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September 10, 2025

An innovative bimodal computed tomography data-driven deep learning model for predicting aortic dissection: a multi-center study

ZLZiqian LiShihezi University LCLing ChenChongqing University SZShengxuming ZhangZhejiang University of Science and Technology

Key Points

The bimodal deep learning model significantly improves diagnostic efficiency for aortic dissection.
Aorta detection achieved an average precision score of 99.14% for non-contrast CT and 99.34% for CTA.
The diagnosis model exhibited an AUC of 0.98 for non-contrast CT and 0.99 for CTA, indicating high accuracy.
This innovative approach may reduce diagnosis time, enhancing timely treatment for life-threatening conditions.

Abstract

Aortic dissection (AD) is a lethal emergency requiring prompt diagnosis. Current computed tomography angiography (CTA)-based diagnosis requires contrast agents, which expends time, whereas existing deep learning (DL) models only support single-modality inputs non-contrast computed tomography (CT) or CTA. In this study, we propose a bimodal DL framework to independently process both types, enabling dual-path detection and improving diagnostic efficiency. Patients who underwent non-contrast CT and CTA from February 2016 to September 2021 were retrospectively included from three institutions, including the First Affiliated Hospital, Zhejiang University School of Medicine (Center I), Zhejiang Hospital (Center II), and Yiwu Central Hospital (Center III). A two-stage DL model for predicting AD was developed. The first stage used an aorta detection network (AoDN) to localize the aorta in non-contrast CT or CTA images. Image patches that contained detected aorta were cut from CT images and combined to form an image patch sequence, which was inputted to an aortic dissection diagnosis network (ADDiN) to diagnose AD in the second stage. The following performances were assessed: aorta detection and diagnosis using average precision at the intersection over union threshold 0.5 (AP@0.5) and area under the receiver operating characteristic curve (AUC). The first cohort, comprising 102 patients (53±15 years, 80 men) from two institutions, was used for the AoDN, whereas the second cohort, consisting of 861 cases (55±15 years, 623 men) from three institutions, was used for the ADDiN. For the AD task, the AoDN achieved AP@0.5 99.14% on the non-contrast CT test set and 99.34% on the CTA test set, respectively. For the AD diagnosis task, the ADDiN obtained an AUCs of 0.98 on the non-contrast CT test set and 0.99 on the CTA test set. The proposed bimodal CT data-driven DL model accurately diagnoses AD, facilitating prompt hospital diagnosis and treatment of AD.

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