Machine Learning in Predicting Tooth Loss: A Systematic Review and Risk of Bias Assessment.
Machine Learning in Predicting Tooth Loss: A Systematic Review and Risk of Bias Assessment.
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DOI：
10.3390/jpm12101682
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发表时间：
2022-10-09
影响因子：
--
通讯作者：
Sato S
中科院分区：
文献类型：
--
作者：
Hasuike A;Watanabe T;Wakuda S;Kogure K;Yanagiya R;Byrd KM;Sato S
预测牙齿脱落是21世纪世纪的一个持续的临床挑战。虽然这是牙科学的一个新兴领域，但采用机器学习的计算解决方案有望提高临床效果，包括牙齿脱落的椅旁诊断。我们的目的是评估使用机器学习的牙齿脱落预后预测模型的偏倚风险。为此，在两个电子数据库（MEDLINE via PubMed; Google Scholar）中检索文献，以查找报告预测模型准确度或曲线下面积（AUC）的研究。AUC测量整个受试者工作特征（ROC）曲线下方的整个二维面积。AUC提供了所有可能的分类阈值上的性能的综合度量。虽然开发和验证都包括在本综述中，但不评估增强模型（AdaBoosting，增强决策树，XGBoost，LightGBM，CatBoost）的准确性或验证的研究被排除在外。五项研究符合纳入标准并显示出高准确性;然而，模型显示出高偏差风险。重要的是，患者水平的评估与社会经济预测因素相结合的表现优于单独的临床预测因素。虽然目前存在局限性，但机器学习辅助的牙齿缺失模型可能会在未来与临床和患者元数据相结合，提高诊断准确性。
Predicting tooth loss is a persistent clinical challenge in the 21st century. While an emerging field in dentistry, computational solutions that employ machine learning are promising for enhancing clinical outcomes, including the chairside prognostication of tooth loss. We aimed to evaluate the risk of bias in prognostic prediction models of tooth loss that use machine learning. To do this, literature was searched in two electronic databases (MEDLINE via PubMed; Google Scholar) for studies that reported the accuracy or area under the curve (AUC) of prediction models. AUC measures the entire two-dimensional area underneath the entire receiver operating characteristic (ROC) curves. AUC provides an aggregate measure of performance across all possible classification thresholds. Although both development and validation were included in this review, studies that did not assess the accuracy or validation of boosting models (AdaBoosting, Gradient-boosting decision tree, XGBoost, LightGBM, CatBoost) were excluded. Five studies met criteria for inclusion and revealed high accuracy; however, models displayed a high risk of bias. Importantly, patient-level assessments combined with socioeconomic predictors performed better than clinical predictors alone. While there are current limitations, machine-learning-assisted models for tooth loss may enhance prognostication accuracy in combination with clinical and patient metadata in the future.
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影响因子：
2.9
作者：
Collins GS;Dhiman P;Andaur Navarro CL;Ma J;Hooft L;Reitsma JB;Logullo P;Beam AL;Peng L;Van Calster B;van Smeden M;Riley RD;Moons KG
通讯作者：
Moons KG
DOI：
10.3390/diagnostics12061422
发表时间：
2022-06-09
期刊：
Diagnostics (Basel, Switzerland)
影响因子：
--
作者：
通讯作者：
--
影响因子：
4.3
作者：
MCGUIRE, MK
通讯作者：
MCGUIRE, MK
影响因子：
5.4
作者：
Cooray, Upul;Watt, Richard G.;Aida, Jun
通讯作者：
Aida, Jun
影响因子：
4.3
作者：
HIRSCHFELD, L;WASSERMAN, B
通讯作者：
WASSERMAN, B