ArticleDiagnostics (Basel, Switzerland)2022
Convolutional Neural Network Performance for Sella Turcica Segmentation and Classification Using CBCT Images.
Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Automated Sella-Turcica Annotation and Mesh Alignment of 3D Stereophotographs for Craniosynostosis Patients Using a PCA-FFNN Based Approach.The Journal of craniofacial surgery · 2026Article
- Applications of artificial intelligence in diagnosis and treatment planning of orthodontics: a narrative review.The Saudi dental journal · 2025Review
- Development of artificial neural network model for predicting the rapid maxillary expansion technique in children with cleft lip and palate.Frontiers in dental medicine · 2025Article
- Prediction of midpalatal suture maturation stage based on transfer learning and enhanced vision transformer.BMC medical informatics and decision making · 2024Article
- Automatic segmentation and classification of frontal sinuses for sex determination from CBCT scans using a two-stage anatomy-guided attention network.Scientific reports · 2024Article
- Artificial Intelligence Systems Assisting in the Assessment of the Course and Retention of Orthodontic Treatment.Healthcare (Basel, Switzerland) · 2023Review
- A Novel Deep Learning-Based Approach for Segmentation of Different Type Caries Lesions on Panoramic Radiographs.Diagnostics (Basel, Switzerland) · 2023Article
- Automatic Feature Segmentation in Dental Periapical Radiographs.Diagnostics (Basel, Switzerland) · 2022Article
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10 authors.
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Abstract
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.
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