Cas Ruiyi (中科睿医) provides an AI‑powered digital platform that uses lightweight VR headsets and wearable gait sensors to capture eye‑movement, pupillometry, and gait data. The system automatically extracts quantitative biomarkers and delivers disease‑specific risk scores and longitudinal reports through a secure, HL7/FHIR‑compatible dashboard, enabling hospitals and research centers to conduct scalable, objective assessments for neurodegenerative and sleep‑related disorders.
Funding
Funding not disclosed
Founders
Product
Problem
Neurological and neurodegenerative disorders often require specialized, time‑consuming assessments that rely on bulky equipment and expert interpretation, limiting early detection and continuous monitoring in routine clinical settings.
Solution
Cas Ruiyi (中科睿医) offers an AI‑driven digital platform that captures eye‑movement, pupil response, and gait metrics using lightweight virtual‑reality (VR) headsets and sensor‑based gait analysis tools. The system automatically extracts quantitative biomarkers associated with conditions such as Alzheimer’s disease, Parkinson’s disease, sleep‑related breathing disorders, and mixed‑feature depression. Collected data are processed with machine‑learning models to generate diagnostic scores and longitudinal trend reports, which clinicians can review through a secure web dashboard or integrate with existing health‑record systems. By providing a standardized, portable, and scalable assessment workflow, the platform enables earlier screening, more precise diagnosis, and data‑guided intervention planning across the full care continuum.
Target Audience
Primary users are hospitals, neurology and geriatric clinics, and research institutions that require objective, scalable assessments of cognitive and motor function for patients with neurodegenerative or sleep‑related disorders.
Features
- VR headset with integrated eye‑tracking and pupillometry for high‑resolution ocular biomarker acquisition
- Wearable gait analysis module that records stride patterns and balance metrics for rapid OSAHS screening
- AI algorithms that translate raw ocular and gait data into disease‑specific risk scores and progression indicators
- Cloud‑based analytics pipeline delivering encrypted results and visual dashboards for clinicians
- Interoperability layer supporting HL7/FHIR standards for seamless integration with hospital information systems
- Modular study design allowing researchers to customize task protocols for clinical trials