Pre-screening of endomysial microvessel density by fast random forest image processing machine learning algorithm accelerates recognition of a modified vascular network in idiopathic inflammatory myopathies.

Abstract

Biomarkers for discrimination among different subgroups of idiopathic inflammatory myopathies (IIM) are difficult to identify and may involve multiple laboratory tests and time-consuming procedures. We assessed the potential for artificial intelligence (AI) to extract features such as density of endomysial microvessels based on automatic analysis of the CD31⁺ vascular network on muscle biopsy images. We also assessed the potential of this technique to save time and its agreement rate with analyses based on the manual selection of microvessels from the same images. A total of 84 images from 84 patients with IIM, diagnosed between 2014 and 2020, were retrieved and analyzed using the Fast Random Forest (FRF) technique. We built a lightweight and explainable algorithm for calculating the pixel percentage of CD31⁺ endomysial capillaries. The FRF technique applied on images of CD31-stained muscle sections achieved a good performance in the recognition of microvessels by estimating their density over a standard area corresponding to a sample of microscope image. The time spent for this analysis was 90% less than the manual choice of microvessels (estimated time considering the computational time and the time spent to manually detecting the microvessels features). The good performance of the FRF demonstrates that the CD31 pixel percentage of endomysial capillaries is sufficient for a correct estimation. Finally, the paper proposes a procedure to integrate AI in the pre-screening process.