ArticleFrontiers in neuroscience2021
Deep Learning-Based Detection of Malformed Optic Chiasms From MRI Images.
Article in Frontiers in neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed, 7 citations in OpenAlex.
- Phenotypic Features Determining Visual Acuity in Albinism and the Role of Amblyogenic Factors.Investigative ophthalmology & visual science · 2024Article
- Visual Field Deficits in Albinism in Comparison to Idiopathic Infantile Nystagmus.Investigative ophthalmology & visual science · 2024Article
- CHIASM-Net: Artificial Intelligence-Based Direct Identification of Chiasmal Abnormalities in Albinism.Investigative ophthalmology & visual science · 2023Article
- Computational approaches for the reconstruction of optic nerve fibers along the visual pathway from medical images: a comprehensive review.Frontiers in neuroscience · 2023Review
- Optical Coherence Tomography as a Biomarker for Differential Diagnostics in Nystagmus: Ganglion Cell Layer Thickness Ratio.Journal of clinical medicine · 2022Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Convolutional neural network (CNN) models are of great promise to aid the segmentation and analysis of brain structures. Here, we tested whether CNN trained to segment normal optic chiasms from the T1w magnetic resonance imaging (MRI) image can be also applied to abnormal chiasms, specifically with optic nerve misrouting as typical for human albinism. We performed supervised training of the CNN on the T1w images of control participants (
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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.