ArticleEuropean heart journal. Digital health2024
Explainable artificial intelligence in deep learning-based detection of aortic elongation on chest X-ray images.
Article in European heart journal. Digital health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Artificial intelligence (AI)-driven ensemble model for comprehensive chest X-ray abnormality detection and deployment.The Indian journal of medical research · 2026Article
- Clinical cognition in the age of cardiovascular AI.Frontiers in cardiovascular medicine · 2026Article
- New CT-based dural ectasia criteria using machine learning to diagnose Marfan and Loeys-Dietz syndromes.European heart journal. Imaging methods and practice · 2026Article
- Role of artificial intelligence in congenital heart disease.World journal of clinical pediatrics · 2025Review
- Identifying Acute Aortic Syndrome and Thoracic Aortic Aneurysm from Chest Radiography in the Emergency Department Using Convolutional Neural Network Models.Diagnostics (Basel, Switzerland) · 2024Article
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5 authors.
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Abstract
Aims: Aortic elongation can result from age-related changes, congenital factors, aneurysms, or conditions affecting blood vessel elasticity. It is associated with cardiovascular diseases and severe complications like aortic aneurysms and dissection. We assess qualitatively and quantitatively explainable methods to understand the decisions of a deep learning model for detecting aortic elongation using chest X-ray (CXR) images. Methods and results: In this study, we evaluated the performance of deep learning models (DenseNet and EfficientNet) for detecting aortic elongation using transfer learning and fine-tuning techniques with CXR images as input. EfficientNet achieved higher accuracy (86.7% Conclusion: Our study presents a comprehensive strategy for analysing CXR images by integrating aortic elongation detection models with explainable artificial intelligence techniques. By enhancing the interpretability and understanding of the models' decisions, this approach holds promise for aiding clinicians in timely and accurate diagnosis, potentially improving patient outcomes in clinical practice.
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