MedTrace Logo

Explainable Machine Learning for the Assessment of Donor Grafts in Liver Transplantation

NCT06535217 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 5636

Last updated 2024-08-02

No results posted yet for this study

Summary

Clinically, organ evaluation generally performed by the senior surgeons based on their experience and the visual and tactual inspection of the graft during procurement. However, it is proved that transplant surgeons intuition in the evaluation of donor risk and the estimation of steatosis is inconsistent and usually inaccurate. Besides, graft assessment is a dynamic process refer to amount of complex factors, which is considered to be an incredibly complicated relationship that is nonlinear in nature. Unfortunately, the classical statistic techniques in vogue such as multiple regression require the statistical assumption of independent and linear relationships between explanatory and outcome variables, and fail to analyse a large number of variables. We attempted to develop liver graft assessment models by predicting postoperative DGF using several ML techniques. Secondly, the best prediction model was selected by comparing the performance of different AI algorithms and logistic regression. Finally, we sought to explain the decision made by AI algorithms using a visualization algorithm based on the best prediction model, helping clinicians evaluate specific organ and whether to receive that may develop DGF postoperatively.

Conditions

  • Liver Transplant; Complications

Interventions

PROCEDURE

Liver transplantation

Liver transplantation

Sponsors & Collaborators

  • the China Liver Transplant Registry

    collaborator UNKNOWN

  • Third Affiliated Hospital, Sun Yat-Sen University

    lead OTHER

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2017-01-01
Primary Completion
2023-06-30
Completion
2024-06-30

Countries

  • China

Study Locations

More Related Trials

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 NCT06535217 on ClinicalTrials.gov