Region-specific Risk Quantification for Interpretable Prognosis of COVID-19
This addresses the need for accurate and interpretable AI tools to assist clinicians in making informed decisions about COVID-19 diagnosis and prognosis, though it is incremental in combining existing techniques for a specific domain.
The paper tackled the problem of improving interpretability and trust in COVID-19 prognosis using chest X-ray images by developing an interpretable deep survival prediction model, achieving superior C-indexes of 0.764 and 0.727 and time-dependent AUCs of 0.799 and 0.691.
The COVID-19 pandemic has strained global public health, necessitating accurate diagnosis and intervention to control disease spread and reduce mortality rates. This paper introduces an interpretable deep survival prediction model designed specifically for improved understanding and trust in COVID-19 prognosis using chest X-ray (CXR) images. By integrating a large-scale pretrained image encoder, Risk-specific Grad-CAM, and anatomical region detection techniques, our approach produces regional interpretable outcomes that effectively capture essential disease features while focusing on rare but critical abnormal regions. Our model's predictive results provide enhanced clarity and transparency through risk area localization, enabling clinicians to make informed decisions regarding COVID-19 diagnosis with better understanding of prognostic insights. We evaluate the proposed method on a multi-center survival dataset and demonstrate its effectiveness via quantitative and qualitative assessments, achieving superior C-indexes (0.764 and 0.727) and time-dependent AUCs (0.799 and 0.691). These results suggest that our explainable deep survival prediction model surpasses traditional survival analysis methods in risk prediction, improving interpretability for clinical decision making and enhancing AI system trustworthiness.