Project Description
I have a curated set of roughly 700 post-operative periapical X-rays taken after root canal treatments by dental students. I need a computer-vision model, built in Python with TensorFlow or PyTorch, that can automatically assess each case and return two outputs:
• a numeric score that can be used in our grading spreadsheet, and
• a concise feedback report highlighting strengths and mistakes.
The evaluation must cover the accuracy of the root-canal pathway, cleanliness of the treated area, and overall completion quality. I will provide a detailed rubric with additional sub-criteria so the model can translate radiographic findings into tutor-style comments.
Your job is to design, train, and validate the model, then package an inference script that accepts a single X-ray or a batch folder, runs the analysis, and writes the score plus feedback to JSON or CSV. Please include clear instructions for environment setup, a short summary of the architecture, and any preprocessing steps so we can reproduce the results and continue training as we add more data.
Acceptance will be based on:
1. At least 85 % agreement with our faculty benchmark on a withheld test set.
2. Consistent scoring across three random re-runs (±2 points).
3. Feedback text reflecting the rubric headings we supply.
If you have prior experience with dental or medical imaging, segmentation, or explainability techniques such as Grad-CAM, let me know—visual overlays that justify each score would be a welcome bonus.