A Remote Smart Health Framework for Anemia Risk Stratification via Edge Medical Vision Systems

We present an offline-first edge telemedicine platform designed for clinics and outreach programs where internet access, power, and IT support are unreliable. The system runs local electronic health record (EHR) and clinical “plug-in” screening services on a single embedded device, accessed through a clinician-facing web app over local WiFi. Data are stored locally with role-based access control and record-level encryption, while interoperability is provided as a best-effort queued synchronization pathway to external systems using HL7 FHIR when connectivity is available. As a representative plug-in, we implement non-invasive anemia screening from fingernail photographs. Images are processed fully on-device: an INT8-quantized YOLOv8n detector extracts nail regions, lightweight color and summary-statistic features are computed per ROI and concatenated, and a supervised regressor estimates hemoglobin. On an NVIDIA Jetson Orin Nano, ROI extraction runs in 22 ms and hemoglobin inference in 34 ms. Across six training strategies (unbalanced, augmented, and KDE-balanced by remark or severity), test RMSE ranges from 2.05–3.13 g/dL; the strongest numeric performance is achieved by severity-balanced SVR (RMSE 2.048 g/dL) and remark-balanced Gradient Boosting (RMSE 2.091 g/dL). Raincloud analyses restricted to true-anemic test cases show that balancing primarily reduces systematic overestimation (which drives false negatives) while augmentation can widen error tails, highlighting the importance of selecting training strategy to match screening objectives rather than optimizing a single aggregate metric.