The concept of AI-assisted self-monitoring for skeletal malocclusion.

Abstract

Background: Skeletal malocclusion is common among populations. Its severity often increases during adolescence, yet it is frequently overlooked. The introduction of deep learning in stomatology has opened a new avenue for self-health management. Methods: In this study, networks were trained using lateral photographs of 2109 newly diagnosed patients. The performance of the models was thoroughly evaluated using various metrics, such as sensitivity, specificity, accuracy, confusion matrix analysis, the receiver operating characteristic curve, and the area under the curve value. Heat maps were generated to further interpret the models' decisions. A comparative analysis was performed to assess the proposed models against the expert judgment of orthodontic specialists. Results: The modified models reached an impressive average accuracy of 84.50% (78.73%-88.87%), with both sex and developmental stage information contributing to the AI system's enhanced performance. The heat maps effectively highlighted the distinct characteristics of skeletal class II and III malocclusion in specific regions. In contrast, the specialist achieved a mean accuracy of 71.89% (65.25%-77.64%). Conclusions: Deep learning appears to be a promising tool for assisting in the screening of skeletal malocclusion. It provides valuable insights for expanding the use of AI in self-monitoring and early detection within a family environment.