Abstract：
BACKGROUND: The recent impact of artificial intelligence in diagnostic services has been enormous. Machine learning tools offer an innovative alternative to diagnose cysts and tumors radiographically that pose certain challenges due to the near similar presentation, anatomical variations, and superimposition. It is crucial that the performance of these models is evaluated for their clinical applicability in diagnosing cysts and tumors.
METHODS: A comprehensive literature search was carried out on eminent databases for published studies between January 2015 and December 2022. Studies utilizing machine learning models in the diagnosis of odontogenic cysts or tumors using Orthopantomograms (OPG) or Cone Beam Computed Tomographic images (CBCT) were included. QUADAS-2 tool was used for the assessment of the risk of bias and applicability concerns. Meta-analysis was performed for studies reporting sufficient performance metrics, separately for OPG and CBCT.
RESULTS: 16 studies were included for qualitative synthesis including a total of 10,872 odontogenic cysts and tumors. The sensitivity and specificity of machine learning in diagnosing cysts and tumors through OPG were 0.83 (95% CI 0.81-0.85) and 0.82 (95% CI 0.81-0.83) respectively. Studies utilizing CBCT noted a sensitivity of 0.88 (95% CI 0.87-0.88) and specificity of 0.88 (95% CI 0.87-0.89). Highest classification accuracy was 100%, noted for Support Vector Machine classifier.
CONCLUSIONS: The results from the present review favoured machine learning models to be used as a clinical adjunct in the radiographic diagnosis of odontogenic cysts and tumors, provided they undergo robust training with a huge dataset. However, the arduous process, investment, and certain ethical concerns associated with the total dependence on technology must be taken into account. Standardized reporting of outcomes for diagnostic studies utilizing machine learning methods is recommended to ensure homogeneity in assessment criteria, facilitate comparison between different studies, and promote transparency in research findings.
METHODS: A comprehensive literature search was carried out on eminent databases for published studies between January 2015 and December 2022. Studies utilizing machine learning models in the diagnosis of odontogenic cysts or tumors using Orthopantomograms (OPG) or Cone Beam Computed Tomographic images (CBCT) were included. QUADAS-2 tool was used for the assessment of the risk of bias and applicability concerns. Meta-analysis was performed for studies reporting sufficient performance metrics, separately for OPG and CBCT.
RESULTS: 16 studies were included for qualitative synthesis including a total of 10,872 odontogenic cysts and tumors. The sensitivity and specificity of machine learning in diagnosing cysts and tumors through OPG were 0.83 (95% CI 0.81-0.85) and 0.82 (95% CI 0.81-0.83) respectively. Studies utilizing CBCT noted a sensitivity of 0.88 (95% CI 0.87-0.88) and specificity of 0.88 (95% CI 0.87-0.89). Highest classification accuracy was 100%, noted for Support Vector Machine classifier.
CONCLUSIONS: The results from the present review favoured machine learning models to be used as a clinical adjunct in the radiographic diagnosis of odontogenic cysts and tumors, provided they undergo robust training with a huge dataset. However, the arduous process, investment, and certain ethical concerns associated with the total dependence on technology must be taken into account. Standardized reporting of outcomes for diagnostic studies utilizing machine learning methods is recommended to ensure homogeneity in assessment criteria, facilitate comparison between different studies, and promote transparency in research findings.
摘要：
背景：人工智能在诊断服务中的最新影响是巨大的。机器学习工具提供了一种创新的替代方法来诊断囊肿和肿瘤，这些囊肿和肿瘤由于几乎相似的表现而面临某些挑战。解剖变异，和叠加。评估这些模型在诊断囊肿和肿瘤中的临床适用性是至关重要的。
方法：对2015年1月至2022年12月期间发表的研究的著名数据库进行了全面的文献检索。包括使用机器学习模型在使用直视图（OPG）或锥形束计算机断层扫描图像（CBCT）诊断牙源性囊肿或肿瘤中的研究。QUADAS-2工具用于评估偏倚风险和适用性问题。对报告足够性能指标的研究进行了荟萃分析，分别用于OPG和CBCT。
结果：共纳入了16项定性合成研究，包括10,872例牙源性囊肿和肿瘤。机器学习通过OPG诊断囊肿和肿瘤的敏感性和特异性分别为0.83（95％CI0.81-0.85）和0.82（95％CI0.81-0.83）。使用CBCT的研究指出，灵敏度为0.88（95％CI0.87-0.88），特异性为0.88（95％CI0.87-0.89）。最高分类准确率为100%，表示为支持向量机分类器。
结论：本综述的结果支持将机器学习模型用作牙源性囊肿和肿瘤的影像学诊断的临床辅助手段。只要他们接受强大的训练，就有一个巨大的数据集。然而,艰苦的过程，投资,必须考虑与完全依赖技术相关的某些道德问题。建议使用机器学习方法对诊断研究的结果进行标准化报告，以确保评估标准的一致性。便于不同研究之间的比较，并提高研究成果的透明度。
方法：对2015年1月至2022年12月期间发表的研究的著名数据库进行了全面的文献检索。包括使用机器学习模型在使用直视图（OPG）或锥形束计算机断层扫描图像（CBCT）诊断牙源性囊肿或肿瘤中的研究。QUADAS-2工具用于评估偏倚风险和适用性问题。对报告足够性能指标的研究进行了荟萃分析，分别用于OPG和CBCT。
结果：共纳入了16项定性合成研究，包括10,872例牙源性囊肿和肿瘤。机器学习通过OPG诊断囊肿和肿瘤的敏感性和特异性分别为0.83（95％CI0.81-0.85）和0.82（95％CI0.81-0.83）。使用CBCT的研究指出，灵敏度为0.88（95％CI0.87-0.88），特异性为0.88（95％CI0.87-0.89）。最高分类准确率为100%，表示为支持向量机分类器。
结论：本综述的结果支持将机器学习模型用作牙源性囊肿和肿瘤的影像学诊断的临床辅助手段。只要他们接受强大的训练，就有一个巨大的数据集。然而,艰苦的过程，投资,必须考虑与完全依赖技术相关的某些道德问题。建议使用机器学习方法对诊断研究的结果进行标准化报告，以确保评估标准的一致性。便于不同研究之间的比较，并提高研究成果的透明度。
