"LiverColor": Machine Learning in Liver Photographs

Sponsor

Hospital Vall d'Hebron (Other)

Overall Status

Recruiting

CT.gov ID

NCT05202886

Collaborator

(none)

246

Enrollment

Location

Arm

Anticipated Duration (Months)

3.7

Patients Per Site Per Month

Study Details

Study Description

Brief Summary

The main goal of this project is to create a machine learning model in order to quantify liver steatosis in liver donor faster, more objective and reliable than histological analysis and surgeons point-of-view.

Condition or Disease	Intervention/Treatment	Phase
Brain Death Liver Steatosis	Diagnostic Test: Liver from deceased donors	N/A

Detailed Description

Surgeons (junior and senior operators) from the HBP & Transplantation Unit took the pictures. They were taken after the laparotomy and before any type of surgical procedure. For each deceased donor case, a total of 5 pictures were taken: one for the left lobe and another for the right one before undergoing a surgical biopsy, two more (one for the left and one for the right lobe) after the histological analysis, near to the site of the surgical biopsy, and finally, one picture after liver perfusion.

Study Design

Study Type:

Interventional

Anticipated Enrollment :

246 participants

Allocation:

N/A

Intervention Model:

Single Group Assignment

Intervention Model Description:

Liver from deceased donorsLiver from deceased donors

Masking:

None (Open Label)

Primary Purpose:

Diagnostic

Official Title:

"LiverColor": AN ALGORITHM QUANTIFICATION OF LIVER GRAFT STEATOSIS USING MACHINE LEARNING AND COLOR IMAGE PROCESSING

Actual Study Start Date :

Jun 30, 2018

Anticipated Primary Completion Date :

Dec 31, 2023

Anticipated Study Completion Date :

Dec 31, 2023

Arms and Interventions

Arm	Intervention/Treatment
Experimental: Liver from deceased donors This study included all consecutive subjects with chronic liver disease who underwent LT for the first time with a deceased donor liver	Diagnostic Test: Liver from deceased donors Liver donors photographed

Arm

Intervention/Treatment

Experimental: Liver from deceased donors

This study included all consecutive subjects with chronic liver disease who underwent LT for the first time with a deceased donor liver

Diagnostic Test: Liver from deceased donors

Liver donors photographed

Outcome Measures

Primary Outcome Measures

The main goal of this project is to create a machine learning model in order to quantify liver steatosis in liver donor faster, more objective and reliable than histological analysis and surgeons point-of-view. [4 weeks]
Accuracy

Secondary Outcome Measures

To build an image dataset to evaluate postransplant liver function. [1 week]
PDF will be evaluated according to Olthoff criteria

Eligibility Criteria

Criteria

Ages Eligible for Study:

N/A and Older

Sexes Eligible for Study:

All

Accepts Healthy Volunteers:

Inclusion Criteria:

Livers from donor donor brain death with informed consent before inclusion in the study was obtained from all participants or families.

Exclusion Criteria:

Age < 18 years old
Donor after cardiac death
Split
Cholestasis due to a biliary obstruction
Total bilirubin levels above 2,5 mg/dL
Glutamic oxaloacetic transaminase (SGOT)/ serum glutamatepyruvate transaminase (SGPT) levels and gamma-glutamyl transaminase (GGT) levels above 400 U/L
Cirrhotic livers