Explainable AI in Deep Learning-based Detection of Aortic Elongation on Chest X-ray Images

Aim 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 aim to assess qualitatively and quantitatively explainable methods in order to understand the decisions of a deep learning model for Aortic Elongation detection with Chest X-Ray (CXR) images. Methods and Results In this work, we evaluated the performance of deep learning models (DenseNet and EfficientNet) for aortic elongation detection based on transfer learning and fine-tunning techniques using CXR as input. DenseNet achieved higher accuracy (84.7% ± 0.7), precision (75.6% ± 1.3), sensitivity (88.7% ± 2.7), specificity (82.3% ± 1.6), F1-score (81.6% ± 1.0), and AUROC (93.1% ± 0.4) than EfficientNet. To gain insights into the decision-making process of the deep learning models, we employed Grad-CAM and LIME explainability methods. Through these techniques, we were able to successfully identify the expected location of aortic elongation in the x-ray images. Moreover, we used the pixel-flipping method to assess quantitatively the interpretations providing valuable insights into models behavior. Conclusion Our study presents a comprehensive strategy for analyzing CXR by integrating Aortic Elongation detection models with explainable methods. By incorporating explainable AI techniques, we enhanced 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.