Convolutional neural network-based classification of glaucoma using optic radiation tissue properties.

Autor: Kruper J; Department of Psychology, University of Washington, Seattle, WA, USA.; eScience Institute, University of Washington, Seattle, WA, USA., Richie-Halford A; Graduate School of Education and Division of Developmental Behavioral Pediatrics, Stanford University, Stanford, CA, USA., Benson NC; eScience Institute, University of Washington, Seattle, WA, USA., Caffarra S; Graduate School of Education and Division of Developmental Behavioral Pediatrics, Stanford University, Stanford, CA, USA.; University of Modena and Reggio Emilia, Modena, Italy., Owen J; Department of Ophthalmology, University of Washington, Seattle, WA, USA.; Roger and Angie Karalis Johnson Retina Center, Seattle, WA, USA., Wu Y; Department of Ophthalmology, University of Washington, Seattle, WA, USA.; Roger and Angie Karalis Johnson Retina Center, Seattle, WA, USA., Egan C; Moorfields Eye Hospital, NHS Trust, London, UK., Lee AY; Department of Ophthalmology, University of Washington, Seattle, WA, USA.; Roger and Angie Karalis Johnson Retina Center, Seattle, WA, USA., Lee CS; Department of Ophthalmology, University of Washington, Seattle, WA, USA.; Roger and Angie Karalis Johnson Retina Center, Seattle, WA, USA., Yeatman JD; Graduate School of Education and Division of Developmental Behavioral Pediatrics, Stanford University, Stanford, CA, USA., Rokem A; Department of Psychology, University of Washington, Seattle, WA, USA. arokem@uw.edu.; eScience Institute, University of Washington, Seattle, WA, USA. arokem@uw.edu.
Jazyk: angličtina
Zdroj: Communications medicine [Commun Med (Lond)] 2024 Apr 11; Vol. 4 (1), pp. 72. Date of Electronic Publication: 2024 Apr 11.
DOI: 10.1038/s43856-024-00496-w
Abstrakt: Background: Sensory 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.
Methods: We 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.
Results: We 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.
Conclusions: Our 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.
(© 2024. The Author(s).)
Databáze: MEDLINE
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