Automated assessment of nasal septum deviation using cone-beam computed tomography images based on artificial intelligence: Development and multicenter validation.
Authors
Affiliations (4)
Affiliations (4)
- Department of Orthodontics, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
- Department of Data Center, Affiliated Hospital of Jining Medical University, Jining, China.
- ENT Institute and Department of Otorhinolaryngology, Eye and ENT Hospital, Fudan University, Shanghai, China.
- Department of Orthodontics, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. Electronic address: [email protected].
Abstract
Nasal septum deviation (NSD) is one of the contributing factors to impaired nasal function and dentofacial developmental abnormalities. Although cone-beam computed tomography (CBCT) is clinically valuable for NSD diagnosis, manual interpretation remains labor-intensive and expertise-dependent. Our study included 330 CBCT scans diagnosed with either NSD or non-NSD to develop an automated 2-stage artificial intelligence (AI) framework integrating real-time detection and classification for NSD screening. In the first stage, the YOLOv11 (You Only Look Once) object detection algorithm was employed to detect the region of interest containing the nasal septum. In the second stage, 3 convolutional neural network architectures, ResNet, EfficientNet, and MobileNet, were evaluated for classifying CBCT images into NSD and normal categories. Among the YOLOv11 variants, YOLOv11n demonstrated superior performance with a precision of 0.996, a recall of 1.000, an mAP50 of 0.995, and an mAP50-95 of 0.873. For the classification task, Mobile_small emerged as the top-performing model, achieving an area under the curve of 0.817, an area under the precision-recall curve of 0.845, and an accuracy of 0.749. An AI-assisted diagnostic tool was developed based on YOLOv11n and MobileNet models and validated on 50 internal and 50 external CBCT scans. With AI assistance, orthodontists' diagnostic accuracy increased by 20.12% and 21.49%, respectively, whereas average diagnosis time decreased by 23.75 seconds, improving efficiency by 53.92%. The proposed system enables rapid NSD screening with diagnostic-level accuracy, demonstrating the viability of lightweight AI models for clinical CBCT analysis. AI-assisted diagnosis improves orthodontists' accuracy and time efficiency in identifying NSD.