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This paper is in the following e-collection/theme issue:

Artificial Intelligence (4758) Clinical Information and Decision Making (3698) Cardiology (282) Tools, Programs and Algorithms (582) Decision Support for Health Professionals (2228) Advanced Data Analytics in eHealth (351) Machine Learning (3167)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/72649, first published .
Surgeon in scrubs and mask demonstrates spinal surgery procedure on model.

A Deep Learning Model for Identifying the Risk of Mesenteric Malperfusion in Acute Aortic Dissection Using Initial Diagnostic Data: Algorithm Development and Validation

A Deep Learning Model for Identifying the Risk of Mesenteric Malperfusion in Acute Aortic Dissection Using Initial Diagnostic Data: Algorithm Development and Validation

Authors of this article:

Zhechuan Jin1, 2 Author Orcid Image ;   Jiale Dong1, 2 Author Orcid Image ;   Chengxiang Li1, 2 Author Orcid Image ;   Yi Jiang3 Author Orcid Image ;   Jian Yang1, 2 Author Orcid Image ;   Lei Xu4 Author Orcid Image ;   Ping Li5 Author Orcid Image ;   Zhun Xie6 Author Orcid Image ;   Yulin Li2 Author Orcid Image ;   Dongjin Wang3 Author Orcid Image ;   Zhili Ji1, 7 Author Orcid Image
Article Authors Cited by (6) Tweetations Metrics

Journals

  1. Zhang Q, Jiang J. Predictive Value of Normalized Lactate Load for Patients with Acute Type A Aortic Dissection: Based on the MIMIC-IV Database. Annals of Thoracic and Cardiovascular Surgery 2025;31(1):n/a View
  2. Ayhan C, Mekhaeil M, Channawi R, Ozcan A, Akargul E, Deger A, Cayan I, Abdalla A, Chan C, Mahon R, Ayhan D, Wijns W, Sultan S, Soliman O. Applications of Artificial Intelligence as a Prognostic Tool in the Management of Acute Aortic Syndrome and Aneurysm: A Comprehensive Review. Journal of Clinical Medicine 2025;14(23):8420 View
  3. Hu M, Pan Y, Zhang J, Zhang L, Tang Q, Qin X, Zhang Z, Jiang W, Lu H, Qin Z, Guo S, Yang H. Retrospective cohort development and internal validation of a prediction model for 30-day mortality after TEVAR in type B aortic dissection. International Journal of Surgery 2026;112(1):1382 View
  4. Li Q. Commentary on “Multimodal deep learning to predict postoperative major adverse cardiac and cerebrovascular events after non-cardiac surgery”. International Journal of Surgery 2026;112(2):5408 View
  5. Mao Y, Liu Q, Fan H, Wang X. Machine learning approaches for risk prediction in aortic dissection: a systematic review and meta-analysis. Frontiers in Cardiovascular Medicine 2026;13 View
  6. Zhu Y, Feng J, Liu R, Ren W, Ma X, Zhang C, Zhang W, Zhong C, Gong L, Kong F. Development and validation of machine learning-based prediction models for adult bowel necrosis for patients presenting with acute abdominal pain at the emergency department: a multicenter retrospective cohort study. BMC Medical Informatics and Decision Making 2026;26(1) View