Centro Integrato di Procreazione Medicalmente Assistita (PMA) e Diagnostica Ostetrico-Ginecologica, Policlinico Universitario Duilio Casula, Monserrato, Cagliari, Italy; University of Cagliari, Cagliari, Italy.
Department of Obstetrics, Gynecology, and Reproduction, Hospital Universitari Dexeus, Spain.
Eur J Obstet Gynecol Reprod Biol. 2021 Jun;261:29-33. doi: 10.1016/j.ejogrb.2021.04.012. Epub 2021 Apr 14.
The aim of this study was to compare the accuracy of seven classical Machine Learning (ML) models trained with ultrasound (US) soft markers to raise suspicion of endometriotic bowel involvement.
Input data to the models was retrieved from a database of a previously published study on bowel endometriosis performed on 333 patients. The following models have been tested: k-nearest neighbors algorithm (k-NN), Naive Bayes, Neural Networks (NNET-neuralnet), Support Vector Machine (SVM), Decision Tree, Random Forest, and Logistic Regression. The data driven strategy has been to split randomly the complete dataset in two different datasets. The training dataset and the test dataset with a 67 % and 33 % of the original cases respectively. All models were trained on the training dataset and the predictions have been evaluated using the test dataset. The best model was chosen based on the accuracy demonstrated on the test dataset. The information used in all the models were: age; presence of US signs of uterine adenomyosis; presence of an endometrioma; adhesions of the ovary to the uterus; presence of "kissing ovaries"; absence of sliding sign. All models have been trained using CARET package in R with ten repeated 10-fold cross-validation. Accuracy, Sensitivity, Specificity, positive (PPV) and negative (NPV) predictive value were calculated using a 50 % threshold. Presence of intestinal involvement was defined in all cases in the test dataset with an estimated probability greater than 0.5.
In our previous study from where the inputs were retrieved, 106 women had a final expert US diagnosis of rectosigmoid endometriosis. In term of diagnostic accuracy the best model was the Neural Net (Accuracy, 0.73; sensitivity, 0.72; specificity 0.73; PPV 0.52; and NPV 0.86) but without significant difference with the others.
The accuracy of ultrasound soft markers in raising suspicion of rectosigmoid endometriosis using Artificial Intelligence (AI) models showed similar results to the logistic model.
本研究旨在比较 7 种基于超声（US）软标记物训练的经典机器学习（ML）模型的准确性，以提高对子宫内膜异位症肠受累的怀疑。
模型的输入数据取自先前发表的 333 例肠子宫内膜异位症研究的数据库。已测试以下模型：k-最近邻算法（k-NN）、朴素贝叶斯、神经网络（NNET-neuralnet）、支持向量机（SVM）、决策树、随机森林和逻辑回归。数据驱动策略是将完整数据集随机分割为两个不同的数据集。训练数据集和测试数据集分别占原始病例的 67%和 33%。所有模型均在训练数据集上进行训练，并使用测试数据集评估预测。根据测试数据集上的准确性选择最佳模型。所有模型使用 R 中的 CARET 包进行训练，重复进行了 10 次 10 倍交叉验证。使用 50%的阈值计算准确性、敏感性、特异性、阳性（PPV）和阴性（NPV）预测值。在测试数据集中，所有病例均根据估计概率大于 0.5 来定义肠内受累情况。
在我们之前的研究中，从输入数据中检索到的研究中，106 名女性的最终专家超声诊断为直肠乙状结肠子宫内膜异位症。在诊断准确性方面，最佳模型是神经网络（准确性为 0.73；敏感性为 0.72；特异性为 0.73；PPV 为 0.52；NPV 为 0.86），但与其他模型没有显著差异。
使用人工智能（AI）模型提高对直肠乙状结肠子宫内膜异位症怀疑的超声软标记物的准确性与逻辑模型相似。
