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

Accurate intraoperative assessment of intestinal tissue viability is critical in determining the extent of resection in cases of intestinal ischemia. Current evaluation methods are largely subjective and lack the precision required for reliable decision-making during surgery.

We aim to develop and validate a hyperspectral imaging (HSI) system combined with machine learning (ML) to objectively assess intestinal wall viability and differentiate between reversible and irreversible ischemia.

A portable HSI system was used to acquire spectral data from rat models with induced intestinal ischemia at different time points (1, 6, and 12 h). Tissue oxygen saturation was calculated using a two-wavelength algorithm. Spectral data were classified using an ML pipeline based on principal component analysis (PCA) and the XGBoost algorithm, trained on histologically validated tissue classes.

Tissue saturation decreased with prolonged ischemia (from 66% in healthy tissue to 21% after 12 h). Classification accuracy using PCA features reached 98% for intact tissue, 95% for possibly reversible ischemia, and 97% for irreversible ischemia. Classification maps closely matched tissue saturation distributions and histological findings. Initial clinical testing confirmed the system's sensitivity to ischemic changes in human subjects, although further training on human data is required for ML application.

HSI combined with ML provides an effective, non-invasive tool for real-time intraoperative assessment of intestinal viability. This approach improves the objectivity of surgical decision-making and may reduce unnecessary resections.