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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: Proof-of-Concept Study

NSD was determined by the method used by Shetty et al [11] and Al-Rawi et al [18] (Figure 2). The annotations were done using the Visual Geometry Group Image Annotator manual annotation open-source software (Figure 3) [19]. After annotation, the data (cropped coronal images) were used for training (80%) and testing (20%) of the AI model. In training the models, we only considered images with a deviated septum and discarded images with a nondeviated septum.

Shishir Shetty, Auwalu Saleh Mubarak, Leena R David, Mhd Omar Al Jouhari, Wael Talaat, Natheer Al-Rawi, Sausan AlKawas, Sunaina Shetty, Dilber Uzun Ozsahin

JMIR Form Res 2024;8:e57335

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