Hepato-Sentinel brings early jaundice detection to Ethiopia's primary care — using a smartphone and AI to cut mortality from 65% to under 30%.
In Ethiopia, 80% of primary facilities lack bilirubin tests. Visual inspection fails to detect jaundice until it's too late — especially on darker skin.
No affordable point-of-care test. Jaundice is missed until bilirubin >5–7 mg/dL.
Fragmented referral pathways cause fatal delays between community and specialist care.
AI models trained on Asian/European data — not validated on Ethiopian skin tones (Fitzpatrick IV–VI).
No established framework to integrate digital tools into Ethiopia's Health Extension Program.
A smartphone-based, offline AI system that detects jaundice at 2 mg/dL — validated for Ethiopian skin tones and integrated with the national health system.
Uses sclera images + colour calibration cards. Works in low-resource settings without lab.
Lightweight, on-device AI (JaunENet architecture) with 98.9% accuracy, 0.128 GFLOPs per image.
Works without internet. Syncs with MoH/eCHIS when available. Referral pathways built-in.
Designed for Health Extension Workers — 47,000+ across Ethiopia. Training & community engagement.
30-month implementation science project — co-developed with MoH, hospitals, and communities.
Our pilot aims to demonstrate a 55% mortality reduction in liver failure and build a sustainable digital health model for Ethiopia.
Multidisciplinary team from AASTU, MoH, Yekatit 12 MC, DBU/Hakim Gizaw Hospital, and partners.
Total request: ETB 1,559,000 (~USD 27,000). Co-funding from MoH, hospitals, and in-kind contributions.
Your support will train 200 health workers, equip 50 health posts, and save lives. Join us in building Ethiopia's first AI-powered hepatology network.
girma.neshir@aastu.edu.et · +251-913-021313