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Usability and Clinical Effectiveness of an Interpretable Deep Learning Framework for Post-Hepatectomy Liver Failure Prediction

NCT06031818 · Status: RECRUITING · Type: OBSERVATIONAL · Enrollment: 80

Last updated 2024-02-06

No results posted yet for this study

Summary

The goal of this in-silico clinical trial is to learn about the usability and clinical effectiveness of an interpretable deep learning framework (VAE-MLP) using counterfactual explanations and layerwise relevance propagation for prediction of post-hepatectomy liver failure (PHLF) in patients with hepatocellular carcinoma (HCC). The main questions it aims to answer are:

* To investigate the usability of the VAE-MLP framework for explanation of the deep learning model.
* To investigate the clinical effectiveness of VAE-MLP framework for prediction of post-hepatectomy liver failure in patients with hepatocellular carcinoma.

In the usability trial the clinicians and radiologists will be shown the counterfactual explanations and layerwise relevance propagation (LRP) plots to evaluate the usability of the framework.

In the clinical trial the clinicians and radiologists will make the prediction under two different conditions: with model explanation and without model explanation with a washout period of at least 14 days to evaluate the clinical effectiveness of the explanation framework.

Conditions

Interventions

OTHER

The explanation of deep learning framework (VAE-MLP) , including counterfactual explanations and layerwise relevance propagation

The radiologist and clinicians will be provided the model prediction results with the explanation of the model and they will fill in a questionnaire to evaluate the usability of the interpretable framework.

OTHER

The model prediction

The radiologist and clinicians will be provided the model prediction results without the explanation of the model and they will be asked to give their own prediction.

OTHER

The model prediction and the explanation of deep learning framework (VAE-MLP) , including counterfactual explanations and layerwise relevance propagation

The radiologist and clinicians will be provided the model prediction results with the explanation of the model and they will be asked to give their own prediction.

Sponsors & Collaborators

  • First Affiliated Hospital, Sun Yat-Sen University

    collaborator OTHER

  • Maastricht University

    lead OTHER

Principal Investigators

  • Philippe Lambin · Maastricht University

Eligibility

Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2023-12-10
Primary Completion
2024-02-28
Completion
2024-03-15

Countries

  • China

Study Locations

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Entities

Diseases

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06031818 on ClinicalTrials.gov