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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

arXiv 2408.03771 ↗First posted 7 Aug 2024cs.CVcited by 0

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Abstract (arXiv)

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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17 of 63 references.

References · 17

  1. 2020Concept Bottleneck Modelscited by 1,032
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  3. 2016European Union regulations on algorithmic decision-making and a "right to explanation"cited by 344
  4. 2020Counterfactual Explanations and Algorithmic Recourses for Machine Learning: A Reviewcited by 174
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  6. 2017Anatomically Constrained Neural Networks (ACNN): Application to Cardiac Image Enhancement and Segmentationcited by 109
  7. 2021What I Cannot Predict, I Do Not Understand: A Human-Centered Evaluation Framework for Explainability Methodscited by 98
  8. 2021Transparency of Deep Neural Networks for Medical Image Analysis: A Review of Interpretability Methodscited by 83
  9. 2019Explanation by Progressive Exaggerationcited by 76
  10. 2019Measuring the Quality of Explanations: The System Causability Scale (SCS). Comparing Human and Machine Explanationscited by 64
  11. 2021DISSECT: Disentangled Simultaneous Explanations via Concept Traversalscited by 37
  12. 2021Using StyleGAN for Visual Interpretability of Deep Learning Models on Medical Imagescited by 35
5 more
  1. 2021Explainable Medical Imaging AI Needs Human-Centered Design: Guidelines and Evidence from a Systematic Reviewcited by 30
  2. 2016Interpretable Deep Neural Networks for Single-Trial EEG Classificationcited by 24
  3. 2019The impact of patient clinical information on automated skin cancer detectioncited by 21
  4. 2023Dermatologist-like explainable AI enhances trust and confidence in diagnosing melanomacited by 18
  5. 2020Interpretable Deep Models for Cardiac Resynchronisation Therapy Response Predictioncited by 17

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