Abstract：
BACKGROUND: Automated segmentation of 3-dimensional pulp space on cone-beam computed tomography images presents a significant opportunity for enhancing diagnosis, treatment planning, and clinical education in endodontics. The aim of this systematic review was to investigate the performance of artificial intelligence-driven automated pulp space segmentation on cone-beam computed tomography images.
METHODS: A comprehensive electronic search was performed using PubMed, Web of Science, and Cochrane databases, up until February 2024. Two independent reviewers participated in the selection of studies, data extraction, and evaluation of the included studies. Any disagreements were resolved by a third reviewer. The Quality Assessment of Diagnostic Accuracy Studies-2 tool was used to assess the risk of bias.
RESULTS: Thirteen studies that met the eligibility criteria were included. Most studies demonstrated high accuracy in their respective segmentation methods, although there was some variation across different structures (pulp chamber, root canal) and tooth types (single-rooted, multirooted). Automated segmentation showed slightly superior performance for segmenting the pulp chamber compared to the root canal and single-rooted teeth compared to multi-rooted ones. Furthermore, the second mesiobuccal (MB2) canalsegmentation also demonstrated high performance. In terms of time efficiency, the minimum time required for segmentation was 13 seconds.
CONCLUSIONS: Artificial intelligence-driven models demonstrated outstanding performance in pulp space segmentation. Nevertheless, these findings warrant careful interpretation, and their generalizability is limited due to the potential risk and low evidence level arising from inadequately detailed methodologies and inconsistent assessment techniques. In addition, there is room for further improvement, specifically for root canal segmentation and testing of artificial intelligence performance in artifact-induced images.
METHODS: A comprehensive electronic search was performed using PubMed, Web of Science, and Cochrane databases, up until February 2024. Two independent reviewers participated in the selection of studies, data extraction, and evaluation of the included studies. Any disagreements were resolved by a third reviewer. The Quality Assessment of Diagnostic Accuracy Studies-2 tool was used to assess the risk of bias.
RESULTS: Thirteen studies that met the eligibility criteria were included. Most studies demonstrated high accuracy in their respective segmentation methods, although there was some variation across different structures (pulp chamber, root canal) and tooth types (single-rooted, multirooted). Automated segmentation showed slightly superior performance for segmenting the pulp chamber compared to the root canal and single-rooted teeth compared to multi-rooted ones. Furthermore, the second mesiobuccal (MB2) canalsegmentation also demonstrated high performance. In terms of time efficiency, the minimum time required for segmentation was 13 seconds.
CONCLUSIONS: Artificial intelligence-driven models demonstrated outstanding performance in pulp space segmentation. Nevertheless, these findings warrant careful interpretation, and their generalizability is limited due to the potential risk and low evidence level arising from inadequately detailed methodologies and inconsistent assessment techniques. In addition, there is room for further improvement, specifically for root canal segmentation and testing of artificial intelligence performance in artifact-induced images.
摘要：
背景：在锥形束计算机断层扫描（CBCT）图像上自动分割三维牙髓空间为增强诊断提供了重要的机会，治疗计划,和牙髓临床教育。这项系统评价的目的是研究AI驱动的自动牙髓空间分割在CBCT图像上的性能。
方法：使用PubMed进行了全面的电子搜索，WebofScience,和Cochrane数据库，直到2024年2月。两名独立审稿人参与了研究的选择，数据提取,以及对纳入研究的评价。任何分歧都由第三位审阅者解决。诊断准确性研究质量评估-2（QUADAS-2）工具用于评估偏倚风险。
结果：纳入了符合资格标准的13项研究。大多数研究表明，他们各自的分割方法具有很高的准确性，虽然不同的结构有一些变化(纸浆室，根管)和牙齿类型(单根，多根)。与根管和单根牙齿相比，与多根牙齿相比，自动分割显示出在分割牙髓腔方面的性能稍好。此外，第二近颊（MB2）管分割也显示出高性能。在时间效率方面，分割所需的最短时间为13秒.
结论：AI驱动模型在纸浆空间分割方面表现突出。然而，这些发现值得仔细解释，由于不充分详细的方法和不一致的评估技术所产生的潜在风险和低证据水平，它们的普遍性受到限制。此外,还有进一步改进的空间，特别是用于根管分割和在伪影诱导图像中测试AI性能。
方法：使用PubMed进行了全面的电子搜索，WebofScience,和Cochrane数据库，直到2024年2月。两名独立审稿人参与了研究的选择，数据提取,以及对纳入研究的评价。任何分歧都由第三位审阅者解决。诊断准确性研究质量评估-2（QUADAS-2）工具用于评估偏倚风险。
结果：纳入了符合资格标准的13项研究。大多数研究表明，他们各自的分割方法具有很高的准确性，虽然不同的结构有一些变化(纸浆室，根管)和牙齿类型(单根，多根)。与根管和单根牙齿相比，与多根牙齿相比，自动分割显示出在分割牙髓腔方面的性能稍好。此外，第二近颊（MB2）管分割也显示出高性能。在时间效率方面，分割所需的最短时间为13秒.
结论：AI驱动模型在纸浆空间分割方面表现突出。然而，这些发现值得仔细解释，由于不充分详细的方法和不一致的评估技术所产生的潜在风险和低证据水平，它们的普遍性受到限制。此外,还有进一步改进的空间，特别是用于根管分割和在伪影诱导图像中测试AI性能。
