Notal Vision Home Optical Coherence Tomography (OCT) System

The Notal Vision Home OCT System (SCANLY) is a patient-operated spectral-domain optical coherence tomography device utilizing a cloud-based deep learning AI to monitor neovascular age-related macular degeneration. By enabling daily self-scans and quantifying retinal fluid biomarkers, it aims to inform timely ophthalmological interventions and improve management between clinic visits.

The Notal Vision Home OCT System (SCANLY) is a home-use spectral-domain optical coherence tomography (SD-OCT) device designed for patients with neovascular age-related macular degeneration (nAMD). Its primary purpose is to allow patients to self-administer daily retinal scans, facilitating continuous monitoring of their condition between regular clinic visits. The system employs a cloud-based artificial intelligence (AI) that analyzes these images, specifically segmenting and quantifying the volumes of subretinal and intraretinal fluid, which are critical biomarkers indicative of disease activity. The SCANLY system integrates a user-friendly OCT imager with a sophisticated deep-learning algorithm, which has been trained on thousands of retinal scans to accurately detect the presence and volume of fluid. Clinical trials, which included over 5,400 home scans, demonstrated high user compliance, with 97% of participants successfully producing high-quality images. Furthermore, the AI's analytical performance exhibited strong agreement with evaluations made by expert human graders, underscoring its reliability in monitoring retinal fluid and its potential to support earlier clinical intervention.

AI Regulatory Concerns

Powered by

Jev reads the full FDA letter and returns calibrated concern probabilities per regulatory category. These are AI estimates, not regulatory advice.

• Q1. How does the AI algorithm differentiate between subretinal and intraretinal fluid, and what is the clinical significance of quantifying each type separately for nAMD management?; What are the long-term adherence rates for patients using the SCANLY home OCT system, and how does this compare to adherence with other home monitoring devices for chronic conditions?; How does the system's sensitivity and specificity for detecting changes in fluid volume compare to standard in-clinic OCT devices, particularly for subtle or early changes?; What is the learning curve for patients to consistently produce high-quality scans, and what training or support mechanisms are most effective?; How will the integration of AI-driven home monitoring data impact ophthalmologists' workflow and decision-making processes for nAMD treatment, particularly regarding treat-and-extend protocols?