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Related Concept Videos

Gross Anatomy of the Liver01:17

Gross Anatomy of the Liver

The liver, the largest gland within the human body, is a firm and reddish-brown organ. This wedge-shaped structure weighs approximately 1.5 kg and occupies a significant portion of the right hypochondriac and epigastric regions. It extends more to the right of the body's midline than to the left.
Located under the diaphragm, the liver is almost entirely ensconced within the rib cage, providing it with substantial protection. Except for the superior most bare area, the liver's surface is covered...

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Related Experiment Video

Updated: Jun 29, 2026

Rat Model of the Associating Liver Partition and Portal Vein Ligation for Staged Hepatectomy ALPPS Procedure
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LiverColor: An Artificial Intelligence Platform for Liver Graft Assessment.

Gemma Piella1, Nicolau Farré1, Daniel Esono1

  • 1Engineering Department, Universitat Pompeu Fabra, 08018 Barcelona, Spain.

Diagnostics (Basel, Switzerland)
|August 10, 2024
PubMed
Summary

A new AI tool, LiverColor, accurately assesses liver steatosis using image analysis, improving donor liver selection for transplantation. This technology offers a faster, more reliable alternative to subjective visual checks and invasive biopsies.

Keywords:
colour and texture analysishepatic steatosisliver assessmentmobile apporgan transplantation

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Area of Science:

  • Hepatology and Transplantation
  • Medical Imaging and Artificial Intelligence

Background:

  • Hepatic steatosis (fatty liver) is a primary reason for discarding donor livers, increasing post-transplant complications.
  • Current evaluation methods like liver biopsy are invasive and impractical during procurement.
  • Surgeons' visual assessments are subjective and error-prone, impacting donor liver selection.

Purpose of the Study:

  • To develop and validate a rapid, accurate, and objective method for detecting hepatic steatosis in donor livers.
  • To improve decision-making during liver procurement and enhance transplantation outcomes.
  • To create a software platform, LiverColor, integrating image analysis and machine learning for graft classification.

Main Methods:

  • Development and validation of machine learning models (random forests, support vector machine) using an in-house dataset of 192 liver photographs.
  • Extraction of color and texture features from images for supervised classification of steatosis levels.
  • Comparison of algorithm performance against liver biopsy results and expert surgeon visual assessments.

Main Results:

  • The LiverColor platform achieved an 85% accuracy and an AUC of 0.82, significantly outperforming subjective surgeon evaluations.
  • The AI-driven approach demonstrated superior predictive performance compared to visual inspection.
  • Surgeons positively rated the platform's usability and data visualization features in simulated and real clinical settings.

Conclusions:

  • Image analysis combined with machine learning provides an effective and safe method for identifying suitable donor livers.
  • LiverColor can reduce reliance on subjective assessments, improving the accuracy and efficiency of liver evaluations.
  • This technology holds potential to enhance liver transplantation success rates by optimizing donor liver selection.