LIO-VisionAR: Intelligence-enabled augmented reality guidance for laser indirect ophthalmoscope-based retinal laser therapy

Abstract

Objective

Laser indirect ophthalmoscope (LIO) retinal therapy is a complex procedure that demands precision. We present LIO-VisionAR, an intelligence-enabled augmented reality (AR) guidance system designed to support safer and more effective training for LIO-based retinal laser therapy.

Methods

A custom retina model with retinopathy areas was developed and integrated into a human phantom model. A virtual retina model and simulator were developed using the color fundus photo to compute the magnification and laser targeting guidance based on the user's AR head-mounted device movement. Randomized user trials compared conventional and AR-guided retinal laser tasks, while multimodal behavioral telemetry were recorded for quantitative performance analysis and proof-of-concept skill inference.

Results

A total of 11 experts and 12 non-experts were included in the study. With AR guidance, laser targeting accuracy increased from 70.8 % to 82.6 % for experts and from 65.7 % to 81.7 % for non-experts. AR guidance increased laser instrumentation time, reflecting a deliberate speed–accuracy trade-off. Analysis of AR-captured behavioral telemetry showed that gaze exploration and temporal control features were associated with performance, and unsupervised clustering revealed distinct behavioral strategies linked to progressively higher accuracy. A composite performance-based skill score exhibited a moderate positive association with laser accuracy (Spearman ρ = 0.45, p = 0.032). Over 80 % of experts agreed that our system is appropriate for teaching and could improve retinal laser therapy training and safety.

Conclusions

LIO-VisionAR improves procedural accuracy under simulated conditions and demonstrates a concrete pathway toward adaptive, intelligence-based AR guidance for ophthalmic microsurgical training.