Artificial intelligence (AI) in the detection of rectosigmoid deep endometriosis

Stefano Guerriero, S. Guerriero, MariaAngela Pascual, M. Pascual, Ajossa S, S. Ajossa, Silvia Ajossa, Manuela Neri, Musa E, Eleonora Musa, Graupera B, Betlem Graupera, B. Graupera, Ignacio Rodriguez, Ignacio Rodríguez, Juan Luis Alcázar, Juan Luis Alcazar, Alcazar JL
European journal of obstetrics, gynecology, and reproductive biology · 2021 · vol. 261 , pp. 29–33 · doi:10.1016/j.ejogrb.2021.04.012 · PMID:33873085 · W3154517988
Diagnostic accuracy OA: closed CC0 ⤵ 27 in-corpus citations
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This study investigated the efficacy of an artificial intelligence model in detecting deep rectosigmoid endometriosis from medical images.

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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.

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Condition tags

endometriosis

MeSH descriptors

Endometriosis Endometriosis Endometriosis Artificial Intelligence Artificial Intelligence Bayes Theorem Bayes Theorem Female Female Humans Humans Sensitivity and Specificity Sensitivity and Specificity Ultrasonography Ultrasonography

Citation neighborhood (2-hop)

Papers in the corpus that this work cites (lower rings, blue) and that cite this one (upper rings, green). Dot size scales with the paper's in-corpus citation count — bigger dot = more influential within the endo/adeno field. Click a dot to open that paper. Outer rings show 2-hop neighbours — papers reached through the immediate citers/citees. [ collapse to 1-hop ]

References (55)

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