LiverColor: Machine Learning in Liver Photographs

Study Overview
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Brief Summary
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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
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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.

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

Conditions
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Brain Death, Liver Steatosis

Intervention / Treatment
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* Diagnostic Test: Liver from deceased donors


Participation Criteria
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Eligibility Criteria
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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
Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Design Details

Primary Purpose: Diagnostic
Intervention Model: Single Group Assignment
Interventional Model Description: Liver from deceased donors
Masking: None (Open Label)


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Experimental: Liver from deceased donors<br>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<br>* Liver donors photographed<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| 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 | 4 weeks | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| To build an image dataset to evaluate postransplant liver function. | PDF will be evaluated according to Olthoff criteria | 1 week | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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machine learning, liver donor (DBD), liver steatosis, liver pictures, liver pool