Department of Oral and Craniofacial Health Sciences, College of Dental Medicine, University of Sharjah, Sharjah, United Arab Emirates.
Operational Research Center in Healthcare, Near East University, Nicosia, Turkey.
JMIR Form Res. 2024 Sep 3;8:e57335. doi: 10.2196/57335.
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.
This research aimed to develop and evaluate a real-time model that can detect probable NSD using cone beam computed tomography (CBCT) images.
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.
The best-performing model-CEH-ResNet101-achieved a mean average precision of 0.911, with an area under the curve of 0.921.
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.
人工智能（AI）模型越来越多地被用于检测不同成像模式中的变异和病变。鼻中隔偏曲（NSD）是一种具有临床意义的重要解剖结构。然而，基于 AI 的 NSD 放射学检测尚未得到研究。
本研究旨在开发和评估一种实时模型，该模型可以使用锥形束计算机断层扫描（CBCT）图像检测可能的 NSD。
从 204 个全容积 CBCT 扫描中获取冠状截面图像。由 2 名颌面放射科医生对扫描进行正常和偏曲分类。然后，使用这些图像来训练和测试 AI 模型。使用基于掩模区域的卷积神经网络（Mask R-CNN），包含 3 种不同的骨干网络（ResNet50、ResNet101 和 MobileNet），用于检测 204 个 CBCT 图像中的鼻中隔偏曲。为了进一步提高检测性能，添加了图像预处理技术（对比度增强 [CEH]）。
表现最佳的模型（CEH-ResNet101）的平均精度为 0.911，曲线下面积为 0.921。
模型的性能表明，该模型能够检测鼻中隔偏曲。未来在该领域的研究应集中于对图像进行额外的预处理，并使用 3D 图像基于多个平面检测 NSD。
