Utilizing optical coherence tomography and machine learning to identify vision abnormalities in pediatric neurofibromatosis type 1 patients

Abstract

Neurofibromatosis type 1 (NF-1) is a genetic disorder associated with a high risk of vision loss in children. Optical coherence tomography (OCT) provides non-invasive, high-resolution imaging of retinal and optic nerve structures, yet translating these data into predictive clinical tools remains challenging. This retrospective longitudinal cohort study analyzed 515 OCT measurements collected across multiple visits from 168 pediatric NF-1 patients (aged 3–19 years) to evaluate the ability of machine learning models to identify current vision abnormalities based on retinal and optic nerve layer thickness, rather than raw OCT images. Among the algorithms tested, the Balanced Random Forest model demonstrated the best performance (AUC¿=¿0.82; sensitivity¿=¿0.66). Thinning of the retinal nerve fiber layer (RNFL) and ganglion cell layer (GCL+) were identified as the strongest predictors of abnormal vision. Data-driven cut-off values for total macular and nerve layer thickness provided clear thresholds for clinical interpretation, while a cumulative “k-out-of-n” analysis showed that combining multiple OCT abnormalities enhanced risk stratification. These findings highlight the potential of explainable machine learning to transform OCT data into interpretable, clinically actionable tools for early detection and management of current vision abnormalities in pediatric NF-1. Validation in larger, multi-center cohorts is needed to confirm generalizability and support clinical adoption.

Peer Reviewed

Postprint (published version)