higher accuracy in classifying subjects with glaucoma when contrasted with CNNs relying on information from non-visual brain connections. Regularized linear regression models were also tested, and showed significantly weaker classification performance. Additionally, the CNN was unable to generalize to the classification of age-group or of age-related macular degeneration.
conclusionsOur findings indicate a distinct and potentially non-linear signature of glaucoma in the tissue properties of optic radiations. This study enhances our understanding of how glaucoma affects brain tissue and opens avenues for further research into how diseases that affect sensory input may also affect brain aging.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.