| Autor: |
Duman ŞB; Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Inonu University, 44210 Malatya, Turkey., Syed AZ; Department of Oral and Maxillofacial Medicine and Diagnostic Sciences, School of Dental Medicine, Case Western Reserve University, Cleveland, OH 44106, USA., Celik Ozen D; Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Inonu University, 44210 Malatya, Turkey., Bayrakdar İŞ; Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Eskişehir Osmangazi University, 26040 Eskişehir, Turkey.; Department of Center of Research and Application for Computer Aided Diagnosis and Treatment in Health, Eskişehir Osmangazi University, 26040 Eskişehir, Turkey., Salehi HS; Department of Electrical and Computer Engineering, California State University, Chico, CA 95929, USA., Abdelkarim A; Department of Oral and Maxillofacial Radiology, University of Texas Health Sciences Center at San Antonio, San Antonio, TX 79229, USA., Celik Ö; Department of Center of Research and Application for Computer Aided Diagnosis and Treatment in Health, Eskişehir Osmangazi University, 26040 Eskişehir, Turkey.; Department of Mathematics-Computer, Eskişehir Osmangazi University Faculty of Science, 26040 Eskişehir, Turkey., Eser G; Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Inonu University, 44210 Malatya, Turkey., Altun O; Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Inonu University, 44210 Malatya, Turkey., Orhan K; Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Ankara University, 06100 Ankara, Turkey.; Ankara University Medical Design Application and Research Center (MEDITAM), Ankara University, 06100 Ankara, Turkey.; Department of Dental and Maxillofacial Radiodiagnostics, Medical University of Lublin, 20-001 Lublin, Poland. |
| Abstrakt: |
The present study aims to validate the diagnostic performance and evaluate the reliability of an artificial intelligence system based on the convolutional neural network method for the morphological classification of sella turcica in CBCT (cone-beam computed tomography) images. In this retrospective study, sella segmentation and classification models (CranioCatch, Eskisehir, Türkiye) were applied to sagittal slices of CBCT images, using PyTorch supported by U-Net and TensorFlow 1, and we implemented the GoogleNet Inception V3 algorithm. The AI models achieved successful results for sella turcica segmentation of CBCT images based on the deep learning models. The sensitivity, precision, and F-measure values were 1.0, 1.0, and 1.0, respectively, for segmentation of sella turcica in sagittal slices of CBCT images. The sensitivity, precision, accuracy, and F1-score were 1.0, 0.95, 0.98, and 0.84, respectively, for sella-turcica-flattened classification; 0.95, 0.83, 0.92, and 0.88, respectively, for sella-turcica-oval classification; 0.75, 0.94, 0.90, and 0.83, respectively, for sella-turcica-round classification. It is predicted that detecting anatomical landmarks with orthodontic importance, such as the sella point, with artificial intelligence algorithms will save time for orthodontists and facilitate diagnosis. |
