Panoramic Radiograph–based Deep Learning Models for Diagnosis and Clinical Decision Support of Furcation Lesions in Primary Molars

Abstract Background: Furcation lesions in primary molars are critical in pediatric dentistry as they often guide decisions between root canal treatment and extraction. This study introduces a novel deep learning-based clinical decision-support system that directly maps radiographic lesion characteristics to corresponding treatment recommendations—a first in the context of pediatric dental imaging. Methods: A total of 387 anonymized panoramic radiographs from children aged 3–13 was labeled into five distinct bone lesion categories. Three object detection models (YOLOv12x, RT-DETR-L, and RT-DETR-X) were trained and evaluated using stratified train-validation-test splits. Diagnostic performance was assessed using precision, recall, mAP@0.5, and mAP@0.5–0.95. Additionally, qualitative accuracy was evaluated with expert-annotated samples. Results: Among the models, RT-DETR-X achieved the highest accuracy (mAP@0.5 = 0.434). All models successfully identified lesion types and supported corresponding clinical decisions. The system reduced diagnostic ambiguity and showed promise in supporting clinicians with varying levels of experience. Conclusions: This study presents the first deep learning–based decision-support framework that links primary molar furcation lesion classification with treatment planning using panoramic radiographs. The proposed models have potential for standardizing diagnostic outcomes, especially in resource-limited settings and mobile clinical environments.