PAI-meets-AI: Open-Weight Ensemble for Periapical Index Scoring
This repository contains the official pre-trained model weights for the study "Development and Internal Validation of CNN-Based Models for Five-Class PAI Scoring".
We provide three distinct, fine-tuned Deep Learning architectures—ResNet50, EfficientNet-B3, and ConvNeXt-Tiny—which can be used individually or combined into an Equal-Weight Soft-Voting Ensemble.
The Periapical Index (PAI) is a standardized 1–5 scale used for assessing apical periodontitis on dental radiographs. These models were trained on a dataset of 17,549 expert-annotated intraoral radiographs to automate this assessment.
We release the weights for the best-performing checkpoint of each architecture.
File Name
Architecture
Input Size
Description
efficientnet-b3_best.pth
efficientnet_b3
300x300
Best single model in our study (QWK 0.769).
resnet50_best.pth
resnet50
224x224
High sensitivity for healthy cases.
convnext-tiny_best.pth
convnext_tiny
224x224
Modern Transformer-style CNN.
Note: An older legacy checkpoint (pai-meets-ai_...) is preserved for reproducibility but the specific named files above are recommended.
Normalization Details
All models were trained using standard ImageNet normalization statistics:
Mean:[0.485, 0.456, 0.406]
Std:[0.229, 0.224, 0.225]
🚀 How to Use
The checkpoints contain the model_state_dict along with the necessary configuration (model_name, num_classes) to reconstruct the architecture using timm.
Intended Use & Limitations
Research Only: These models are intended for research and educational purposes. They are not certified medical devices (SaMD) and should not be used for primary clinical diagnosis.
Input Data: The models are trained on cropped periapical radiographs centered on the tooth apex. Feeding full uncropped radiographs will lead to incorrect results.
License: The weights are released under the MIT License, allowing for open use, modification, and distribution with attribution.
Data Privacy
The original dataset containing 17,549 clinical radiographs is private and protected by GDPR/health data regulations. It cannot be shared. However, these released weights act as a privacy-preserving "condensed representation" of that dataset, enabling others to benefit from the learned features without accessing patient data.
Citation
If you use these weights in your research, please cite our paper:
code Bibtex
@software{Torgersen_PAI-meets-AI_2025,
author = {Torgersen, Gerald and [Co-Authors]},
title = {{PAI-meets-AI: Open-Weight Deep Learning Models for PAI Scoring}},
year = {2025},
publisher = {GitHub/HuggingFace},
url = {https://github.com/geraldOslo/PAI-meets-AI}
}
