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
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| Main Authors: | , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866910559520161792 |
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| author | 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 |
| author_facet | 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 |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_03771 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | 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 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 Computer Vision and Pattern Recognition 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. |
| 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 |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2408.03771 |