Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/72649, first published .
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

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

Zhechuan Jin   1, 2 * , PhD ;   Jiale Dong   1, 2 * , MD ;   Chengxiang Li   1, 2 * , PhD ;   Yi Jiang   3 * , MD ;   Jian Yang   1, 2 , PhD ;   Lei Xu   4 , MD ;   Ping Li   5 , MD ;   Zhun Xie   6 , PhD ;   Yulin Li   2 , PhD ;   Dongjin Wang   3 * , MD ;   Zhili Ji   1, 7 , MD

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

2 Beijing Institute of Heart, Lung and Blood Vessel Diseases, Beijing Anzhen Hospital, Capital Medical University, Beijing, China

3 Department of Cardiovascular Surgery, Nanjing Drum Tower Hospital, Nanjing Medical University, Nanjing, China

4 Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China

5 Department of Cardiovascular Surgery, Beijing Anzhen Hospital, Capital Medical University, Beijing, China

6 School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing, China

7 Department of Hepatobiliary and Pancreaticosplenic Surgery, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China

*these authors contributed equally

Corresponding Author:

  • Zhili Ji, MD
  • Department of General Surgery
  • Beijing Anzhen Hospital
  • Capital Medical University
  • No. 2 Anzhen Road, Chaoyang District
  • Beijing 100029
  • China
  • Phone: 86 010-64412431
  • Fax: 86 010-64428322
  • Email: anzhenjzl@mail.ccmu.edu.cn