SKINOPATHY AI: Smartphone-Based Ophthalmic Screening and Longitudinal Tracking Using Lightweight Computer Vision

TL;DR

SKINOPATHY AI demonstrates that multi-signal ophthalmic screening is feasible on unmodified smartphones without cloud-based AI inference, providing a foundation for future clinically validated mobile ophthalmoscopy tools.

Abstract

Early ophthalmic screening in low-resource and remote settings is constrained by access to specialized equipment and trained practitioners. We present SKINOPATHY AI, a smartphone-first web application that delivers five complementary, explainable screening modules entirely through commodity mobile hardware: (1) redness quantification via LAB a* color-space normalization; (2) blink-rate estimation using MediaPipe FaceMesh Eye Aspect Ratio (EAR) with adaptive thresholding; (3) pupil light reflex characterization through Pupil-to-Iris Ratio (PIR) time-series analysis; (4) scleral color indexing foricterus and anemia proxies via LAB/HSV statistics; and (5) iris-landmark-calibrated lesion encroachment measurement with millimeter-scale estimates and longitudinal trend tracking. The system is implemented as a React/FastAPI stack with OpenCV and MediaPipe, MongoDB-backed session persistence, and PDF report generation. All algorithms are fully deterministic, privacy-preserving, and designed for non-diagnostic consumer triage. We detail system architecture, algorithm design, evaluation methodology, clinical context, and ethical boundaries of the platform. SKINOPATHY AI demonstrates that multi-signal ophthalmic screening is feasible on unmodified smartphones without cloud-based AI inference, providing a foundation for future clinically validated mobile ophthalmoscopy tools.

Figures (7)

• Figure 1: Schematic overview of SKINOPATHY AI three-tier architecture.
• Figure 2: SKINOPATHY AI --- Red-Eye Meter module. The user captures or uploads an eye photograph; the system computes a redness score (0--10) via LAB a* analysis and displays triage guidance. Illustrated output: 3.66/10 (mild, monitor). Camera device selection and live preview are provided to guide optimal capture. Screening only; not diagnostic.
• Figure 3: SKINOPATHY AI --- Blink Coach module. The user records a 10-second selfie video; MediaPipe FaceMesh and adaptive EAR thresholding detect blink events. Illustrated output: 17 blinks detected, rate = 102.05 blinks/min (elevated; re-recording with stable head position recommended). The module provides blink count, rate, and normative guidance (typical adult range: 15--20 blinks/min).
• Figure 4: SKINOPATHY AI --- Pupil Reflex Test module. The user records a 10-second video while briefly exposing the eye to a bright light source at approximately t = 3 s. MediaPipe FaceMesh with iris refinement tracks the Pupil-to-Iris Ratio (PIR) time-series. Key metrics extracted include PLR amplitude ($\Delta_{\text{rel}}$), latency (L$_{\text{ms}}$), mean constriction velocity (v$_{\text{mean}}$), and a recording quality score Q. The module targets neurological screening signals; absent or markedly delayed constriction warrants clinical follow-up. This figure shows the baseline without the application of a bright light source.
• Figure 5: SKINOPATHY AI --- Pupil Reflex Test module (additional view). The user records a 10-second video while briefly exposing the eye to a bright light source at approximately t = 3 s. MediaPipe FaceMesh with iris refinement tracks the Pupil-to-Iris Ratio (PIR) time-series.