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Automated Stanford classification of aortic dissection using a 2-step hierarchical neural network at computed tomography angiography

  • Imaging Informatics and Artificial Intelligence
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objectives

This study aimed to evaluate the feasibility of automatic Stanford classification of classic aortic dissection (AD) using a 2-step hierarchical neural network.

Methods

Between 2015 and 2019, 130 arterial phase series (57 type A, 43 type B, and 30 negative cases) in aortic CTA were collected for the training and validation. A 2-step hierarchical model was built including the first step detecting AD and the second step predicting the probability (0–1) of Stanford types. The model’s performance was evaluated with an off-line prospective test in 2020. The sensitivity and specificity for Stanford type A, type B, and no AD (Sens A, B, N and Spec A, B, N, respectively) and Cohen’s kappa were reported.

Results

Of 298 cases (22 with type A, 29 with type B, and 247 without AD) in the off-line prospective test, the Sens A, Sens B, and Sens N were 95.45% (95% confidence interval [CI], 77.16–99.88%), 79.31% (95% CI, 60.28–92.01%), and 93.52% (95% CI, 89.69–96.25%), respectively. The Spec A, Spec B, and Spec N were 98.55% (95% CI, 96.33–99.60%), 94.05% (95% CI, 90.52–96.56%), and 94.12% (95% CI, 83.76–98.77%), respectively. The classification rate achieved 92.28% (95% CI, 88.64–95.04%). The Cohen’s kappa was 0.766 (95% CI, 0.68–0.85; p < 0.001).

Conclusions

Stanford classification of classic AD can be determined by a 2-step hierarchical neural network with high sensitivity and specificity of type A and high specificity in type B and no AD.

Key Points

• The Stanford classification for aortic dissection is widely adopted and divides it into Stanford type A and type B based on the ascending thoracic aorta dissected or not.

• The 2-step hierarchical neural network for Stanford classification of classic aortic dissection achieved high sensitivity (95.45%) and specificity (98.55%) of type A and high specificity in type B and no aortic dissection (94.05% and 94.12%, respectively) in 298 test cases.

• The 2-step hierarchical neural network demonstrated moderate agreement (Cohen’s kappa: 0.766, p < 0.001) with cardiovascular radiologists in detection and Stanford classification of classic aortic dissection in 298 test cases.

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Abbreviations

2D:

Two-dimensional

3D:

Three-dimensional

AD:

Aortic dissection

CI:

Confidence interval

CNN:

Convolutional neural network

TEVAR:

Thoracic endovascular aortic repair

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Acknowledgements

The authors would like to thank Convergence CT for assistance with English editing.

Funding

This study has received funding from the Ministry of Science and Technology of Taiwan (Grants 109–2634-F-006–023).

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Authors and Affiliations

  1. Department of Medical Imaging, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, 704, No. 138 Sheng-Li Road, Tainan, Taiwan

    Li-Ting Huang, Yi-Shan Tsai, Han-Sheng Huang & Chien-Kuo Wang

  2. Clinical Innovation and Research Center, National Cheng Kung University Hospital, Tainan, Taiwan

    Yi-Shan Tsai

  3. Graduate Degree Program of College of Electrical and Computer Engineering, National Yang Ming Chiao Tung University, Hsinchu, Taiwan

    Cheng-Fu Liou & Tsung-Han Lee

  4. College of Artificial Intelligence, National Yang Ming Chiao Tung University, Hsinchu, Taiwan

    Po-Tsun Paul Kuo

  5. AI Research Center, Advantech Co., Ltd, Taipei, Taiwan

    Po-Tsun Paul Kuo

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  1. Li-Ting Huang
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  2. Yi-Shan Tsai
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  3. Cheng-Fu Liou
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  4. Tsung-Han Lee
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  7. Chien-Kuo Wang
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Corresponding author

Correspondence to Chien-Kuo Wang.

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Guarantor

The scientific guarantor of this publication is Chien-Kuo Wang.

Conflict of interest

The authors of this manuscript declare relationships with the following companies:

Po-Tsun, Paul, Kuo: an employee in the AI Research Centre, Advantech Company.

Authors who are not employees of or consultants for Advantech Company had control of the inclusion of any data and information that might present a conflict of interest for the author who is an employee of that industry.

The other authors have no conflict of interest to disclose.

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No complex statistical methods were necessary for this paper.

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Written informed consent was waived by the Institutional Review Board.

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Institutional Review Board approval was obtained.

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• retrospective

• diagnostic or prognostic study

• performed at one institution

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Huang, LT., Tsai, YS., Liou, CF. et al. Automated Stanford classification of aortic dissection using a 2-step hierarchical neural network at computed tomography angiography. Eur Radiol 32, 2277–2285 (2022). https://doi.org/10.1007/s00330-021-08370-2

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  • DOI: https://doi.org/10.1007/s00330-021-08370-2

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