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Deep Learning Model for Predicting the Outcome of Endovascular Abdominal Aortic Aneurysm Repair

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Abstract

The objective of this study is to establish a model for predicting the outcome of endovascular aneurysm repair for abdominal aortic aneurysms using deep learning algorithms. We performed a case series analysis of 493 patients with infra-renal abdominal aortic aneurysm who underwent elective endovascular aneurysm repair procedures between January 2016 and December 2019 in our single center. ITK-SNAP software was used to draw the abdominal aortic aneurysms region of interest. Images were preprocessed and deep learning model was built using MATrix LABoratory. Randomly divided, 80% of the patients were used as the training set, and 20% of the patients were enrolled in the test set. The area under the curve from receiver-operating characteristic curve was used to evaluate the predictive effect of the model. To further understand the prediction process of the deep learning model, visualization techniques were used to analyze the features learned by the model and the response of the convolutional layer to the different outcomes of endovascular aneurysm repair. The mean follow-up was 32.0 months, including 156 patients (31.6%) experiencing endovascular aneurysm repair-related severe adverse events. Number of patients in the training set was 394 (of which 269 had no severe adverse events and 125 had severe adverse events) and the number of patients in test set was 99 (of which 68 had no severe adverse events and 31 had severe adverse events). By training on 92,012–93,925 computed tomography angiography images (n = 315) and validation on 22,269–24,182 computed tomography angiography images (n = 79), the deep learning model finally achieved encouraging predictive performance in the test set (n = 99) with an area under the curve of 0.81 SD0.01, accuracy 0.82 SD0.02, and F1 score 0.87 SD0.02. The visualization techniques improved the model interpretability. The deep learning model could be an efficient adjunctive tool to predict outcomes after endovascular aneurysm repair.

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Acknowledgements

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: this work was supported by the National Natural Science Foundation of China (grant number 81870342).

Funding

The authors acknowledge that the present research was sponsored by the National Natural Science Foundation of China (grant number 81870342).

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Authors and Affiliations

  1. Department of Vascular SurgeryInstitute of Vascular SurgeryNational Clinical Research Center for Interventional Medicine, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China

    Yonggang Wang, Min Zhou, Yong Ding, Xu Li, Zhenyu Zhou, Zhenyu Shi & Weiguo Fu

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  1. Yonggang Wang
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  2. Min Zhou
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  3. Yong Ding
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  4. Xu Li
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Contributions

YW, ZS, and WF contributed to the conception and design; MZ, YD, XL, and ZZ performed the data collection and interpretation; YW and ZS performed ROI segmentation and images preprocessing; YW analyzed the datasets and wrote this article. ZS obtained the funding. All authors read and approved the final manuscript.

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Correspondence to Zhenyu Shi or Weiguo Fu.

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Wang, Y., Zhou, M., Ding, Y. et al. Deep Learning Model for Predicting the Outcome of Endovascular Abdominal Aortic Aneurysm Repair. Indian J Surg 85 (Suppl 1), 288–296 (2023). https://doi.org/10.1007/s12262-022-03506-0

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