Developer of an AI-based medical diagnostic system designed to address the unmet need for accurate, accessible, and scalable detection of obstructive sleep apnea (OSA) and other sleep disorders. Despite the high prevalence of OSA and its strong association with severe health complications such as cardiovascular events, respiratory failure, and increased mortality, current screening practices are limited, relying mainly on low-accuracy questionnaires or expensive, time-consuming sleep lab studies. The solution leverages oximetry signal analysis to detect all levels of OSA severity with clinical-grade accuracy comparable to polysomnography (PSG). It enables non-invasive, multi-night, at-home or in-hospital diagnosis, classification of sleep stages, and continuous patient monitoring. The technology has undergone successful clinical validation at a leading hospital and aims to make high-quality sleep diagnostics more widely available, reducing underdiagnosis, improving patient outcomes, and lowering healthcare costs. Targeting a multibillion-dollar global market with established reimbursement pathways and lacking direct competitors offering comparable clinical-grade, scalable solutions, the system fills a critical gap in sleep disorder detection and management.