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Related Experiment Video

Updated: Jun 8, 2026

Murine Model of Intestinal Ischemia-reperfusion Injury

Murine Model of Intestinal Ischemia-reperfusion Injury

Published on: May 11, 2016

Hyperspectral and machine-learning-based classification of ischemic intestinal tissue.

Valery V Shupletsov¹, Ilya A Goryunov¹, Nikita A Adamenkov^1,2

¹Orel State University, Research & Development Center of Biomedical Photonics, Orel, Russia.

Journal of Biomedical Optics

|November 3, 2025

Summary

Hyperspectral imaging (HSI) combined with machine learning (ML) objectively assesses intestinal viability during surgery. This non-invasive tool improves surgical decisions and reduces unnecessary tissue removal in cases of intestinal ischemia.

Keywords:

XGBoost hyperspectral imaging intestinal ischemia machine learning tissue oxygen saturation

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Last Updated: Jun 8, 2026

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Label-free, High-Resolution 3D Imaging and Machine Learning Analysis of Intestinal Organoids via Low-Coherence Holotomography

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Published on: August 12, 2025

Area of Science:

Medical imaging
Surgical assessment
Biomedical engineering

Background:

Intraoperative assessment of intestinal tissue viability is crucial for determining resection extent in intestinal ischemia.
Current subjective methods lack precision for reliable surgical decision-making.

Purpose of the Study:

Develop and validate a hyperspectral imaging (HSI) system integrated with machine learning (ML).
Objectively assess intestinal wall viability and differentiate between reversible and irreversible ischemia.

Main Methods:

A portable HSI system acquired spectral data from rat models with induced intestinal ischemia.
Tissue oxygen saturation was calculated using a two-wavelength algorithm.
ML pipeline (PCA and XGBoost) classified spectral data trained on histological validation.

Main Results:

Tissue saturation decreased significantly with prolonged ischemia (66% to 21% at 12h).
ML classification achieved high accuracy (95-98%) for different ischemia states.
HSI-ML maps correlated with tissue saturation and histology; initial human tests showed sensitivity.

Conclusions:

HSI combined with ML offers an effective, non-invasive tool for real-time intraoperative intestinal viability assessment.
This approach enhances objectivity in surgical decision-making and can minimize unnecessary resections.