Predicting HFA 30-2 Visual Fields with Deep Learning from Multimodal OCT-Fundus Feature Fusion and Structure-Function...

Glaucoma is a leading cause of irreversible vision loss. During clinical follow-up, visual field (VF) tests (Humphrey Field Analyzer 30-2) assesses functional loss, while optical coherence tomography (OCT) and fundus imaging provide structural information. However, VF measurement can be subjective, exhibit test-retest variability, and sometimes exhibit structure-function discordance (SFD). Therefore, predicting VF values from structural images may support clinical decision-making. To estimate Humphrey 30-2 measures (mean deviation (MD), pattern standard deviation (PSD), and point-wise threshold sensitivity (TS)) in glaucoma/ocular hypertension (OHT) using a ViT-B/32-based feature-fusion approach on OCT and fundus images, and to examine the effect of SFD via sensitivity analysis. Visual features were extracted from color optic disc photographs, red-free fundus images, retinal nerve fiber layer (RNFL) thickness map, and circular RNFL plots using Vision Transformer (ViT-B/32)-based models. These features were combined with demographic and clinical data to form a multimodal artificial intelligence model. Global VF indices (MD, PSD) were estimated with probabilistic regression that accounts for uncertainty, and point-wise TS values were predicted using a location-aware network. In a separate analysis, eyes exhibiting SFD were identified and excluded to assess model performance under OCT-VF concordance. Mean absolute errors (MAE) were 2.26, 1.42, and 2.96 dB for MD, PSD, and mean TS, respectively, and the proportions within ± 2 dB were 59.65%, 75.44%, and 57.90%. After excluding SFD eyes, MAEs decreased to 1.82, 1.30, and 2.12 dB for MD, PSD, and mean TS, respectively; the proportions within ± 2 dB increased to 66.7%, 76.5% and 62.7%, respectively. These findings indicate that discordance affects performance and that predictions are more reliable in clinically concordant cases. ViT-B/32-based deep feature fusion offers clinically meaningful accuracy for predicting VF metrics from multimodal structural images. SFD was frequently detected among the lowest-performing cases, and this possibility should be considered when interpreting low-performing outputs.