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Related Experiment Video

Updated: Jun 6, 2025

Time-Resolved, Dynamic Computed Tomography Angiography for Characterization of Aortic Endoleaks and Treatment Guidance via 2D-3D Fusion-Imaging

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Published on: December 9, 2021

Detection of Aortic Dissection and Intramural Hematoma in Non-Contrast Chest Computed Tomography Using a You Only

Yu-Seop Kim¹, Jae Guk Kim^2,3, Hyun Young Choi^2,3

¹Department of Convergence Software, Hallym University, Chuncheon 24252, Republic of Korea.

Journal of Clinical Medicine

|November 27, 2024

Summary

A deep learning model accurately differentiates aortic dissection (AD) and aortic intramural hematoma (IMH) from normal aorta (NA) using non-contrast CT scans. This AI tool aids diagnosis when contrast agents are contraindicated.

Keywords:

aortic dissection artificial intelligence contrast media deep learning machine learning tomography

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Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

Area of Science:

Medical Imaging
Artificial Intelligence in Radiology
Cardiovascular Diseases

Background:

Aortic dissection (AD) and aortic intramural hematoma (IMH) are life-threatening conditions with overlapping clinical presentations.
Contrast-enhanced computed tomography (CT) is typically essential for diagnosing AD and IMH.
Developing alternative diagnostic methods is crucial, especially when contrast administration is not feasible.

Purpose of the Study:

To develop and evaluate a deep learning algorithm for differentiating AD and IMH from normal aorta (NA) using non-contrast CT images.
To assess the efficacy of the YOLOv4 model in identifying these aortic pathologies without contrast agents.

Main Methods:

A retrospective analysis of 8881 non-contrast chest CT scans from 121 patients was performed.
A deep learning model, YOLO (You Only Look Once) v4, was trained and validated on CT images categorized as NA, AD, or IMH.
The dataset was divided into training, validation, and testing sets in an 8:1:1 ratio.

Main Results:

The YOLOv4 deep learning model achieved over 92% accuracy in simultaneously distinguishing between AD, IMH, and NA.
The model demonstrated robust performance in identifying these aortic conditions using non-contrast CT imaging alone.

Conclusions:

The developed deep learning model offers a promising non-contrast imaging approach for diagnosing AD and IMH.
This AI-powered tool can assist clinicians in identifying critical aortic pathologies when contrast-enhanced CT is challenging or contraindicated.