arXiv Computer Vision
Patient-Level, Leakage-Aware Deep Learning for Cross-Center Periapical Radiograph Classification
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Patient-Level, Leakage-Aware Deep Learning for Cross-Center Periapical Radiograph Classification
Dental caries and endodontic disease are among the most common health conditions worldwide, and intraoral periapical radiographs are central to their detection, treatment planning, and follow-up. Auto...
arXiv AI
Sep 17
Automated Dental Caries Segmentation in Panoramic Radiographs Using Dual-Stage Deep Learning
The paper introduces a dual‑stage deep learning system for detecting dental caries in panoramic radiographs. It first localizes teeth using Faster R‑CNN, then applies U‑Net for pixel‑wise caries segmentation, converting polygon annotations into high‑resolution binary masks. Trained on 3,000 images with both expert and algorithmic labels, the model achieves an IoU of 0.9013, Dice of 0.9482, Recall of 0.9433, and Precision of 0.9774, outperforming existing methods and reducing false positives.
arXiv Machine Learning
Sep 21
Detection is solved, delineation is not: what governs tooth segmentation on panoramic radiographs
The study evaluates automatic tooth segmentation on panoramic radiographs using a large annotated corpus of 1,422 images and 42,142 tooth polygons. It finds that increasing input resolution improves boundary precision (mask mAP50‑95 rises from 0.656 to 0.717) while detection performance remains unchanged, and that architectural changes have minimal impact on in‑domain accuracy. Targeted interventions such as LoRA adaptation, promptable foundation models, and anatomical label assignment provide negligible gains, indicating that resolution and acquisition diversity should be prioritized over model novelty.
arXiv Computer Vision
Sep 17
AgenTeeth: A Model-Agnostic Framework for Suppressing Hallucination in Frozen Vision-Language Models on Dental X-Rays via Tool Evidence Injection
arXiv:2609.17800v1 Announce Type: new Abstract: Vision-language models (VLMs) remain largely unreliable on panoramic dental radiographs and can rely on learned anatomical priors rather than evidence...
arXiv Computer Vision
Sep 10
Morphological Decoupling-Based Skeletal Classification for Clinical Assessment of Malocclusion
arXiv:2609.09801v1 Announce Type: cross Abstract: Malocclusion skeletal grading is a fundamental task in orthodontics, critical for diagnosis and treatment planning. Traditionally, cone-beam computed...
arXiv AI
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Reliability- and Anatomy-Consistency-Aware Multimodal Learning for Robust Fracture Classification from Bangladeshi Radiographs
arXiv:2608.21482v1 Announce Type: cross Abstract: Background: Multimodal fracture classifiers may benefit from patient and anatomical metadata, but they can also become brittle when contextual inform...