Methodological Explainability Evaluation of an Interpretable Deep Learning Model for Post-Hepatectomy Liver Failure Prediction Incorporating Counterfactual Explanations and Layerwise Relevance Propagation: A Prospective In Silico Trial

• Main Authors: Zhong, Xian, Salahuddin, Zohaib, Chen, Yi, Woodruff, Henry C, Long, Haiyi, Peng, Jianyun, Udawatte, Nuwan, Casale, Roberto, Mokhtari, Ayoub, Zhang, Xiaoer, Huang, Jiayao, Wu, Qingyu, Tan, Li, Chen, Lili, Li, Dongming, Xie, Xiaoyan, Lin, Manxia, Lambin, Philippe
• Format: Journal Article
• Language: English
• Published: 07-08-2024
• Subjects: Computer Science - Computer Vision and Pattern Recognition
• DOI: 10.48550/arxiv.2408.03771

Artificial intelligence (AI)-based decision support systems have demonstrated
value in predicting post-hepatectomy liver failure (PHLF) in hepatocellular
carcinoma (HCC). However, they often lack transparency, and the impact of model
explanations on clinicians' decisions has not been thoroughly evaluated.
Building on prior research, we developed a variational autoencoder-multilayer
perceptron (VAE-MLP) model for preoperative PHLF prediction. This model
integrated counterfactuals and layerwise relevance propagation (LRP) to provide
insights into its decision-making mechanism. Additionally, we proposed a
methodological framework for evaluating the explainability of AI systems. This
framework includes qualitative and quantitative assessments of explanations
against recognized biomarkers, usability evaluations, and an in silico clinical
trial. Our evaluations demonstrated that the model's explanation correlated
with established biomarkers and exhibited high usability at both the case and
system levels. Furthermore, results from the three-track in silico clinical
trial showed that clinicians' prediction accuracy and confidence increased when
AI explanations were provided.