ScopeAI

ScopeAI looks at colonoscopy images and flags polyps in real time, drawing a box around each one with a confidence score. The goal was a tool a small clinic could actually run, not one that needs a research lab's hardware.

Catching polyps early is how colorectal cancer gets caught early, but most detection models assume a powerful GPU. That rules out the rural clinics and small hospitals that would benefit most. I wanted something accurate that runs on limited compute.

The first prototype classified image patches with a sliding-window CNN, which was slow and clumsy. I moved to YOLOv8 Nano, which detects multiple polyps directly with bounding boxes, and trained it on the Kvasir-SEG dataset of a thousand colonoscopy images. A Flask service runs the inference; a Next.js front end handles uploads and reporting.

• Real-time analysis of single images or batches, with bounding-box detection.
• Around 6ms inference per image on modest hardware.
• Downloadable reports with annotated images and confidence scores.
• User accounts and a history of past analyses.
• 95.5 mAP50 on the held-out test set.

A Next.js and React front end talks to a Flask and Python backend running YOLOv8 from Ultralytics, with an early TensorFlow CNN as the baseline. Data lives in SQLite, and the two servers run side by side locally.

• Next.js
• Flask
• Python
• YOLOv8
• TensorFlow
• SQLite

ScopeAI works as a proof of concept and validated the core idea: a small, fast model can do real-time polyp detection on ordinary hardware. I wrote up how it works on the blog (opens in a new tab), and plan to open-source it.