Rbl-Sf: A Method With Segformer For Measuring Alveolar Bone

To use deep learning (DL) for precise radiographic bone loss (RBL) measurements in specific tooth positions. Through the design of two tasks for tooth position recognition and tooth semantic segmentation using the SegFormer model, specific tooth positions' crown, intrabony portion, and suprabony portion of the root were obtained. The RBL was then measured by the length through these three areas using the principal component analysis (PCA) principal axis. The average intersection over union (IoU) for the tooth position recognition task was 0.8906, with an F1 score of 0.9338, the average IoU for the tooth semantic segmentation task was 0.8465, with an F1 score of 0.9138, and when combining the two tasks, the average IoU was 0.7889, with an F1 score of 0.8674. The correlation coefficient between the RBL prediction results based on the PCA principal axis and the clinicians’ measurements exceeded 0.85. Compared to the two measurement methods, the average precision of predicted RBL staging was 0.7722, the average sensitivity was 0.7416, and the average F1-score was 0.7444. The method for predicting RBL using DL and PCA produced promising results, offering rapid and reliable auxiliary information for future periodontal disease diagnosis.