AI-Enhanced Blood Cell Recognition and Analysis: Advancing Traditional Microscopy with the Web-Based Platform IKOSA

Abstract

Microscopy of stained blood smears is still a ubiquitous technique in pathology. It is often used in addition to automated electronic counters or flow cytometers to evaluate leukocytes and their morphologies in a rather simple manner and has low requirements for resources and equipment. However, despite the constant advances in microscopy, computer science, and pathology, it still usually follows the traditional approach of manual assessment by humans. We aimed to extend this technique using AI-based automated cell recognition methods while maintaining its technical simplicity. Using the web platform IKOSA, we developed an AI-based workflow to segment and identify all blood cells in DAPI-Giemsa co-stained blood smears. Thereby, we could automatically detect and classify neutrophils (young and segmented), lymphocytes, eosinophils, and monocytes, in addition to erythrocytes and platelets, in contrast to previously published algorithms, which usually focus on only one type of blood cell. Furthermore, our method delivers quantitative measurements, unattainable by the classical method or formerly published AI techniques, and it provides more sophisticated analyses based on entropy or gray-level co-occurrence matrices (GLCMs), which have the potential to monitor changes in internal cellular structures associated with disease states or responses to treatment. We conclude that AI-based automated blood cell evaluation has the potential to facilitate and improve routine diagnostics by adding quantitative shape and structure parameters to simple leukocyte counts of classical analysis.