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Background <p>Aortic dissection (AD) is a life-threatening condition. We developed an artificial intelligence (AI) algorithm capable of robust, accurate, and automated AD detection and sub-classification.</p> Materials and methods <p>Based on 2010–2023 data from Mannheim University Medical Centre, heterogeneous internal training cases with confirmed AD (<i>n</i> = 70) were manually segmented and, together with non-AD cases (<i>n</i> = 87), used for training of a convolutional neural network (CNN; U-Net architecture) configured using the nnU-Net framework. Internal test dataset was composed of 106 cases. The external test was performed on a public dataset: 100 AD cases from ImageTBAD, Guangdong Provincial People’s Hospital, China, and 38 non-AD cases from the AVT dataset (multiple sources). Model performance was evaluated by area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC), sensitivity, specificity, precision, and F1-score, and by investigating performance on different subsets of cases. Confidence intervals were determined using DeLong’s method and bootstrapping.</p> Results <p>The best-performing algorithm achieved an AUROC of 98.7% (95% CI: 96.1–100.0%) and an AUPRC of 98.9% (96.0–100.0%) on the internal test dataset, 97.0% (94.7–99.3%) and 99.06% (98.0–99.7%) on the external test datasets, respectively. In the internal test dataset, of 15 unsuspected AD cases, 14 (93.3%) were successfully detected by the algorithm. On the external test dataset, sensitivity, specificity, precision, and F1-score were 92.0%, 100.0%, 100.0%, and 95.8%, respectively.</p> Conclusion <p>The developed AI pipeline highlighted the capability of optimized CNNs to reliably detect AD across heterogeneous multicenter datasets. The resulting tool will be made publicly available for further scientific evaluation.</p> Relevance statement <p>Artificial Intelligence demonstrated promising potential to detect AD on heterogeneous thoracic CT imaging data.</p> Key Points <p><UnorderedList Mark="Bullet"> <ItemContent> <p>Early detection of aortic dissection (AD) is crucial for timely treatment.</p> </ItemContent> <ItemContent> <p>A modern convolutional neural network (CNN) achieved 93.5% sensitivity and 100.0% specificity for AD detection on multicenter, heterogeneous CT data.</p> </ItemContent> <ItemContent> <p>These results demonstrate the potential of streamlined, optimized CNNs for robust AD detection on CT, supporting fast clinical response.</p> </ItemContent> </UnorderedList></p> Graphical Abstract <p></p>

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Automated AI detection of thoracic aortic dissection on CT imaging

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月22日

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  • Tobias Norajitra,
  • Michael A. Baumgartner,
  • Lucas R. Cusumano,
  • Jesus G. Ulloa,
  • Christian S. Rizzo,
  • Florian Haag,
  • Alexander Hertel,
  • Nils A. Rathmann,
  • Steffen J. Diehl,
  • Stefan O. Schoenberg,
  • Klaus H. Maier-Hein,
  • Johann S. Rink

摘要

Background

Aortic dissection (AD) is a life-threatening condition. We developed an artificial intelligence (AI) algorithm capable of robust, accurate, and automated AD detection and sub-classification.

Materials and methods

Based on 2010–2023 data from Mannheim University Medical Centre, heterogeneous internal training cases with confirmed AD (n = 70) were manually segmented and, together with non-AD cases (n = 87), used for training of a convolutional neural network (CNN; U-Net architecture) configured using the nnU-Net framework. Internal test dataset was composed of 106 cases. The external test was performed on a public dataset: 100 AD cases from ImageTBAD, Guangdong Provincial People’s Hospital, China, and 38 non-AD cases from the AVT dataset (multiple sources). Model performance was evaluated by area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC), sensitivity, specificity, precision, and F1-score, and by investigating performance on different subsets of cases. Confidence intervals were determined using DeLong’s method and bootstrapping.

Results

The best-performing algorithm achieved an AUROC of 98.7% (95% CI: 96.1–100.0%) and an AUPRC of 98.9% (96.0–100.0%) on the internal test dataset, 97.0% (94.7–99.3%) and 99.06% (98.0–99.7%) on the external test datasets, respectively. In the internal test dataset, of 15 unsuspected AD cases, 14 (93.3%) were successfully detected by the algorithm. On the external test dataset, sensitivity, specificity, precision, and F1-score were 92.0%, 100.0%, 100.0%, and 95.8%, respectively.

Conclusion

The developed AI pipeline highlighted the capability of optimized CNNs to reliably detect AD across heterogeneous multicenter datasets. The resulting tool will be made publicly available for further scientific evaluation.

Relevance statement

Artificial Intelligence demonstrated promising potential to detect AD on heterogeneous thoracic CT imaging data.

Key Points

Early detection of aortic dissection (AD) is crucial for timely treatment.

A modern convolutional neural network (CNN) achieved 93.5% sensitivity and 100.0% specificity for AD detection on multicenter, heterogeneous CT data.

These results demonstrate the potential of streamlined, optimized CNNs for robust AD detection on CT, supporting fast clinical response.

Graphical Abstract