人工智能在口腔颌面部锥形束计算机断层扫描成像中的诊断性能:范围综述和荟萃分析。

IF 1.7 Q3 DENTISTRY, ORAL SURGERY & MEDICINE
Imaging Science in Dentistry Pub Date : 2023-06-01 Epub Date: 2023-03-24 DOI:10.5624/isd.20220224
Farida Abesi, Mahla Maleki, Mohammad Zamani
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引用次数: 0

摘要

目的:本研究旨在进行范围综述和荟萃分析,以提供人工智能使用口腔颌面锥束计算机断层扫描(CBCT)进行检测和分割的召回率和精确度的总体估计值:截至 2022 年 10 月 31 日,我们在 Embase、PubMed 和 Scopus 中进行了文献检索,以找出报告人工智能系统使用口腔颌面 CBCT 图像自动检测或分割解剖标志物或病变的召回率和精确度值的研究。召回率(灵敏度)表示正确检测到某些结构的百分比。精确度(阳性预测值)表示在所有检测到的结构中准确识别出结构的百分比。对性能值进行提取和汇总,并用 95% 的置信区间(CIs)表示估计值:结果:最终共纳入了 12 项符合条件的研究。人工智能的总体汇总召回率为 0.91(95% CI:0.87-0.94)。在亚组分析中,检测的集合召回率为 0.88(95% CI:0.77-0.94),分割的集合召回率为 0.92(95% CI:0.87-0.96)。人工智能的总体汇总精确度为 0.93(95% CI:0.88-0.95)。亚组分析显示,检测的集合精确度值为 0.90(95% CI:0.77-0.96),分割的集合精确度值为 0.94(95% CI:0.89-0.97):结论:人工智能在口腔颌面 CBCT 图像上的表现非常出色。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Diagnostic performance of artificial intelligence using cone-beam computed tomography imaging of the oral and maxillofacial region: A scoping review and meta-analysis.

Diagnostic performance of artificial intelligence using cone-beam computed tomography imaging of the oral and maxillofacial region: A scoping review and meta-analysis.

Diagnostic performance of artificial intelligence using cone-beam computed tomography imaging of the oral and maxillofacial region: A scoping review and meta-analysis.

Diagnostic performance of artificial intelligence using cone-beam computed tomography imaging of the oral and maxillofacial region: A scoping review and meta-analysis.

Purpose: 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.

Materials and methods: 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).

Results: 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.

Conclusion: Excellent performance was found for artificial intelligence using oral and maxillofacial CBCT images.

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来源期刊
Imaging Science in Dentistry
Imaging Science in Dentistry DENTISTRY, ORAL SURGERY & MEDICINE-
CiteScore
2.90
自引率
11.10%
发文量
42
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