Key result
Artificial neural networks demonstrated high predictive accuracy for detecting endoleak type I (94%), type II (82-90%), and type III (96%) post-EVAR, with an overall model accuracy >86%.
Why the study?
To evaluate the plausibility and applicability of prediction, pattern recognition, and modelling of complications post-endovascular aneurysm repair (EVAR) using artificial intelligence for more accurate clinical surveillance.
Does artificial intelligence improve the prediction of complications post-endovascular aneurysm repair compared to traditional statistical analysis?
Observational
Does artificial intelligence improve the prediction of complications post-endovascular aneurysm repair compared to traditional statistical analysis?
Artificial intelligence models demonstrate high accuracy (>86%) in predicting adverse outcomes such as endoleaks post-endovascular aneurysm repair, potentially improving surveillance stratification.
OBJECTIVES: The study evaluates the plausibility and applicability of prediction, pattern recognition and modelling of complications post-endovascular aneurysm repair (EVAR) by artificial intelligence for more accurate surveillance in practice. METHODS: = 26 preoperative attributes (factors) on endpoint of endoleak (types I-VI), occlusion, migration and mortality over a 13-year period was conducted. In addition to the traditional statistical analysis, data was subjected to machine learning algorithm through artificial neural network. The predictive accuracy (specificity and -1 sensitivity) on each endpoint is presented with percentage and receiver operative curve. The pattern recognition and model classification were conducted using discriminate analysis, decision tree, logistic regression, naive Bayes and support vector machines, and the best fit model was deployed for pattern recognition and modelling. RESULTS: The accuracy of the training, validation and predictive ability of artificial neural network in detection of endoleak type I was 95, 96 and 94%, type II (94, 83, 90 and 82%) and type III was 96, 94 and 96%, respectively. Endpoints are associated with increase in weights through predictive modeling that were not detected through statistical analytics. The overall accuracy of the model was >86%. CONCLUSION: The study highlights the applicability, accuracy and reliability of artificial intelligence in the detection of adverse outcomes post-EVAR for an accurate surveillance stratification.
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Kordzadeh et al. (2020) conducted an observational in Complications post-endovascular aneurysm repair (EVAR). Artificial intelligence (machine learning algorithms) vs. Traditional statistical analysis was evaluated on Predictive accuracy for endoleak, occlusion, migration and mortality. Artificial neural networks demonstrated high predictive accuracy for detecting endoleak type I (94%), type II (82-90%), and type III (96%) post-EVAR, with an overall model accuracy >86%.
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