Abesi Farida, Maleki Mahla, Zamani Mohammad
Department of Oral and Maxillofacial Radiology, Dental Faculty, Babol University of Medical Sciences, Babol, Iran.
Student Research Committee, Babol University of Medical Sciences, Babol, Iran.
Imaging Sci Dent. 2023 Jun;53(2):101-108. doi: 10.5624/isd.20220224. Epub 2023 Mar 24.
The aim of this study was to conduct a scoping review and meta-analysis to provide overall estimates of the recall and precision of artificial intelligence for detection and segmentation using oral and maxillofacial cone-beam computed tomography (CBCT) scans.
A literature search was done in Embase, PubMed, and Scopus through October 31, 2022 to identify studies that reported the recall and precision values of artificial intelligence systems using oral and maxillofacial CBCT images for the automatic detection or segmentation of anatomical landmarks or pathological lesions. Recall (sensitivity) indicates the percentage of certain structures that are correctly detected. Precision (positive predictive value) indicates the percentage of accurately identified structures out of all detected structures. The performance values were extracted and pooled, and the estimates were presented with 95% confidence intervals (CIs).
In total, 12 eligible studies were finally included. The overall pooled recall for artificial intelligence was 0.91 (95% CI: 0.87-0.94). In a subgroup analysis, the pooled recall was 0.88 (95% CI: 0.77-0.94) for detection and 0.92 (95% CI: 0.87-0.96) for segmentation. The overall pooled precision for artificial intelligence was 0.93 (95% CI: 0.88-0.95). A subgroup analysis showed that the pooled precision value was 0.90 (95% CI: 0.77-0.96) for detection and 0.94 (95% CI: 0.89-0.97) for segmentation.
Excellent performance was found for artificial intelligence using oral and maxillofacial CBCT images.
本研究旨在进行一项范围综述和荟萃分析，以提供使用口腔颌面锥形束计算机断层扫描（CBCT）进行检测和分割的人工智能召回率和精度的总体估计。
截至2022年10月31日，在Embase、PubMed和Scopus数据库中进行文献检索，以识别报告使用口腔颌面CBCT图像进行解剖标志或病理病变自动检测或分割的人工智能系统召回率和精度值的研究。召回率（敏感性）表示正确检测到的特定结构的百分比。精度（阳性预测值）表示在所有检测到的结构中准确识别的结构的百分比。提取并汇总性能值，并给出95%置信区间（CI）的估计值。
最终共纳入12项符合条件的研究。人工智能的总体合并召回率为0.91（95%CI：0.87-0.94）。在亚组分析中，检测的合并召回率为0.88（95%CI：0.77-0.94），分割的合并召回率为0.92（95%CI：0.87-0.96）。人工智能的总体合并精度为0.93（95%CI：0.88-0.95）。亚组分析表明，检测的合并精度值为0.90（95%CI：0.77-0.96），分割的合并精度值为0.94（95%CI：0.89-0.97）。
使用口腔颌面CBCT图像的人工智能表现出色。
