The use of artificial intelligence in the diagnosis of aortic aneurysms

Objective. The purpose of this study is to review the application of deep neural network learning methods in the diagnosis and treatment of aortic aneurysm (AA), based on imaging methods, and special attention will also be paid to screening, diagnosis, lesion segmentation, surgical care, and outcome prediction. Methods. A review was conducted of scientific publications that used deep learning models, such as convolutional neural networks (SNN), in various aspects of AA diagnosis and treatment. Results. Deep learning models have demonstrated significant progress in the treatment and diagnosis of aortic aneurysms. For screening and diagnosis, models such as ResNet provide high accuracy in detecting aneurysms on contrast-free CT scans. Methods such as U-Net allow accurate measurement of aneurysm size and volume, which is important for planning the volume of surgery. Deep learning also helps in surgical procedures by accurately predicting stent position and postoperative complications. In addition, the models are able to accurately predict the progression of the aneurysm and the prognosis for the patient. Conclusions. Deep learning technologies demonstrate significant potential in improving the diagnosis, treatment, and control of aortic aneurysms. These advances can lead to a more accurate and personalized approach to patients, improving treatment outcomes for patients with this pathology. 

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