Machine Learning Applied to Visual Fields of Dominant Optic Atrophy Patients

Purpose: Identification and quantification of characteristic visual field (VF) patterns in patients with dominant optic atrophy (DOA) using the archetypal analysis (AA) machine learning algorithm. Methods: In this retrospective study, we collected 30-2 or 24-2 VFs performed with Humphrey Visual Field analyzer from 144 patients (280 eyes) affected by molecularly confirmed DOA carrying OPA1 heterozygous mutation. The VFs were randomly separated into a training set (224 VFs, 80%) and test set (56 VFs, 20%). An AA model was developed by decomposing the VFs of the training set into archetypes (ATs). Spearman correlations were calculated between ATs’ weights and mean deviation (MD) and visual acuity (VA). Statistical comparison was performed between ATs weights according to mutation subtype groups. Results: The DOA-AA model was composed of eight ATs with a high performance in the test set (R2 = 0.88). According to the Ocular Hypertension Treatment Study (OHTS) classification, the central/ceco-central scotoma resembling ATs presented the highest weights (24%) followed by superior defects (13%). ATs with more abnormal VF resembling defects correlated most with MD (AT5-8), whereas only the total loss AT7 with VA (P value < 0.01). Subtype mutations linked with worse clinical features had statistically significantly higher weights for worse ATs (AT7, P < 0.001). Conclusions: The developed AA model allowed the identification and quantification of VF patterns in DOA. Furthermore, a clinical genotype-phenotype association was supported by the comparison of severity at VF AA decomposition. Translational Relevance: AA enables an objective identification of quantifiable visual field defects intrinsic to DOA providing functional details based on genotype.