Machine-learning algorithms for the identification of visual field loss associated with the antiseizure medication vigabatrin-a proof of concept

Wild, John M. ORCID: https://orcid.org/0000-0003-3019-3889, Smith, Philip E.M. ORCID: https://orcid.org/0000-0003-4250-2562 and Knupp, Carlo ORCID: https://orcid.org/0000-0001-9127-2252 2025. Machine-learning algorithms for the identification of visual field loss associated with the antiseizure medication vigabatrin-a proof of concept. British Journal of Ophthalmology 10.1136/bjo-2024-325804

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Abstract

The antiseizure medication, vigabatrin, is associated with visual field loss (VAVFL). However, the fields can be challenging to interpret due to unfamiliarity with the characteristics of the defect and/or to difficulty in obtaining a reliable examination, particularly in patients with cognitive limitations associated with the epilepsy. Two machine-learning pattern recognition algorithms were developed to identify VAVFL, objectively. The algorithms adhered to the European Medicines Agency-approved protocol for the detection of VAVFL (Three Zone Age Corrected Full Field 135 Screening Test (FF135) and the Central C30-2 Threshold Test (C30-2T) with the Humphrey Field Analyzer). Each algorithm compared the similarity of the measured field from each eye to that of modelled reference patterns of VAVFL, matched for equivalent severity, and objectively derived from a previously described case series of 123 adults. The algorithms were augmented by the optional inclusion of symmetrisation, a signal-to-noise enhancement technique based on the between-eye mirror image symmetry of VAVFL. Utility of the algorithms for identifying VAVFL was evaluated against a case series of 89 consecutively identified individuals stratified across six diagnostic categories including homonymous and glaucomatous losses. The algorithms exhibited excellent agreement with a 'gold standard' clinical interpretation (sensitivity and specificity: FF135, 22/23; 30/30; C30-2T, 17/18; 48/51). Symmetrisation was particularly useful in identifying VAVFL when perimetric learning or fatigue influenced the outcome for one eye and for visualisation in the presence of concomitant homonymous loss. The directly interpretable machine-learning outcome correctly identified VAVFL and could assist patient management in community (neuro-)ophthalmology. [Abstract copyright: © Author(s) (or their employer(s)) 2025. No commercial re-use. See rights and permissions. Published by BMJ Group.]

Item Type:	Article
Date Type:	Published Online
Status:	In Press
Schools:	Schools > Medicine Schools > Optometry and Vision Sciences
Publisher:	BMJ Publishing Group
ISSN:	0007-1161
Date of First Compliant Deposit:	9 May 2025
Date of Acceptance:	13 December 2024
Last Modified:	09 May 2025 17:15
URI:	https://orca.cardiff.ac.uk/id/eprint/177314

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