Expert Annotated Mandibular Third Molar (ExAn-MTM) Dataset
Description
📌 Overview of ExAn-MTM Dataset The ExAn-MTM dataset was developed to address the scarcity of publicly available annotated datasets for mandibular third molar (MTM) detection. It is derived from high-quality panoramic radiographs (PRs) and consists of 973 expertly annotated images, adapted from the original m-TM dataset. This dataset provides a reliable benchmark for AI-driven dental diagnostics, computer vision research, and medical imaging studies. 📂Dataset Structure- The dataset includes bounding box annotations for two clinically important classes of mandibular third molars: 0 → e-MTM (erupted mandibular third molar) 1 → i-MTM (impacted mandibular third molar) Annotations were created by an experienced oral and maxillofacial radiologist using the MakeSense annotation tool, and are provided in YOLO format. Folder structure: /ExAn-MTM dataset/ ├─ train/ │ ├─ images/ │ ├─ labels/ ├─ valid/ │ ├─ images/ │ ├─ labels/ Data split: Training Set → 875 images (624 e-MTM, 980 i-MTM) Validation Set → 98 images (63 e-MTM, 117 i-MTM) 🎯 Applications The dataset can be used for: Training and validating object detection models (YOLO, Faster R-CNN, RetinaNet, etc.) Benchmarking AI-based clinical decision support systems (CDSS) in dentistry Conducting comparative studies in medical imaging and annotation methods Advancing research in explainable AI (XAI) for dental applications 🌍 Significance First publicly available, expert-annotated dataset dedicated to mandibular third molar detection. Adheres to FAIR principles (Findable, Accessible, Interoperable, Reusable). Enhances reproducibility and transparency in dental AI research. Provides a solid foundation for developing and benchmarking diagnostic models in dental radiology. 📑 Citation This dataset is derived from and must be cited alongside the following studies: 1- Kayadibi, İ., Köse, U., Güraksın, G.E. et al. E-MTMYOLO: an explainable YOLOv5-based architecture for accurate detection of mandibular third molar using a novel expert-annotated dataset. J Supercomput 81, 1286 (2025). https://doi.org/10.1007/s11227-025-07775-w 2- Kayadibi, İ., Köse, U., Güraksın, G. E., & Çetin, B. (2025). An AI-assisted explainable mTMCNN architecture for detection of mandibular third molar presence from panoramic radiography. International Journal of Medical Informatics, 195, 105724. https://doi.org/10.1016/j.ijmedinf.2024.105724
Files
Steps to reproduce
To utilize the ExAn-MTM dataset: 1. Download and extract the dataset while keeping the folder structure. 2. Load the PRs and YOLO-format labels using your preferred deep learning framework (e.g., PyTorch, YOLO, TensorFlow). 3 Apply standard preprocessing (e.g., resizing) if needed. 4. Train your detection model using the training set. 5. Validate the model on the validation set using metrics like mAP, precision, recall 6. Optimize and deploy your model for MTM detection tasks.
Institutions
- Afyon Kocatepe Universitesi
- Selcuk Universitesi
- Suleyman Demirel Universitesi