LiverColor: Machine Learning in Liver Photographs

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

Status

UNKNOWN

Phases

Study type

Interventional

Source

ClinicalTrials.gov

Registry ID

NCT05202886

Enrollment

246

Registered

2022-01-24

Start date

2018-06-30

Completion date

2023-12-31

Last updated

2022-01-24

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Brain Death, Liver Steatosis

Keywords

machine learning, liver donor (DBD), liver steatosis, liver pictures, liver pool

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.

Interventions

DIAGNOSTIC_TESTLiver from deceased donors

Liver donors photographed

Study design

Allocation

Intervention model

SINGLE_GROUP

Primary purpose

DIAGNOSTIC

Masking

NONE

Intervention model description

Liver from deceased donors

Eligibility

Sex/Gender

ALL

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

Design outcomes

Primary

Measure	Time frame	Description
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

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

Countries

Spain

Contacts

Primary ContactConcepcion Gómez-Gavara, PhD

imgoga@hotmail.com+34696690464

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026