AfriScan Nova

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  Afriscan Nova landing page

Inspiration

AfriScan Nova was inspired by the need to help clinicians quickly triage chest X-rays and identify patients who may require urgent TB testing.

What it does

AfriScan Nova is an AI-powered chest X-ray triage assistant. The system allows a clinician to upload a chest X-ray image, after which the AI:

• predicts tuberculosis risk
• provides a confidence score
• highlights suspicious lung regions using explainable AI
• generates a structured triage summary with suggested clinical tests The goal is not to replace clinicians but to support them with faster triage and decision assistance. ## How we built it AfriScan Nova consists of three main components. Frontend A React dashboard that allows clinicians to upload chest X-rays and view AI results. Backend A FastAPI server that processes uploaded images and runs a deep learning model. AI Model A ResNet18 convolutional neural network trained to classify chest X-rays for TB risk. Grad-CAM explainable AI is used to highlight lung regions influencing the prediction. The system performs: Image validation to ensure the input is a chest X-ray AI prediction of TB probability Risk classification (Low / Moderate / High) Grad-CAM localization of suspicious lung regions ## Challenges we ran into One major challenge was ensuring the AI model’s predictions were explainable. In healthcare applications, clinicians must be able to understand why the model made a decision. To address this, we implemented Grad-CAM visualization to highlight the image regions influencing the model's prediction. Another challenge was validating uploaded images to ensure that non-medical images are rejected to prevent incorrect predictions. ## Accomplishments that we're proud of One of our biggest accomplishments was successfully building a complete end-to-end AI system within a very short hackathon timeframe. AfriScan Assist can take a chest X-ray image, analyze it using a deep learning model, and return a tuberculosis risk prediction along with visual explanations using Grad-CAM heatmaps. We are also proud of integrating multiple components into a single working platform: an intuitive frontend interface for uploading scans, a backend API that handles image processing and AI inference, and an explainable AI visualization layer that highlights suspicious regions in the lungs. This makes the output more interpretable for clinicians and not just a “black box” prediction. Another accomplishment is that the system is designed with real-world healthcare challenges in mind, particularly for regions with limited access to radiologists. By providing quick triage insights, AfriScan Nova demonstrates how AI can support early detection and improve healthcare accessibility. ## What we learned During development, we learned about:
• building AI pipelines for medical imaging
• implementing explainable AI techniques
• integrating machine learning models with web applications
• designing clinical-style dashboards for decision support ## What's next for AfriScan Nova Future improvements could include:
• integration with mobile X-ray units used in rural clinics
• additional disease detection such as pneumonia or lung cancer
• deployment as a cloud-based clinical decision support tool AfriScan Nova aims to support faster screening and early detection in TB control programs.

Built With

• amazon
• fastapi
• grad-cam
• python
• pytorch
• react
• restnet18
• tailwindcss
• typescript
• vite