Reliable Dental Radiograph Diagnosis via Calibrated Hybrid Representation Learning

Sayem ahmed Shayeed, Md. Jalal Uddin Chowdhury, Shadman Sakib

Keywords: Dental radiograph analysis, medical imaging, representation learning, calibration, interpretability, healthcare AI

TL;DR: A calibrated hybrid ConvNeXt-ML framework enables accurate, reliable, and interpretable multi-class diagnosis from intraoral dental radiographs.

Abstract: Automated diagnosis from intraoral radiographs can enable scalable oral-health screening in settings with limited access to specialist interpretation. We present a calibrated hybrid representation-learning framework for multi-class dental radiograph diagnosis that combines a fine-tuned ConvNeXt-Tiny feature extractor with classical machine-learning classifiers trained on frozen deep embeddings. We evaluate the framework on DentIRO, a four-class single-tooth intraoral radiograph dataset, using patient-level train, validation, and locked-test splits to reduce evaluation leakage. On the locked test set, the proposed model achieves approximately 98.6% macro-F1 and 99.1% accuracy, with low calibration error and Grad-CAM evidence highlighting clinically meaningful tooth structures. These results demonstrate the potential of calibrated hybrid deep-feature pipelines for accurate, reliable, and interpretable dental radiograph analysis.

Track: Track 2: ML Research by Muslim Authors

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Submission Number: 36