Label‐free histological analysis of retrieved thrombi in acute ischemic stroke using optical diffraction tomography and deep learning

For patients with acute ischemic stroke, histological quantification of thrombus composition provides evidence for determining appropriate treatment. However, the traditional manual segmentation of stained thrombi is laborious and inconsistent. In this study, we propose a label‐free method that combines optical diffraction tomography (ODT) and deep learning (DL) to automate the histological quantification process. The DL model classifies ODT image patches with 95% accuracy, and the collective prediction generates a whole‐slide map of red blood cells and fibrin. The resulting whole‐slide composition displays an average error of 1.1% and does not experience staining variability, facilitating faster analysis with reduced labor. The present approach will enable rapid and quantitative evaluation of blood clot composition, expediting the preclinical research and diagnosis of cardiovascular diseases. For patients with acute ischemic stroke, thrombus composition can provide evidence for determining appropriate treatment. We combine optical diffraction tomography (ODT) and deep learning for automated and quantitative analysis of thrombus composition. The deep learning model predicts the whole‐slide composition map of red blood cells and fibrin, through accurate patch classification. This approach can reduce both labor and variability of thrombus histology, which traditionally involves manual measurements and analyses.