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CHIASM-NetArtificial Intelligence-Based Direct Identification of Chiasmal Abnormalities in Albinism..pdf (2.34 MB)

CHIASM-Net: Artificial Intelligence-Based Direct Identification of Chiasmal Abnormalities in Albinism

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posted on 2023-11-08, 16:21 authored by Robert J Puzniak, Gokulraj T Prabhakaran, Rebecca J McLean, Sebastian Stober, Sarim Ather, Frank A Proudlock, Irene Gottlob, Robert A Dineen, Michael B Hoffmann

Purpose: Albinism is a congenital disorder affecting pigmentation levels, structure, and function of the visual system. The identification of anatomical changes typical for people with albinism (PWA), such as optic chiasm malformations, could become an important component of diagnostics. Here, we tested an application of convolutional neural networks (CNNs) for this purpose.


Methods: We established and evaluated a CNN, referred to as CHIASM-Net, for the detection of chiasmal malformations from anatomic magnetic resonance (MR) images of the brain. CHIASM-Net, composed of encoding and classification modules, was developed using MR images of controls (n = 1708) and PWA (n = 32). Evaluation involved 8-fold cross validation involving accuracy, precision, recall, and F1-score metrics and was performed on a subset of controls and PWA samples excluded from the training. In addition to quantitative metrics, we used Explainable AI (XAI) methods that granted insights into factors driving the predictions of CHIASM-Net.


Results: The results for the scenario indicated an accuracy of 85 ± 14%, precision of 90 ± 14% and recall of 81 ± 18%. XAI methods revealed that the predictions of CHIASM-Net are driven by optic-chiasm white matter and by the optic tracts.


Conclusions: CHIASM-Net was demonstrated to use relevant regions of the optic chiasm for albinism detection from magnetic resonance imaging (MRI) brain anatomies. This indicates the strong potential of CNN-based approaches for visual pathway analysis and ultimately diagnostics.

History

Author affiliation

School of Psychology and Vision Science, University of Leicester

Version

  • VoR (Version of Record)

Published in

Investigative ophthalmology & visual science

Volume

64

Issue

13

Pagination

14

Publisher

Association for Research in Vision and Ophthalmology (ARVO)

issn

0146-0404

eissn

1552-5783

Copyright date

2023

Available date

2023-11-08

Spatial coverage

United States

Language

eng

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