Classifying and Quantifying Changes in Papilloedema Using Machine Learning
Author Type(s)
Resident/Fellow
Document Type
Article
Publication Date
1-1-2024
DOI
10.1136/bmjno-2023-000503
Journal Title
BMJ Neurology Open
Department
Medicine
Abstract
BACKGROUND: Machine 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.
METHODS: We 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.
FINDINGS: The average ML-determined grade correlated strongly with the reference standard (r=0.76, p
INTERPRETATION: Supervised ML of fundus photos quantified the degree of papilloedema and changes over time reflecting the effects of ACZ. Given the increasing availability of fundus photography, neurologists will be able to use ML to quantify papilloedema on a continuous scale that incorporates the features of the Frisén grade to monitor interventions.
Recommended Citation
Branco, J., Wang, J., Elze, T., Garvin, M., Pasquale, L., Kardon, R., Woods, B., Szanto, D., & Kupersmith, M. (2024). Classifying and Quantifying Changes in Papilloedema Using Machine Learning. BMJ Neurology Open, 6 (1), 000503-000503. https://doi.org/10.1136/bmjno-2023-000503
