Classifying and quantifying changes in papilloedema using machine learning

BackgroundMachine learning (ML) can differentiate papilloedema from normal optic discs using fundus photos. Currently, papilloedema severity is assessed using the descriptive, ordinal Frisén scale. We hypothesise that ML can quantify papilloedema and detect a treatment effect on papilloedema due to...

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Bibliographic Details
Published in:BMJ neurology open 2024-06, Vol.6 (1), p.e000503
Main Authors: Branco, Joseph, Wang, Jui-Kai, Elze, Tobias, Garvin, Mona K, Pasquale, Louis R, Kardon, Randy, Woods, Brian, Szanto, David, Kupersmith, Mark J
Format: Article
Language:English
Subjects:
NEUROOPHTHALMOLOGY
OPHTHALMOLOGY
Original Research
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Summary:BackgroundMachine learning (ML) can differentiate papilloedema from normal optic discs using fundus photos. Currently, papilloedema severity is assessed using the descriptive, ordinal Frisén scale. We hypothesise that ML can quantify papilloedema and detect a treatment effect on papilloedema due to idiopathic intracranial hypertension.MethodsWe trained a convolutional neural network to assign a Frisén grade to fundus photos taken from the Idiopathic Intracranial Hypertension Treatment Trial (IIHTT). We applied modified subject-based fivefold cross-validation to grade 2979 longitudinal images from 158 participants’ study eyes (ie, the eye with the worst mean deviation) in the IIHTT. Compared with the human expert-determined grades, we hypothesise that ML-estimated grades can also demonstrate differential changes over time in the IIHTT study eyes between the treatment (acetazolamide (ACZ) plus diet) and placebo (diet only) groups.FindingsThe average ML-determined grade correlated strongly with the reference standard (r=0.76, p
ISSN:2632-6140
2632-6140
DOI:10.1136/bmjno-2023-000503