Abstract：
BACKGROUND: Oral Potentially Malignant Disorders (OPMDs) refer to a heterogenous group of clinical presentations with heightened rate of malignant transformation. Identification of risk levels in OPMDs is crucial to determine the need for active intervention in high-risk patients and routine follow-up in low-risk ones. Machine learning models has shown tremendous potential in several areas of dentistry that strongly suggest its application to estimate rate of malignant transformation of precancerous lesions.
METHODS: A comprehensive literature search was performed on Pubmed/MEDLINE, Web of Science, Scopus, Embase, Cochrane Library database to identify articles including machine learning models and algorithms to predict malignant transformation in OPMDs. Relevant bibliographic data, study characteristics, and outcomes were extracted for eligible studies. Quality of the included studies was assessed through the IJMEDI checklist.
RESULTS: Fifteen articles were found suitable for the review as per the PECOS criteria. Amongst all studies, highest sensitivity (100%) was recorded for U-net architecture, Peaks Random forest model, and Partial least squares discriminant analysis (PLSDA). Highest specificity (100%) was noted for PLSDA. Range of overall accuracy in risk prediction was between 95.4% and 74%.
CONCLUSIONS: Machine learning proved to be a viable tool in risk prediction, demonstrating heightened sensitivity, automation, and improved accuracy for predicting transformation of OPMDs. It presents an effective approach for incorporating multiple variables to monitor the progression of OPMDs and predict their malignant potential. However, its sensitivity to dataset characteristics necessitates the optimization of input parameters to maximize the efficiency of the classifiers.
METHODS: A comprehensive literature search was performed on Pubmed/MEDLINE, Web of Science, Scopus, Embase, Cochrane Library database to identify articles including machine learning models and algorithms to predict malignant transformation in OPMDs. Relevant bibliographic data, study characteristics, and outcomes were extracted for eligible studies. Quality of the included studies was assessed through the IJMEDI checklist.
RESULTS: Fifteen articles were found suitable for the review as per the PECOS criteria. Amongst all studies, highest sensitivity (100%) was recorded for U-net architecture, Peaks Random forest model, and Partial least squares discriminant analysis (PLSDA). Highest specificity (100%) was noted for PLSDA. Range of overall accuracy in risk prediction was between 95.4% and 74%.
CONCLUSIONS: Machine learning proved to be a viable tool in risk prediction, demonstrating heightened sensitivity, automation, and improved accuracy for predicting transformation of OPMDs. It presents an effective approach for incorporating multiple variables to monitor the progression of OPMDs and predict their malignant potential. However, its sensitivity to dataset characteristics necessitates the optimization of input parameters to maximize the efficiency of the classifiers.
摘要：
背景：口腔潜在恶性疾病（OPMD）是指一组异质性的临床表现，恶性转化率升高。OPMDs的风险水平的识别对于确定对高风险患者进行积极干预和对低风险患者进行常规随访的必要性至关重要。机器学习模型在牙科的几个领域显示出巨大的潜力，强烈表明其应用于估计癌前病变的恶性转化率。
方法：对Pubmed/MEDLINE进行了全面的文献检索，WebofScience,Scopus,Embase,CochraneLibrary数据库用于识别文章，包括机器学习模型和算法，以预测OPMD中的恶性转化。相关书目数据，研究特点，并为符合条件的研究提取结局.通过IJMEDI检查表评估纳入研究的质量。
结果：根据PECOS标准，有15篇文章适合本综述。在所有研究中，U网架构的灵敏度最高(100%)，峰值随机森林模型,和偏最小二乘判别分析(PLSDA)。对于PLSDA注意到最高特异性(100%)。风险预测的总体准确度范围在95.4%至74%之间。
结论：机器学习被证明是风险预测的可行工具，表现出更高的灵敏度，自动化,提高了预测OPMD转化的准确性。它提出了一种有效的方法来结合多个变量来监测OPMD的进展并预测其恶性潜力。然而,它对数据集特征的敏感性需要优化输入参数以最大化分类器的效率。
方法：对Pubmed/MEDLINE进行了全面的文献检索，WebofScience,Scopus,Embase,CochraneLibrary数据库用于识别文章，包括机器学习模型和算法，以预测OPMD中的恶性转化。相关书目数据，研究特点，并为符合条件的研究提取结局.通过IJMEDI检查表评估纳入研究的质量。
结果：根据PECOS标准，有15篇文章适合本综述。在所有研究中，U网架构的灵敏度最高(100%)，峰值随机森林模型,和偏最小二乘判别分析(PLSDA)。对于PLSDA注意到最高特异性(100%)。风险预测的总体准确度范围在95.4%至74%之间。
结论：机器学习被证明是风险预测的可行工具，表现出更高的灵敏度，自动化,提高了预测OPMD转化的准确性。它提出了一种有效的方法来结合多个变量来监测OPMD的进展并预测其恶性潜力。然而,它对数据集特征的敏感性需要优化输入参数以最大化分类器的效率。
