Contact person: AI Dental Cavity Detector

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Location: Bengaluru, India

Budget: Recommended by industry experts

Time to start: As soon as possible

Project description:
"I’m building a mobile app that lets dentists and patients point a phone’s camera at teeth and receive an instant assessment of possible cavities. The app must run smoothly on both iOS and Android, so a single cross-platform codebase (Flutter, React Native, or similar) makes sense.

Core workflow: the user opens the app, captures an image of the tooth surface, and the onboard AI model analyses that photo to highlight areas with suspected decay and returns a probability score. A lightweight results screen should appear in under five seconds on mid-range phones.

I don’t yet have labelled images to train the model, so the first step is to help me source or license an appropriate dental dataset (intra-oral photos with cavity annotations). If no public set is sufficient, advise on a small data-collection protocol I can run with local clinics. Once we know the data path, I’ll rely on you to handle preprocessing, augmentation, and model training (PyTorch or TensorFlow preferred).

Key deliverables
• Cross-platform app (iOS + Android) with camera capture, image preview, and AI inference pipeline
• Trained cavity-detection model packaged for on-device or efficient cloud inference
• Documentation covering data-sourcing steps, model architecture, and how to retrain with new images
• Simple UI assets and icon set so the build is store-ready

Acceptance will be based on a test build that correctly installs on one iPhone and one Android device, processes sample images, and returns consistent detection results. Let me know your proposed tech stack, any similar projects you’ve shipped, and the estimated timeline for MVP delivery." (client-provided description)