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Shetty S, Mubarak AS, R David L, Al Jouhari MO, Talaat W, AlKawas S, Al-Rawi N, Shetty S, Uzun Ozsahin D
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
The application of mask region based convolutional neural networks in the detection of nasal septal deviation using cone beam computed tomography images: A proof of concept study
Shishir Shetty;
Auwalu Saleh Mubarak;
Leena R David;
Mhd Omar Al Jouhari;
Wael Talaat;
Sausan AlKawas;
Natheer Al-Rawi;
Sunaina Shetty;
Dilber Uzun Ozsahin
ABSTRACT
Background:
Nasal septal deviation (NSD) is an important key anatomical concern with clinical implications. However, artificial intelligence based radiographic detection of nasal septal deviation has not yet been studied.
Objective:
The objective of this research was to develop and evaluate a real-time model that can detect probable NSD using cone beam tomographic (CBCT) images.
Methods:
Coronal sections images were obtained from 204 full volume CBCT scans. The scans were classified as normal and deviated by two maxillofacial radiologists. The images were then used for training and testing of AI model. Mask region based convolutional neural networks (Mask RCNN) comprising three different backbones ResNet50, ResNet-101 and MobileNet was employed to detect deviated nasal septum in 204 CBCT images. To further improve the detection, an image pre-processing technique (Contrast Enhancement) was added.
Results:
The best performing models CEH-ResNet101 achieved 0.911 mean average precision (mAP). The area under curve (AUC) of CEH-ResNet101 was 0.921.
Conclusions:
The performance of the model shows that the model is capable of detecting nasal septal deviation. Future research in this field can focus on additional preprocessing of images and detection of NSD based on multiple planes using three dimensional images
Citation
Please cite as:
Shetty S, Mubarak AS, R David L, Al Jouhari MO, Talaat W, AlKawas S, Al-Rawi N, Shetty S, Uzun Ozsahin D
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