Diagnostic
Vision AI
Automated screening of radiological images to accelerate triage 4x faster in remote rural clinics — with no internet connection required.
Diagnosis Speed
4x Faster
Radiological triage time vs. unassisted clinician
Sensitivity
96.4%
Detection rate for critical findings in X-rays
Clinics Covered
38
Remote rural health centres across Rwanda
The Challenge
One Radiologist for Every 500,000 Patients
Rwanda's rural health centres have access to X-ray equipment but often lack trained radiologists to interpret results. Images are physically transported or transmitted to Kigali — causing delays of days or weeks for critical diagnoses.
- warningAverage 6-day wait for radiology report in rural districts vs. same-day in Kigali.
- warningTB and pneumonia going undetected until patients present with advanced symptoms.
- warningClinicians making treatment decisions without imaging confirmation, increasing risk.
The AI Solution
Edge-Deployed Diagnostic Vision Platform
X-Ray Anomaly Detection
CNN model trained on 280,000+ annotated chest X-rays flags TB, pneumonia, and cardiac enlargement with radiologist-level sensitivity.
Offline-First Architecture
Edge-deployed model runs entirely on a local device — no internet connection required — enabling use in the most remote clinics.
Triage Prioritisation Queue
Cases scored by urgency so clinicians see the highest-risk patients first, reducing time-to-treatment for critical conditions.
Bring AI diagnostics to your facility
Let's explore how computer vision can support your clinical team and reduce diagnostic delays — even in low-connectivity environments.