In American journal of ophthalmology ; h5-index 67.0
PURPOSE : To use longitudinal optical coherence tomography (OCT) and OCT-Angiography (OCTA) data to detect glaucomatous visual field (VF) progression with a supervised machine learning approach.
DESIGN : Prospective cohort study.
METHODS : 110 eyes of glaucoma suspect (33.6%) and glaucoma (66.4%) patients with a minimum of five 24-2 VF tests and three optic nerve head and macula images over an average follow-up duration of 4.1 years were included. VF progression was defined using a composite measure including either a "likely progression event" on Guided Progression Analysis, a statistically significant negative slope of VF mean deviation or VF index, or a positive pointwise linear regression event. Feature-based gradient boosting classifiers were developed using different subsets of baseline and longitudinal OCT and OCTA summary parameters. Area under the receiver operating characteristic curve (AUROC) was used to compare the classification performance of different models.
RESULTS : VF progression was detected in 28 eyes (25.5%). The model with combined baseline and longitudinal OCT and OCTA parameters at the global and hemifield levels had the best classification accuracy to detect VF progression (AUROC=0.89). Models including combined OCT and OCTA parameters had higher classification accuracy compared to those with individual subsets of OCT, or OCTA features alone. Including hemifield measurements significantly improved the models' classification accuracy compared to using global measurements alone. Including longitudinal rates of change of OCT and OCTA parameters (AUROCs=0.80-0.89) considerably increased the classification accuracy of the models with baseline measurements alone (AUROCs=0.60-0.63).
CONCLUSIONS : Longitudinal OCTA measurements complement OCT-derived structural metrics for the evaluation of functional VF loss in glaucoma patients.
Kamalipour Alireza, Moghimi Sasan, Khosravi Pooya, Mohammadzadeh Vahid, Nishida Takashi, Micheletti Eleonora, Wu Jo-Hsuan, Mahmoudinezhad Golnoush, Li Elizabeth Hf, Christopher Mark, Zangwill Linda, Javidi Tara, Weinreb Robert N
2022-Oct-31
Glaucoma, Longitudinal, Machine learning, OCT, OCTA, Progression
