TeleROP-VLF: Federated Vision-Language Model on Wearable Devices for Explainable Retinopathy of Prematurity Vascular Geometrical Feature Screening

Retinopathy of Prematurity (ROP) remains one of the leading causes of blindness in children globally. Global screening for ROP is limited by the shortage of trained ophthalmologists and low access to care in resource-limited settings. Vision-Language Models (VLMs) can be utilized to analyze medical images. However, VLMs also have limitations for ROP screening, including patient data privacy concerns, computational requirements for point-of-care devices, and the need for clinically interpretable predictions. In this study, we present TeleROP-VLF, a federated learning framework for VLM collaboration to train and perform analysis across multi-wearable retinal imaging devices while preserving sensitive patient data from leaving the hospital. TeleROP-VLF contributes three distinct innovations to the literature: 1) a lightweight VLM architecture that fits within wearable retina imaging devices through structured pruning and knowledge distillation; 2) a federated aggregation protocol with provable convergence; and 3) an explainable module that provides vessel geometry analysis with clinical meaning, aligned with established ROP diagnosis criteria including plus disease indicators, zone classification, and tortuosity. Using TeleROP-VLF in five neonatal intensive care units, we observe diagnostic performance comparable to expert ophthalmologists at 87.6% accuracy ultimately producing on-device inference that processes within 2.3 seconds on an autonomous edge computing platform while providing differential privacy guarantees and retaining a privacy budget of ϵ = 1.8. Coupled with clinical validation of explainable vascular geometric features, TeleROP-VLF aids clinician trust and facilitates early interventions to prevent vision loss for vulnerable premature infants.