Decision Support System for Orthgnathic diagnosis and treatment planning based on machine learning

Abstract

Objectives This study aims to establish a machine learn-based decision support system for the treatment of dental and maxillofacial malformations and verify its feasibility. Materials and Methods Between January 2015 to August 2020, 574 patients with spiral CT and have confirmed diagnosis of dento-maxillofacial deformities were used to train the diagnostic model based on five kind of machine learning algorithm. The diagnostic performance of the algorithm was compared with the ground truth and evaluated by accuracy, sensitivity, specifcity, and area under the ROC curve (AUC). The adaptive artificial bee colony (aABC) algorithm was used to calculate the orthognathic surgical plan based on cephalometrics in the normal population. The algorithm was used to automatically generate surgical plans for 50 patients and the plans were rated by three experienced maxillofacial surgeons. Results The binary relevance extreme gradient boosting model delivered the best overall performance. The final diagnosis success rates on six different kinds of maxillofacial deformities were all over 90%, except for maxillary overdevelopment (89.27%). Conclusions The machine learning algorithms show high accuracy and effectiveness in diagnosis and surgical plan design of dento-maxillofacial deformities. The AUC of all diagnostic types was greater than 0.88. The median score for surgical plan was 9. The scheme scores were further improved after human-computer interaction. Clinical Relevance The decision support system based on machine learning could be used for automatic diagnosis and surgical design for patients with dento-maxillofacial deformities, which will help improving diagnostic efciency and provide expertise to areas with lower medical levels.