Deep learning-based detection of incisal translucency patterns.

Abstract

Statement of problem: The evaluation of incisal translucency in anterior teeth greatly influences esthetic treatment outcomes. This evaluation is mostly subjective and often overlooked among dental professionals. The application of artificial intelligence-based models to detect the incisal translucency of anterior teeth may be of value to dentists in their restorative dental practice, but studies are lacking.

Purpose: The purpose of this study was to assess the accuracy of deep learning models in predicting the translucency patterns of anterior teeth.

Material and methods: Approximately 240 Joint Photographic Experts Group (JPEG) images of anterior teeth from participants over 18 years were collected using a smartphone. These images were resized to 224×224 pixels and classified by the presence or absence of translucency. Augmentation techniques enhanced the training dataset, and a 3-model deep learning approach was used: YOLOv5 detected central incisors, Vision Transformers (ViT) identified translucency, and U-Net segmented the translucent areas. The images were split 80 to 20 for training and testing, with performance evaluated using accuracy, precision, recall, F1 score, confusion matrix, and dice scores.

Results: YOLOv5 achieved a precision of 1.00 at a confidence threshold of 0.910. The ViT system showed an accuracy of 91.66%, with 58 of 64 images predicting correctly with an F1 score of 94.83%. U-Net segmentation after training with annotated images achieved an accuracy of 91% with a dice score of 0.948.

Conclusions: The integration of YOLOv5 for detection, ViT for classification, and U-Net for segmentation demonstrates a comprehensive approach to addressing the classification of incisal translucencies. By leveraging the strengths of deep learning models, high accuracy and precision can be achieved in detecting the incisal translucency patterns of anterior teeth.