Leveraging infrared spectral signatures supported by machine learning to evaluate the patency of coronary artery bypass graft conduits: A preliminary study.

Coronary artery bypass grafting (CABG) is a critical intervention for obstructive coronary artery disease, with the graft patency significantly impacting long-term outcomes. This study evaluates the potential of infrared (IR) spectroscopic imaging combined with machine learning to assess biochemical markers for determining the patency in internal mammary artery (IMA), radial artery (RA), and saphenous vein (SV) conduits. Tissue samples of the grafts from CABG patients were analysed using IR spectroscopic imaging. Hyperspectral images were pre-processed and subjected to unsupervised hierarchical cluster analysis (UHCA) to identify regions with high triglyceride content. Spectral signatures of triglycerides, proteins, nucleic acids, and carbohydrates were extracted, and ratiometric analysis revealed significant variations in glycosylation and protein-to-lipid ratios among graft types (p < 0.005). The IMA exhibited lower glycosylation and protein-to-lipid ratios compared to the RA and SV. Principal component analysis followed by linear discriminant analysis (PCA-LDA) achieved clear differentiation of graft types, with second-derivative spectra enhancing class separation. These findings highlight IR spectroscopy's potential to provide detailed biochemical insights for pre-operative graft selection, potentially improving CABG outcomes by identifying conduits with optimal patency characteristics. This preliminary study evaluates the feasibility of IR spectroscopy for assessing the quality of autologous bypass grafts prior to CABG. Further studies involving larger patient cohorts and evaluation of potential gender-related differences would help strengthen and validate these findings.