Back to results"LiverColor": Machine Learning in Liver Photographs
Primary Purpose
Brain Death, Liver Steatosis
Status
Recruiting
Phase
Not Applicable
Locations
Spain
Study Type
Interventional
Intervention
Liver from deceased donors
Sponsored by
About this trial
This is an interventional diagnostic trial for Brain Death focused on measuring machine learning, liver donor (DBD), liver steatosis, liver pictures, liver pool
Eligibility Criteria
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
Sites / Locations
- Concepción Gómez-GavaraRecruiting
Arms of the Study
Arm 1
Arm Type
Experimental
Arm Label
Liver from deceased donors
Arm Description
This study included all consecutive subjects with chronic liver disease who underwent LT for the first time with a deceased donor liver
Outcomes
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.
Accuracy
Secondary Outcome Measures
To build an image dataset to evaluate postransplant liver function.
PDF will be evaluated according to Olthoff criteria
Full Information
1. Study Identification
Unique Protocol Identification Number
NCT05202886
Brief Title
"LiverColor": Machine Learning in Liver Photographs
Official Title
"LiverColor": AN ALGORITHM QUANTIFICATION OF LIVER GRAFT STEATOSIS USING MACHINE LEARNING AND COLOR IMAGE PROCESSING
Study Type
Interventional
2. Study Status
Record Verification Date
January 2022
Overall Recruitment Status
Recruiting
Study Start Date
June 30, 2018 (Actual)
Primary Completion Date
December 31, 2023 (Anticipated)
Study Completion Date
December 31, 2023 (Anticipated)
3. Sponsor/Collaborators
Responsible Party, by Official Title
Principal Investigator
Name of the Sponsor
Hospital Vall d'Hebron
4. Oversight
Studies a U.S. FDA-regulated Drug Product
No
Studies a U.S. FDA-regulated Device Product
No
Data Monitoring Committee
Yes
5. 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.
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.
6. Conditions and Keywords
Primary Disease or Condition Being Studied in the Trial, or the Focus of the Study
Brain Death, Liver Steatosis
Keywords
machine learning, liver donor (DBD), liver steatosis, liver pictures, liver pool
7. Study Design
Primary Purpose
Diagnostic
Study Phase
Not Applicable
Interventional Study Model
Single Group Assignment
Model Description
Liver from deceased donors
Masking
None (Open Label)
Allocation
N/A
Enrollment
246 (Anticipated)
8. Arms, Groups, and Interventions
Arm Title
Liver from deceased donors
Arm Type
Experimental
Arm Description
This study included all consecutive subjects with chronic liver disease who underwent LT for the first time with a deceased donor liver
Intervention Type
Diagnostic Test
Intervention Name(s)
Liver from deceased donors
Intervention Description
Liver donors photographed
Primary Outcome Measure Information:
Title
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.
Description
Accuracy
Time Frame
4 weeks
Secondary Outcome Measure Information:
Title
To build an image dataset to evaluate postransplant liver function.
Description
PDF will be evaluated according to Olthoff criteria
Time Frame
1 week
10. Eligibility
Sex
All
Accepts Healthy Volunteers
No
Eligibility Criteria
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
Central Contact Person:
First Name & Middle Initial & Last Name or Official Title & Degree
Concepcion Gómez-Gavara, PhD
Phone
+34696690464
Email
imgoga@hotmail.com
Overall Study Officials:
First Name & Middle Initial & Last Name & Degree
Concepcion Gómez-Gavara, PhD
Organizational Affiliation
Vall D´Hebron University Hospital
Official's Role
Principal Investigator
Facility Information:
Facility Name
Concepción Gómez-Gavara
City
Barcelona
Country
Spain
Individual Site Status
Recruiting
Facility Contact:
First Name & Middle Initial & Last Name & Degree
Concepción Gómez-Gavara, PhD
Phone
0034696690464
Email
imgoga@hotmail.com
12. IPD Sharing Statement
Plan to Share IPD
No
Learn more about this trial
"LiverColor": Machine Learning in Liver Photographs
We'll reach out to this number within 24 hrs