Next MICCAI Industrial Talk: October 14, 2025
Wednesday 8th October 2025
Don't miss the next exciting talk in our MICCAI Industrial Talk Series:
Title: AI-based Rapid and Accurate Warning of Acute Aortic Syndrome from Non-contrast CT
Presenter: Yan-Jie Zhou, PhD, Staff Algorithm Expert, Alibaba DAMO Academy
Date: Tuesday, October 14, 2025
Time: 7:00 - 8:00 AM (PDT) / 10:00 –11:00 AM (EDT) / 4:00 - 5:00 PM (CEST)
Abstract:
The accurate and timely diagnosis of acute aortic syndromes (AAS) in patients presenting with acute chest pain remains a clinical challenge. Aortic CT angiography (CTA) is the imaging protocol of choice in patients with suspected AAS. However, due to economic and workflow constraints in China, the majority of suspected patients initially undergo non-contrast CT as the initial imaging testing, and CTA is reserved for those at higher risk. Although non-contrast CT can reveal specific signs indicative of AAS, its diagnostic efficacy when used alone has not been well characterized.
We present an artificial intelligence-based warning system, iAorta, using non-contrast CT for AAS identification, which demonstrates remarkably high accuracy and provides clinicians with interpretable warnings. iAorta was evaluated through a comprehensive step-wise retrospective and prospective study. For the prospective pilot deployment that we conducted, iAorta correctly identified 21 out of 22 patients with AAS among 15,584 consecutive patients presenting with acute chest pain and under non-contrast CT protocol in the emergency department (ED) and enabled the average diagnostic time of these 21 AAS positive patients to be 102.1 (75-133) mins.
In summary, the iAorta can help avoid delayed or missed diagnosis of AAS in settings where non-contrast CT remains the unavoidable the initial or only imaging test in resource-constrained regions and in patients who cannot or did not receive intravenous contrast.
Bio:
Dr. Yan-Jie Zhou is aStaff Algorithm Expert in the Alibaba DAMO Academy. He is a technical leader in chest pain triple‑rule‑out research at DAMO Academy. He obtained his Ph.D. from the Institute of Automation, Chinese Academy of Sciences. He has published papers and abstracts on Nature Medicine, IEEE Transactions on Medical Imaging, CVPR, MICCAI, ICRA, RSNA, etc. He has received multiple awards, including the Excellent Technology Project Award at Alibaba DAMO Academy, the President's Award of Excellence at Chinese Academy of Sciences. His research mainly focuses on robot-assisted intervention and medical image analysis, especially on disease screening and diagnosis in CT images using deep learning.