Validation Analysis of Panoramic Dental Application (PDApp) Software as a Tool for Predicting Third Molar Eruption Based on Panoramic Radiograph Images
The decision-making process for third molar removal or maintenance remains controversial in dental practice. The most important variables to be analyzed in predicting the potential of third molar eruption are retromolar space and the direction of eruption. The various methods for prediction include linear measures: measurement of the available space, mandibular size and growth, size of the third molar, and third molar angulation. The available software is not suitable for predicting third molar eruption. The purpose of the present work was to develop a clinical tool that can automatically predict eruption of the third molars based on combined linear and angular measurements. In this paper, the development and validation analysis of Panoramic Dental Application (PDApp) software (registered by the University of Santiago de Compostela (USC)) is presented, which can automatically predict third molar eruption from panoramic radiographs. This prediction is performed using a machine learning classifier (a support vector machine with Gaussian kernel) trained on a set of 188 cases wherein third molar angulation and the radiological retention coefficient are used as input data. Operating in the daily practice of the School of Dentistry at USC, an accuracy of 97.96% in predicting the potential of third molar eruption is achieved for a set of 539 third molars belonging to 289 patients. The software was also rated as the best imaginable system by the system usability scale (SUS) questionnaire. In this study, we developed and analyzed a new, unique software tool with increased diagnostic accuracy that will facilitate and optimize dental care in routine clinical workflow.
keywords: artificial intelligence, Machine Learning, Oral radiology, Panoramic radiography, Third molar,