Training an Artificial Intelligence Model for Aortic Dissection Detection Using Non-Contrast Computed Tomography Images from Human Patients

Summary

This protocol describes training an artificial intelligence model to detect aortic dissection using non-contrast computed tomography images, enabling rapid and accessible screening in clinical settings.

Abstract

Aortic dissection (AD) is an extreme consequence of impaired vascular remodeling homeostasis and requires rapid, accurate identification in clinical practice. This protocol describes an artificial intelligence−based learning model for AD identification utilizing non-contrast computed tomography (CT). Chest CT and aortic CT angiography datasets were collected from AD and non-AD patients at a Grade A tertiary hospital. Vascular structures on each axial image were manually segmented and annotated using the open-source software LabelMe to establish a segmentation dataset for model development and evaluation. The dataset was partitioned into training, test, and validation sets at an 8:1:1 ratio for model training and validation. Following the development of a model with robust detection performance, an online processing platform was constructed to visualize and present the results effectively. This approach provides a powerful, intelligent tool for rapid, preliminary screening of AD and addresses the unmet clinical need for accessible early detection across diverse clinical environments.

Introduction

Aortic dissection (AD) is a life-threatening acute condition characterized by blood entering the medial layer of the aortic wall through a tear in the intimal lining, forming a dissecting and expanding false lumen¹. Without timely diagnosis and treatment, the mortality rate is extremely high, the incidence of death within 24 h (including death before reaching the hospital) was 93%². Contrast-enhanced computed tomography angiography (CTA) is the gold standard for diagnosing AD, as it can clearly visualize the true and false lumens, the location of the tear, and the extent of involvement³. However, ....

Protocol

All human-related data collection in this study was conducted in accordance with the ethical standards of the Declaration of Helsinki and was approved by the Ethics Committee of China-Japan Union Hospital of Jilin University (Approval Number: 2019103004). Informed consent was obtained in writing from all individual participants or their legal guardians prior to the collection of their relevant information. All personal information of the subjects was kept strictly confidential to protect their privacy, and no experimental operations were performed on the subjects during the data collection process.

1. Dataset Construction

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Results

This section presents the reproducible, implementation-verified results of the two-class object detection model for AD detection from NCCT images, strictly aligned with the validated training pipeline and COCO evaluation framework (Figure 1). All metrics are derived from the held-out test set using COCOeval, with no fabricated data or unvalidated indicators.

Quantitative Detection Performance

The model was evaluated on .......

Discussion

Clinicians, particularly emergency physicians, may experience diminished diagnostic performance when patients present with atypical symptoms or when high emergency department patient volume imposes time constraints. In contrast, an AI model trained to identify AD on NCCT can deliver consistent and stable performance even in asymptomatic patients, without being limited by reading time, thereby potentially improving the accuracy and efficiency of diagnosing AD and intramural hematoma⁹.

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Cascade R-CNN architecture	OpenMMLab (MMDetection)	configs/cascade_rcnn/cascade_ rcnn_hrnetv2p_w32_20e_coco.py	Detection architecture used in framework
Chest computed tomography images (non-contrast)	Self-constructed clinical dataset	NCCT axial image set	Clinical imaging data used for model development
COCO-format annotation files	Generated during protocol	JSON (COCO format)	Converted annotation files used for model training
COCO pre-trained weights	OpenMMLab MMDetection model zoo	cascade_rcnn_hrnetv2p_w32_20e_ coco_20200208-928455a4.pth	Used for model initialization
HRNetV2p-W32 architecture	OpenMMLab (MMDetection)	HRNetV2p-W32 backbone (implemented in MMDetection 2.28.2)	Backbone model used
ITK-SNAP	ITK-SNAP Development Team	3.8.0	Used for image format conversion and slice export
JSON annotation files	LabelMe output	Standard JSON format	Contain annotation coordinates and labels
LabelMe	MIT CSAIL	4.8.3	Used for manual image annotation
MMDetection	OpenMMLab	2.28.2	Object detection framework used for implementation
MMCV	OpenMMLab	1.7.2	Core library supporting MMDetection
NumPy	NumPy Developers	1.26.4	Numerical computation library
NVIDIA RTX 3080 Ti GPU	NVIDIA	RTX 3080 Ti	Hardware used for training
OpenCV	OpenCV	4.9.0	Image processing and visualization
pycocotools	PyPI / COCO API	2.0.6	COCO-format evaluation library
Python	Python Software Foundation	3.10.20	Programming environment
PyTorch	PyTorch	2.0.1+cu118	Deep learning framework
TorchVision	PyTorch	0.15.2+cu118	Vision utilities
Ubuntu Operating System	Canonical	22.04.1 LTS	Training environment OS