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Related Experiment Video

Updated: Jun 12, 2025

Multimodality Diagnosis of Mesenteric Ischemia
05:07

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Published on: July 21, 2023

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A Deep Learning Model for Identifying the Risk of Mesenteric Malperfusion in Acute Aortic Dissection Using Initial

Zhechuan Jin1,2, Jiale Dong1,2, Chengxiang Li1,2

  • 1Department of General Surgery, Beijing Anzhen Hospital, Capital Medical University, Beijing, China.

Journal of Medical Internet Research
|June 10, 2025
PubMed
Summary

An integrated deep learning model accurately identifies patients with acute aortic dissection (AAD) at high risk for mesenteric malperfusion (MMP). This tool aids early risk assessment and timely treatment decisions for this complex condition.

Keywords:
acute aortic dissectioncomputed tomography angiographydeep learningmesenteric malperfusionmultimodality

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Area of Science:

  • Cardiovascular Imaging
  • Artificial Intelligence in Medicine
  • Gastrointestinal Surgery

Background:

  • Mesenteric malperfusion (MMP) is a rare but severe complication of acute aortic dissection (AAD).
  • Delayed diagnosis of MMP in AAD patients contributes to poor outcomes due to a lack of reliable risk assessment tools.

Purpose of the Study:

  • To develop and validate a deep learning model for identifying AAD patients at high risk of MMP.
  • The model integrates multimodal data for improved risk prediction.

Main Methods:

  • A multicenter retrospective study involving 525 AAD patients.
  • Developed three models: benchmark (laboratory data), MAM (CT angiography), and integrated (multimodal data).
  • Assessed model performance using AUC, accuracy, sensitivity, specificity, and Brier score; used CAM for visualization.

Main Results:

  • The integrated model showed superior performance in external validation (AUC 0.780) compared to benchmark (0.586) and MAM (0.732) models.
  • The integrated model achieved 76.0% accuracy, 66.7% sensitivity, and 78.3% specificity.
  • The integrated model's risk score was independently associated with in-hospital mortality risk in AAD patients.

Conclusions:

  • An integrated deep learning model using imaging and clinical data offers superior diagnostic accuracy for MMP in AAD patients compared to unimodal approaches.
  • This model can aid in early risk identification and timely therapeutic decision-making.
  • Further prospective validation is recommended to confirm clinical utility.