(untitled) (Academic Journal of International University of Erbil, 2025)
Root canal morphology, particularly the curvature of the root canals, plays a crucial role in the efficacy of endodontic diagnosis and treatment planning. Yet, assessing this morphology typically relies on manual annotation and interpretation of dental radiographs, which can be time-consuming and prone to human error. These challenges are especially pronounced in low-resource settings such as Kurdistan, the northern part of Iraq, where access to precision imaging tools is limited. This study proposes a novel AI-based framework for classifying mandibular distal root canal curvature. Furthermore, it introduces a semi-automated annotation pipeline that integrates Schneider’s method with AutoCAD, facilitating the labeling of root canal curvatures for deep learning applications. Initially, a total number of 11,490 OPG images were gathered from public and private clinics. After undergoing rigorous preprocessing, 1,166 OPG images were selected and categorized into three classes: straight, moderate, and severe, based on Schneider’s method for root canal curvature classification. Three pre-trained convolutional neural network models — ResNet-101, DenseNet-121, and EfficientNet-B0 — were fine-tuned on the collected OPGs. ResNet-101 was evaluated using a fivefold cross-validation test, while DenseNet-121 and EfficientNet-B0 were fine-tuned using a two-phase transfer learning strategy. In this approach, the initial feature extractor was first frozen, followed by fine-tuning of the whole model. To address the limited sample size, data augmentation techniques were also employed to mitigate overfitting. Among those three models, ResNet-101 achieved the highest classification accuracy, 0.907, and an F1-score of 0.907, demonstrating superior ability to capture anatomical features. These findings underscore the effectiveness and promising role of deep learning in supporting dental diagnosis, particularly in endodontics. The proposed AI framework, combined with a semi-automated annotation approach, has the potential to improve the scalability and accessibility of root canal morphology assessment even in low-resource clinical settings.