目的:评估深度学习(DL)在检测中的表现,分类,上颌窦疾病的分割。
方法:两名审稿人对包括PubMed在内的数据库进行了电子搜索,Scopus,科克伦,IEEE。所有不迟于2024年2月7日发表的英语论文都进行了评估。还在手动期刊上搜索了与DL相关的诊断上颌窦疾病的研究。
结果:1167项研究中有14项符合纳入标准。所有研究都基于射线照相图像训练DL模型。六项研究应用于检测任务,一个专注于分类,两个分段的病变,5项研究结合了2种类型的DL模型。DL算法的准确率从75.7%到99.7%不等,曲线下面积(AUC)在0.7和0.997之间变化。
结论:DL可以准确地处理上颌窦疾病的诊断任务。学生,居民,和牙医可以通过DL算法来辅助诊断并对与上颌窦相关的种植治疗做出合理的决定。
OBJECTIVE: To assess the performance of deep learning (DL) in the detection, classification, and segmentation of maxillary sinus diseases.
METHODS: An electronic search was conducted by two reviewers on databases including PubMed, Scopus, Cochrane, and IEEE. All English papers published no later than February 7, 2024, were evaluated. Studies related to DL for diagnosing maxillary sinus diseases were also searched in journals manually.
RESULTS: Fourteen of 1167 studies were eligible according to the inclusion criteria. All studies trained DL models based on radiographic images. Six studies applied to detection tasks, one focused on classification, two segmented lesions, and five studies made a combination of two types of DL models. The accuracy of the DL algorithms ranged from 75.7% to 99.7%, and the area under curves (AUC) varied between 0.7 and 0.997.
CONCLUSIONS: DL can accurately deal with the tasks of diagnosing maxillary sinus diseases. Students, residents, and dentists could be assisted by DL algorithms to diagnose and make rational decisions on implant treatment related to maxillary sinuses.