ArticleDiagnostics (Basel, Switzerland)2024
Diagnostic Performance of a Deep Learning-Powered Application for Aortic Dissection Triage Prioritization and Classification.
Article in Diagnostics (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Time Is Aorta: Can Artificial Intelligence Improve Surgical Timelines in Acute Type A Aortic Dissection? A Comprehensive Review.Health science reports · 2026Article
- AI-driven diagnosis of acute aortic syndrome based on multi-modal information fusion.Scientific reports · 2026Article
- Performance of image-based deep learning models for aortic dissection segmentation and diagnosis: a systematic review and meta-analysis.Frontiers in cardiovascular medicine · 2026Review
- Automated AI detection of thoracic aortic dissection on CT imaging.European radiology experimental · 2025Article
- An innovative bimodal computed tomography data-driven deep learning model for predicting aortic dissection: a multi-center study.Quantitative imaging in medicine and surgery · 2025Article
- Artificial intelligence-driven diagnosis of acute thoracic aortic dissection: integrating imaging, biomarkers, and clinical workflows-a narrative review.Annals of translational medicine · 2025Review
- Multi-Stage Cascaded Deep Learning-Based Model for Acute Aortic Syndrome Detection: A Multisite Validation Study.Journal of clinical medicine · 2025Article
- Questions and Certainty in Diagnosis and Management of Acute Type B Aortic Dissection.Reviews in cardiovascular medicine · 2025Review
- Artificial intelligence in aortic CT angiography: current applications and future perspectives.Frontiers in cardiovascular medicine · 2025Review
- Enhancing Radiologist Efficiency with AI: A Multi-Reader Multi-Case Study on Aortic Dissection Detection and Prioritization.Diagnostics (Basel, Switzerland) · 2024Article
- Mapping artificial intelligence models in emergency medicine: A scoping review on artificial intelligence performance in emergency care and education.Turkish journal of emergency medicineReview
- Artificial intelligence in modern clinical practice (Review).Medicine internationalReview
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Authors and funding
12 authors.
Funding
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Abstract
This multicenter retrospective study evaluated the diagnostic performance of a deep learning (DL)-based application for detecting, classifying, and highlighting suspected aortic dissections (ADs) on chest and thoraco-abdominal CT angiography (CTA) scans. CTA scans from over 200 U.S. and European cities acquired on 52 scanner models from six manufacturers were retrospectively collected and processed by CINA-CHEST (AD) (Avicenna.AI, La Ciotat, France) device. The diagnostic performance of the device was compared with the ground truth established by the majority agreement of three U.S. board-certified radiologists. Furthermore, the DL algorithm's time to notification was evaluated to demonstrate clinical effectiveness. The study included 1303 CTAs (mean age 58.8 ± 16.4 years old, 46.7% male, 10.5% positive). The device demonstrated a sensitivity of 94.2% [95% CI: 88.8-97.5%] and a specificity of 97.3% [95% CI: 96.2-98.1%]. The application classified positive cases by the AD type with an accuracy of 99.5% [95% CI: 98.9-99.8%] for type A and 97.5 [95% CI: 96.4-98.3%] for type B. The application did not miss any type A cases. The device flagged 32 cases incorrectly, primarily due to acquisition artefacts and aortic pathologies mimicking AD. The mean time to process and notify of potential AD cases was 27.9 ± 8.7 s. This deep learning-based application demonstrated a strong performance in detecting and classifying aortic dissection cases, potentially enabling faster triage of these urgent cases in clinical settings.
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