1. Prediction of visual field progression with serial optic disc photographs using deep learning.
- Author
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Mohammadzadeh, Vahid, Wu, Sean, Davis, Tyler, Vepa, Arvind, Morales, Esteban, Besharati, Sajad, Edalati, Kiumars, Martinyan, Jack, Rafiee, Mahshad, Martynian, Arthur, Scalzo, Fabien, Caprioli, Joseph, and Nouri-Mahdavi, Kouros
- Subjects
Field of vision ,Glaucoma ,Imaging ,Optic Nerve ,Humans ,Deep Learning ,Visual Fields ,Disease Progression ,Optic Disk ,Female ,Male ,Middle Aged ,Visual Field Tests ,Photography ,ROC Curve ,Follow-Up Studies ,Aged ,Retrospective Studies ,Intraocular Pressure ,Glaucoma ,Optic Nerve Diseases ,Vision Disorders - Abstract
AIM: We tested the hypothesis that visual field (VF) progression can be predicted with a deep learning model based on longitudinal pairs of optic disc photographs (ODP) acquired at earlier time points during follow-up. METHODS: 3919 eyes (2259 patients) with ≥2 ODPs at least 2 years apart, and ≥5 24-2 VF exams spanning ≥3 years of follow-up were included. Serial VF mean deviation (MD) rates of change were estimated starting at the fifth visit and subsequently by adding visits until final visit. VF progression was defined as a statistically significant negative slope at two consecutive visits and final visit. We built a twin-neural network with ResNet50-backbone. A pair of ODPs acquired up to a year before the VF progression date or the last VF in non-progressing eyes were included as input. Primary outcome measures were area under the receiver operating characteristic curve (AUC) and model accuracy. RESULTS: The average (SD) follow-up time and baseline VF MD were 8.1 (4.8) years and -3.3 (4.9) dB, respectively. VF progression was identified in 761 eyes (19%). The median (IQR) time to progression in progressing eyes was 7.3 (4.5-11.1) years. The AUC and accuracy for predicting VF progression were 0.862 (0.812-0.913) and 80.0% (73.9%-84.6%). When only fast-progressing eyes were considered (MD rate < -1.0 dB/year), AUC increased to 0.926 (0.857-0.994). CONCLUSIONS: A deep learning model can predict subsequent glaucoma progression from longitudinal ODPs with clinically relevant accuracy. This model may be implemented, after validation, for predicting glaucoma progression in the clinical setting.
- Published
- 2024