Artificial intelligence-assisted determination of available sites for palatal orthodontic mini-implants based on palatal thickness through CBCT.

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Abstract

To develop an artificial intelligence (AI) system for automatic palate segmentation through CBCT, and to determine the personalized available sites for palatal mini-implants by measuring palatal bone and soft tissue thickness according to the AI predicted results.8400 target slices (from 70 CBCT scans) from orthodontic patients were collected, labeled by well-trained orthodontists and randomly divided into two groups: a training set and a test set. After the deep learning process, we evaluated the performance of our deep learning model with the mean Dice similarity coefficient (DSC), average symmetric surface distance (ASSD), sensitivity (SEN), positive predictive value (PPV) and mean thickness percentage error (MTPE). The pixel traversal method was proposed to measure the thickness of palatal bone and soft tissue, and to predict available sites for palatal orthodontic mini-implants. Then, an example of available sites for palatal mini-implants from test set was mapped.The average DSC, ASSD, SEN, PPV and MTPE for the segmented palatal bone tissue were 0.831, 1.122, 0.876,0.815, 6.70% while that for the palatal soft tissue were 0.741, 1.091, 0.861, 0.695, 12.2% respectively. Besides, an example of available sites for palatal mini-implants was mapped according to predefined criteria.Our AI system showed high accuracy for palatal segmentation and thickness measurement, which is helpful for the determination of available sites and the design of surgical guide for palatal orthodontic mini-implants.This article is protected by copyright. All rights reserved.