ResourceClinical Diagnostics
AI in hematology: Sepsis detection and differentiation
11 Nov 2025This study investigates how AI can be integrated into hematology workflows to overcome key challenges in sepsis diagnosis, including delayed detection, limited biomarker specificity, and nonspecific clinical presentations. By leveraging the HORIBA Yumizen H series analyzers together with the Generative Manifold Learning (GML) framework developed by GeodAIsics, Horiba aims to transform sepsis screening using only Complete Blood Count (CBC) data, enabling faster, more accurate, and accessible diagnostic support.
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Artificial Intelligence / Machine LearningArtificial intelligence (AI) and machine learning (ML) are transformative technologies used to analyze complex data, identify patterns, and make data-driven predictions across diverse scientific fields. Automate the analysis of large or complex data sets using AI algorithms and leverage machine learning models to improve diagnostics, accelerate drug discovery, and refine experimental design. Discover the best AI/ML software, platforms, and analytical tools in our peer-reviewed product directory: compare features, read customer reviews, and request pricing directly from manufacturers.HematologyIn Haematology / Hematology, complete blood cell counts (or full blood counts) are obtained using automated blood count analyzers to enumerate blood cell types. Hematology also encompasses haemostasis and coagulation, thrombophilia and hemophilia, plasma viscosity and ESR analysis, hemoglobinopathies, cell morphology and haematinic measurement.SepsisSepsis is a life-threatening response to infection causing organ dysfunction. Research focuses on early detection, biomarkers, and therapies to reduce mortality. Explore tools for sepsis diagnostics and analysis in our peer-reviewed product directory, including detection kits and biomarkers.

