Abstract
Objectives
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.
Materials and Methods
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.
Results
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.
Conclusions
The accuracy of ultrasound soft markers in raising suspicion of rectosigmoid endometriosis
using Artificial Intelligence (AI) models showed similar results to the logistic model.
Keywords
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Article info
Publication history
Published online: April 13, 2021
Accepted:
April 11,
2021
Received in revised form:
April 6,
2021
Received:
March 6,
2021
Identification
Copyright
© 2021 Elsevier B.V. All rights reserved.
