BATUHAN KULELİ MEHMET UĞURLU
Aydın Dental Journal - 2024;10(1):1-7
ABSTRACT Objectives: The aim of this study is to investigate the success of pharyngeal airway detection using a special artificial intelligence algorithm on lateral cephalometric images obtained from cone beam computed tomography images. Materials and Methods: The data set of our study was performed on the lateral cephalometric radiographs was obtained from cone beam computed tomography images of 1040 patients before orthodontic treatment using a special artificial intelligence algorithm and the segmentation method were applied with the free drawing tchnique and the pharyngeal airway was determined. Airway labeling on images was done using CranioCatch annotation software (CranioCatch, Eskişehir, Turkey). Results: The artificial intelligence model was trained with the Yolov5x model as 500 epochs and 0.01 learning rate. Sensitivity, precision and F1 scores in the artifical intelligence model trained in the study were 1, 0.9903 and 0.9951 respectively. Conclusion: The model in which we evaluated the pharyngeal airway was generally successful. Our study is promising for the development of future CBCT reporting systems. It is thought that these deep learning-based systems will save physicians time as a decision support mechanism in routine clinical practices. It is also anticipated that it will help in minimizing interobserver differences in the evaluation of the pharyngeal airway and inconsistencies that may occur in the evaluations made by observers at different times.
