Automated Skeletal Classification with Lateral Cephalometry Based on Artificial Intelligence

Abstract

Lateral cephalometry has been widely used for skeletal classification in orthodontic diagnosis and treatment planning. However, this conventional system, requiring manual tracing of individual landmarks, contains possible errors of inter- and intravariability and is highly time-consuming. This study aims to provide an accurate and robust skeletal diagnostic system by incorporating a convolutional neural network (CNN) into a 1-step, end-to-end diagnostic system with lateral cephalograms. A multimodal CNN model was constructed on the basis of 5,890 lateral cephalograms and demographic data as an input. The model was optimized with transfer learning and data augmentation techniques. Diagnostic performance was evaluated with statistical analysis. The proposed system exhibited >90% sensitivity, specificity, and accuracy for vertical and sagittal skeletal diagnosis. Clinical performance of the vertical classification showed the highest accuracy at 96.40 (95% CI, 93.06 to 98.39; model III). The receiver operating characteristic curve and the area under the curve both demonstrated the excellent performance of the system, with a mean area under the curve >95%. The heat maps of cephalograms were also provided for deeper understanding of the quality of the learned model by visually representing the region of the cephalogram that is most informative in distinguishing skeletal classes. In addition, we present broad applicability of this system through subtasks. The proposed CNN-incorporated system showed potential for skeletal orthodontic diagnosis without the need for intermediary steps requiring complicated diagnostic procedures.

Automated cephalometric landmark detection with confidence regions using Bayesian convolutional neural networks

“…Through the last decade, various clinical fields have reported an increase in clinical efficiency according to the application of artificial intelligence. In particular, recent studies in the dental field have shown excellent performance in clinical applications as a diagnostic aid system for deep learning models [14,15]. Many deep learning based computer aided landmark detection studies have performed better than previous studies.…”

Artificial intelligence-driven novel tool for tooth detection and segmentation on panoramic radiographs

“…In this way, images can be used as an input for the neural networks in order to achieve several different outputs [15]. As such, deep learning methods have already shown promising results for detecting caries [13], root fractures [9], periodontal diseases [14], for differentiating cysts and jaw tumors [8], for skeletal classification on lateral cephalograms [31], and even for improving oral cancer outcomes [32]. Regarding teeth and bone segmentation, deep learning is an encouraging approach to segment anatomical structures and later on in clinical decision making [5].…”

A Deep Learning Approach to Automatic Gingivitis Screening based Onclassification and Localization in RGB Photos

“…al. also evaluated the performance of CNNs for the skeletal classi cation with lateral cephalometry 15 . Different from the previous work, our task takes imageries as input, which can be less standard in distribution comparing to the medical imaging.…”