Machine learning models for prognosis prediction in endodontic microsurgery
Machine learning models for prognosis prediction in endodontic microsurgery
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用于牙髓显微外科预后预测的机器学习模型
DOI：
10.1016/j.jdent.2022.103947
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发表时间：
2022-01-22
影响因子：
4.4
通讯作者：
Gu, Lisha
中科院分区：
文献类型：
--
作者：
Qu, Yang;Lin, Zhenzhe;Gu, Lisha
目的：建立并验证用于牙髓显微手术预后预测的机器学习模型，避免治疗失败，支持临床决策。方法：对178例患者的234颗牙进行研究。我们开发了梯度增强机（GBM）和随机森林（RF）模型。对于每个模型，随机抽取80%的数据作为训练集，剩下的20%作为测试集。在模型训练和检验中采用分层5重交叉验证方法。通过相关性分析和重要性排序进行特征选择。计算受试者工作特征（ROC）曲线的预测准确性、敏感性、特异性、阳性预测值（PPV）、阴性预测值（NPV）、F1评分、曲线下面积（AUC），评价预测效果。结果：牙型、病变大小、骨缺损类型、牙根充填密度、牙根充填长度、桩尖延伸、年龄、性别8个重要预测因素。对于GBM模型，预测精度为0.80，敏感性为0.92，特异性为0.71，PPV为0.71，NPV为0.92，F1为0.80，AUC为0.88。RF模型的准确率为0.80，灵敏度为0.85，特异性为0.76，PPV为0.73，NPV为0.87，F1为0.79，AUC为0.83。结论：所建立的模型由8个常见变量组成，具有预测牙髓显微手术预后的潜在能力。在我们的数据集上，GBM模型略微优于RF模型。临床意义：临床医生可以使用机器学习模型进行牙髓显微手术的术前分析。这些模型有望提高临床决策的效率，并有助于临床与患者的沟通。
Objectives: This study aimed to establish and validate machine learning models for prognosis prediction in endodontic microsurgery, avoiding treatment failure and supporting clinical decision-making. Methods: A total of 234 teeth from 178 patients were included in this study. We developed gradient boosting machine (GBM) and random forest (RF) models. For each model, 80% of the data were randomly selected for the training set and the remaining 20% were used as the test set. A stratified 5-fold cross-validation approach was used in model training and testing. Correlation analysis and importance ranking were conducted for feature selection. The predictive accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), F1 score, and the area under the curve (AUC) of receiver operating characteristic (ROC) curves were calculated to evaluate the predictive performance. Results: There were eight important predictors, including tooth type, lesion size, type of bone defect, root filling density, root filling length, apical extension of post, age, and sex. For the GBM model, the predictive accuracy was 0.80, with a sensitivity of 0.92, specificity of 0.71, PPV of 0.71, NPV of 0.92, F1 of 0.80, and AUC of 0.88. For the RF model, the accuracy was 0.80, with a sensitivity of 0.85, specificity of 0.76, PPV of 0.73, NPV of 0.87, F1 of 0.79, and AUC of 0.83. Conclusions: The trained models were developed by eight common variables, showing the potential ability to predict the prognosis of endodontic microsurgery. The GBM model outperformed the RF model slightly on our dataset. Clinical significance: Clinicians can use machine learning models for preoperative analysis in endodontic micro-surgery. The models are expected to improve the efficiency of clinical decision-making and assist in clinician-patient communication.