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Related Experiment Video

Updated: Oct 18, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Automatic Liver Viability Scoring with Deep Learning and Hyperspectral Imaging.

Eric Felli^1,2,3, Mahdi Al-Taher⁴, Toby Collins⁴

¹Hepatology, Department of Biomedical Research, Inselspital, University of Bern, 3008 Bern, Switzerland.

Diagnostics (Basel, Switzerland)

|September 28, 2021

Summary

This summary is machine-generated.

Hyperspectral imaging combined with artificial intelligence accurately predicts liver viability and reperfusion injury after hepatic artery occlusion. This non-invasive approach shows promise for intraoperative assessment, guiding surgical decisions and improving patient outcomes.

Keywords:

CNNs artificial intelligence convolutional networks deep learning hepatic artery occlusion hyperspectral imaging liver viability

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Published on: April 28, 2022

Area of Science:

Medical imaging
Hepatology
Artificial Intelligence

Background:

Hyperspectral imaging (HSI) evaluates liver oxygenation and ischemia.
Hepatic artery occlusion (HAO) causes hypoxia and ischemia-reperfusion injury (IRI).
Intraoperative prediction of IRI using HSI remains unassessed.

Purpose of the Study:

To assess HSI's ability to detect and predict liver viability and IRI intraoperatively.
To develop an AI-based analysis of HSI for liver viability assessment.

Main Methods:

Combined HSI with quantitative optical tissue property extraction.
Utilized a deep learning model (convolutional neural networks) for AI analysis.
Evaluated liver viability in an HAO rat model.

Main Results:

AI score of liver viability strongly correlated with capillary lactate (r = -0.78, p = 0.0320) and Suzuki's score (r = -0.96, p = 0.0012).
CD31 immunostaining confirmed microvascular damage consistent with AI score.
Demonstrated significant correlation between AI-derived liver viability and IRI indicators.

Conclusions:

HSI-AI analysis shows potential for predicting liver viability and IRI.
This non-invasive method could guide intraoperative decision-making.
Further development is warranted for clinical application in liver surgery.