TY  - JOUR
AU  - Shetty, Shishir
AU  - Mubarak, Auwalu Saleh
AU  - R David, Leena
AU  - Al Jouhari, Mhd Omar
AU  - Talaat, Wael
AU  - Al-Rawi, Natheer
AU  - AlKawas, Sausan
AU  - Shetty, Sunaina
AU  - Uzun Ozsahin, Dilber
PY  - 2024
DA  - 2024/9/3
TI  - The Application of Mask Region-Based Convolutional Neural Networks in the Detection of Nasal Septal Deviation Using Cone Beam Computed Tomography Images: Proof-of-Concept Study
JO  - JMIR Form Res
SP  - e57335
VL  - 8
KW  - convolutional neural networks
KW  - nasal septal deviation
KW  - cone beam computed tomography
KW  - tomographic
KW  - tomography
KW  - nasal
KW  - nose
KW  - face
KW  - facial
KW  - image
KW  - images
KW  - imagery
KW  - artificial intelligence
KW  - CNN
KW  - neural network
KW  - neural networks
KW  - ResNet
AB  - Background: Artificial intelligence (AI) models are being increasingly studied for the detection of variations and pathologies in different imaging modalities. Nasal septal deviation (NSD) is an important anatomical structure with clinical implications. However, AI-based radiographic detection of NSD has not yet been studied. Objective: This research aimed to develop and evaluate a real-time model that can detect probable NSD using cone beam computed tomography (CBCT) images. Methods: Coronal section images were obtained from 204 full-volume CBCT scans. The scans were classified as normal and deviated by 2 maxillofacial radiologists. The images were then used to train and test the AI model. Mask region-based convolutional neural networks (Mask R-CNNs) comprising 3 different backbones—ResNet50, ResNet101, and MobileNet—were used to detect deviated nasal septum in 204 CBCT images. To further improve the detection, an image preprocessing technique (contrast enhancement [CEH]) was added. Results: The best-performing model—CEH-ResNet101—achieved a mean average precision of 0.911, with an area under the curve of 0.921. Conclusions: The performance of the model shows that the model is capable of detecting nasal septal deviation. Future research in this field should focus on additional preprocessing of images and detection of NSD based on multiple planes using 3D images. 
SN  - 2561-326X
UR  - https://formative.jmir.org/2024/1/e57335
UR  - https://doi.org/10.2196/57335
UR  - http://www.ncbi.nlm.nih.gov/pubmed/39226096
DO  - 10.2196/57335
ID  - info:doi/10.2196/57335
ER  -