OCT-based Visual Field Estimation Using Segmentation-free 3D CNN Shows Lower Variability than Subjective Standard Automated Perimetry

PurposeTo train and evaluate segmentation-free 3D convolutional neural network (3DCNN) models for estimating visual field (VF) from optical coherence tomography (OCT) images and to independently assess the longitudinal variability and progression detection capabilities of Humphrey Field Analyzer (HFA) measurements and OCT-based estimated VF (OCT-VF) in a diverse clinical population. DesignRetrospective multicenter study. Participants13,366 patients (24,313 eyes) underwent HFA tests (24-2, trimmed 30-2, or 10-2 test patterns) and macular OCT imaging at five ophthalmic institutions. The dataset included 129,007 paired OCT-VF data points representing various ocular conditions. MethodsWe trained segmentation-free 3DCNN models using comprehensive OCT datasets without disease-specific exclusions, employing 10-fold cross-validation to estimate VF thresholds and mean deviation (MD). Unlike previous studies, we independently assessed both OCT-VF and HFA measurements by creating separate longitudinal datasets with standardized measurement counts and observation periods for comparative analysis, enabling direct evaluation of clinical reliability. We analyzed absolute residual variability from regression lines using jackknife resampling, applied Bonferroni correction for multiple comparisons, and used Spearmans correlation for progression analysis. Main Outcome MeasuresOCT-VF and HFA VF agreement, residual variability, progression detection rates, and progression rate correlations. ResultsOCT-VF and HFA VF showed correlations (Pearsons r: 24-2 thresholds 0.863, MD 0.924; 10-2 thresholds 0.881, MD 0.939; all p < 0.001). OCT-VF demonstrated significantly lower residual variability than HFA for all parameters (OCT-VF vs. HFA: 0.58 vs. 1.12 dB for 24-2 MD; 0.70 vs. 1.12 dB for 10-2 MD; all p < 0.001). This advantage persisted across all test points (mean variability reduction: 60.4% for 24-2; 55.1% for 10-2), age groups, and most severity levels. OCT-VF identified more progression events (24-2 MD: 113% more, 10-2 MD: 48.6% more). MD slopes showed correlations between OCT-VF and HFA (Pearsons r: 24-2 MD 0.831, 10-2 MD 0.863; all p < 0.001). ConclusionsThe segmentation-free 3DCNN models objectively estimated VF from OCT images with significantly lower longitudinal variability than performance-dependent HFA measurements across diverse ocular conditions. The lower variability of OCT-VF enhances statistical power for progression detection, suggesting its clinical potential as a complementary tool derived from routine OCT data, decreasing measurement noise, and enabling more timely therapeutic interventions.