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

• URL: http://arxiv.org/abs/2408.03771v1
• Date: Wed, 7 Aug 2024 13:47:32 GMT
• Title: 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
• Authors: Xian Zhong, Zohaib Salahuddin, Yi Chen, Henry C Woodruff, Haiyi Long, Jianyun Peng, Nuwan Udawatte, Roberto Casale, Ayoub Mokhtari, Xiaoer Zhang, Jiayao Huang, Qingyu Wu, Li Tan, Lili Chen, Dongming Li, Xiaoyan Xie, Manxia Lin, Philippe Lambin,
• Abstract summary: 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. Results from the three-track in silico clinical trial showed that clinicians' prediction accuracy and confidence increased when AI explanations were provided.
• Score: 13.171582596404313
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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