Department of Oral and Dentomaxillofacial Radiology, Faculty of Dentistry, Inonu University, Malatya, Turkey.
Department of Oral and Maxillofacial Medicine and Diagnostic Sciences, School of Dental Medicine, Case Western Reserve University, Cleveland, OH, USA.
BMC Oral Health. 2024 Oct 10;24(1):1208. doi: 10.1186/s12903-024-04924-0.
Maxillofacial complex automated segmentation could alternative traditional segmentation methods to increase the effectiveness of virtual workloads. The use of DL systems in the detection of maxillary sinus and pathologies will both facilitate the work of physicians and be a support mechanism before the planned surgeries.
The aim was to use a modified You Only Look Oncev5x (YOLOv5x) architecture with transfer learning capabilities to segment both maxillary sinuses and maxillary sinus diseases on Cone-Beam Computed Tomographic (CBCT) images.
Data set consists of 307 anonymised CBCT images of patients (173 women and 134 males) obtained from the radiology archive of the Department of Oral and Maxillofacial Radiology. Bilateral maxillary sinuses CBCT scans were used to identify mucous retention cysts (MRC), mucosal thickenings (MT), total and partial opacifications, and healthy maxillary sinuses without any radiological features.
Recall, precision and F1 score values for total maxillary sinus segmentation were 1, 0.985 and 0.992, respectively; 1, 0.931 and 0.964 for healthy maxillary sinus segmentation; 0.858, 0.923 and 0.889 for MT segmentation; 0.977, 0.877 and 0.924 for MRC segmentation; 1, 0.942 and 0.970 for sinusitis segmentation.
This study demonstrates that maxillary sinuses can be segmented, and maxillary sinus diseases can be accurately detected using the AI model.
颌面复合体自动分割可以替代传统的分割方法，以提高虚拟工作量的效率。在检测上颌窦和病变方面使用深度学习系统，将有助于医生的工作，并在计划手术前提供支持机制。
本研究旨在使用具有迁移学习能力的改进型 You Only Look Once v5x（YOLOv5x）架构对上颌窦和上颌窦疾病进行 Cone-Beam Computed Tomographic（CBCT）图像分割。
数据集由来自口腔颌面放射科放射学档案的 307 名匿名 CBCT 图像组成，包括 173 名女性和 134 名男性。使用双侧上颌窦 CBCT 扫描来识别黏液潴留囊肿（MRC）、黏膜增厚（MT）、完全和部分混浊以及没有任何放射学特征的健康上颌窦。
总上颌窦分割的召回率、精度和 F1 分数值分别为 1、0.985 和 0.992；健康上颌窦分割分别为 1、0.931 和 0.964；MT 分割分别为 0.858、0.923 和 0.889；MRC 分割分别为 0.977、0.877 和 0.924；鼻窦炎分割分别为 1、0.942 和 0.970。
本研究表明，使用 AI 模型可以对上颌窦进行分割，并准确检测上颌窦疾病。
