AI may help differentiate external cervical resorption lesions from caries lesions
AI may help differentiate external cervical resorption lesions from caries lesions suggests a new study published in the Journal of Endodontics.
The aim of this study was to leverage label-efficient self-supervised learning (SSL) to train a model that can detect ECR and differentiate it from caries.
Periapical (PA) radiographs of teeth with ECR defects were collected. Two board-certified endodontists reviewed PA radiographs and cone beam computed tomographic (CBCT) images independently to determine presence of ECR (ground truth). Radiographic data were divided into three regions of interest (ROIs): healthy teeth, teeth with ECR, and teeth with caries. Nine contrastive SSL models (SimCLR v2, MoCo v2, BYOL, DINO, NNCLR, SwAV, MSN, Barlow Twins, and SimSiam) were implemented in the assessment alongside seven baseline deep learning models (ResNet-18, ResNet-50, VGG16, DenseNet, MobileNetV2, ResNeXt-50, and InceptionV3). A 10-fold cross-validation strategy and a hold-out test set were employed for model evaluation. Model performance was assessed via various metrics including classification accuracy, precision, recall, and F1-score.
Results
Included were 190 PA radiographs, composed of 470 ROIs. Results from 10-fold cross-validation demonstrated that most SSL models outperformed the transfer learning baseline models, with DINO achieving the highest mean accuracy (85.64 ± 4.56), significantly outperforming 13 other models (p<.05). DINO reached the highest test set (i.e., three ROIs) accuracy (84.09%) while MoCo v2 exhibited the highest recall and F1-score (77.37% and 82.93%, respectively).
This study showed that AI can assist clinicians in detecting ECR and differentiating it from caries. Additionally, it introduced the application of SSL in detecting ECR, emphasizing that SSL-based models can outperform transfer learning baselines and reduce reliance on large, labelled datasets.
Reference:
Artificial Intelligence for Detection of External Cervical Resorption Using Label-efficient Self-supervised Learning Method. Hossein Mohammad-Rahimi, Omid Dianat, Reza Abbasi, Saeed Reza Motamedian, MS, Mohammad Hossein Rohban, Ali Nosrat. Show all authors. Published:November 15, 2023DOI:https://doi.org/10.1016/j.joen.2023.11.004
Keywords:
AI, may, help, differentiate, external, cervical, resorption, lesions, from, caries, lesions, Journal of Endodontics, Artificial intelligence, Caries, Diagnosis, Resorption, Self-supervised learning, Hossein Mohammad-Rahimi, Omid Dianat, Reza Abbasi, Saeed Reza Motamedian, MS, Mohammad Hossein Rohban, Ali Nosrat
