Evaluation of AI Model for Cephalometric Landmark Classification (TG Dental).

The accuracy of cephalometric landmark identification for malocclusion classification is essential for diagnosis and treatment planning. Identifying these landmarks is often complex and time-consuming for orthodontists. An AI model for classification was recently developed. This model was investigated based on current regulatory considerations as a result of the strict regulations on software systems and the lack of information on artificial intelligence (AI) requirements in this publication. The platform developed by the ITU/WHO for AI is used to assess the models of the application. The auditing procedure assessed the development process concerning medical device regulations, data protection regulations, and ethical considerations. Upon that, the major tasks during the development were evaluated, such as qualification, annotation procedure, and data set attributes. The AI models were investigated under consideration of technical, clinical, regulatory, and ethical considerations. The risk to the patient and user's health can be considered low according to the International Medical Device Regulators Forum (IMDRF) definition. This application facilitates the decision and planning of malocclusion treatment based on lateral cephalograms without cephalometric landmarks. It is comparable with common standards in orthodontic diagnosis.

Deep learning for the classification of cervical maturation degree and pubertal growth spurts:A pilot study / 대한치과교정학회지

Objective@#This study aimed to present and evaluate a new deep learning model for determining cervical vertebral maturation (CVM) degree and growth spurts by analyzing lateral cephalometric radiographs. @*Methods@#The study sample included 890 cephalograms. The images were classified into six cervical stages independently by two orthodontists. The images were also categorized into three degrees on the basis of the growth spurt: pre-pubertal, growth spurt, and post-pubertal. Subsequently, the samples were fed to a transfer learning model implemented using the Python programming language and PyTorch library. In the last step, the test set of cephalograms was randomly coded and provided to two new orthodontists in order to compare their diagnosis to the artificial intelligence (AI) model’s performance using weighted kappa and Cohen’s kappa statistical analyses. @*Results@#The model’s validation and test accuracy for the six-class CVM diagnosis were 62.63% and 61.62%, respectively. Moreover, the model’s validation and test accuracy for the three-class classification were 75.76% and 82.83%, respectively. Furthermore, substantial agreements were observed between the two orthodontists as well as one of them and the AI model. @*Conclusions@#The newly developed AI model had reasonable accuracy in detecting the CVM stage and high reliability in detecting the pubertal stage. However, its accuracy was still less than that of human observers. With further improvements in data quality, this model should be able to provide practical assistance to practicing dentists in the future.