Empowering Low-Resource Settings with RetinAI: An AI System with Wearable Headset and Retinal Imaging for Eye Tumor Home-Screening

Overview

Early diagnosis of retinoblastoma (RB) in the current clinical practice remains challenging due to the lack of access to timely and accurate eye examinations which is essential as late stages of RB often lead to enucleation and blindness. To address this issue, this research develops RetinAI, the first low-cost wearable headset and retinal camera with artificial intelligence systems for early detection of RB. RetinAI consists of two components, an extraocular detection device coupled with a YOLOv11 deep lea rning model to detect the early sign of RB, leukocoria, and an internal retinal image system powered by ResNet-50 or YOLOv11 to detect retinal tumors. The hardware includes a 3D printed headset and lens/camera connector, Pi Camera 3, infrared/white-LED light, LCD screen, 20D lens, and Raspberry Pi 5. For leukocoria detection, color analysis of leukocoria showed they had different Value, Hue, and Saturation from normal pupils. YOLOv11 model was developed and showed a high performance of 98% mAP, and can dete ct leukocoria as small as 1 mm in diameter using the RetinAI headset. For RB retinal tumor detection, ResNet50 and YOLOv11 were developed and achieved 97% accuracy and 96% mAP respectively. Clinically, using RetinAI on retinoblastoma patients demonstrated that RetinAI can detect retinoblastoma tumor. RetinAI is the first low-cost system that can not only detect eye tumors from external pupil but also from retina without any pupil dilation medication. RetinAI can significantly improve RB early detection and be used at home or in small clinics without eye specialists, especially in underserved communities.

Competition history

• JSHS 2025 Category not listed

Resources

• National JSHS abstract book ↗

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