We use cookies to understand how you use our site and to improve your experience. This includes personalizing content and advertising. To learn more, click here. By continuing to use our site, you accept our use of cookies. Cookie Policy.

Features Partner Sites Information LinkXpress hp
Sign In
Advertise with Us

Download Mobile App

Unique AI-Based Approach Automates Clinical Analysis of Blood Data

By LabMedica International staff writers
Posted on 19 Jul 2023
Print article
Image: AI-assisted analysis of single-cell blood data brings precision diagnostics to immune medicine (Photo courtesy of Freepik)
Image: AI-assisted analysis of single-cell blood data brings precision diagnostics to immune medicine (Photo courtesy of Freepik)

The clinical analysis of blood data, known as cytometry, is a labor-intensive process that is largely subjective, even for the most skilled laboratory staff. Current cytometry-based diagnostics for blood cancer and other immune diseases require doctors and analysts to evaluate complex, high-dimensional data sets. This analysis, which averages around 20 minutes per sample, is not only time-consuming but also faces a significant shortage of trained personnel. Moreover, the process is quite subjective, with approximately 30% variability in analysis between different operators. These challenges have limited the use of cytometry data for more personalized treatment. Now, a cloud-based machine learning platform can help labs manage their caseloads, provide an objective second opinion to every patient, and offer new insights to physicians for tailoring treatments to every patient's unique immune system.

hema.to (Munich, Germany) offers user-friendly software for clinical decision support in blood cancer cases using cytometry data. This artificial intelligence (AI)-powered tool, which is FDA registered and has CE-IVD approval, streamlines the diagnostic workflow, benefiting both diagnosticians and patients. Already implemented in leading hematology labs, the AI software is now being scaled up to support blood cancer diagnostics in laboratories across Europe and demonstrate significant improvements in diagnostic quality.

hema.to's proprietary algorithms, developed using its extensive and continuously growing database of diverse cytometry data sources, can predict disease biomarkers directly from the raw data generated by blood measurement devices. This addresses a hitherto unresolved issue caused by the lack of standardized measurement protocols, resulting in complex data variability that previously hampered automation. The company specializes in integrating data from various sources to identify predictive disease biomarkers. This technology has already been incorporated into the regular clinical workflow of two German labs for decision support. hema.to now plans to broaden its user base, expand the range of supported diseases, and enhance the quality of its AI models.

“Europe’s largest leukemia lab had the real need to speed-up their internal analysis workflows and worked with us to build a world-first AI prototype,” said Karsten Miermans, co-founder and CEO of hema.to. After the success of demonstration of AI-assisted clinical cytometry, we noticed that all labs have the same manual workflows and pain points. We founded hema.to two years ago to help labs across the world with their clinical cytometry workflows.”

Related Links:

Platinum Member
Flu SARS-CoV-2 Combo Test
OSOM® Flu SARS-CoV-2 Combo Test
Magnetic Bead Separation Modules
POCT Fluorescent Immunoassay Analyzer
Gold Member
Fully Automated Cell Density/Viability Analyzer
BioProfile FAST CDV

Print article


Clinical Chemistry

view channel
Image: PhD student and first author Tarek Eissa has analyzed thousands of molecular fingerprints (Photo courtesy of Thorsten Naeser / MPQ / Attoworld)

Screening Tool Detects Multiple Health Conditions from Single Blood Drop

Infrared spectroscopy, a method using infrared light to study the molecular composition of substances, has been a foundational tool in chemistry for decades, functioning similarly to a molecular fingerprinting... Read more

Molecular Diagnostics

view channel
Image: Protein ‘signatures’ obtained via a blood sample can be used to predict the onset of 67 diseases (Photo courtesy of Queen Mary University of London)

Protein Signatures in Blood Can Predict Risk of Developing More Than 60 Diseases

Measuring specific proteins to diagnose conditions like heart attacks, where troponin is tested, is a well-established clinical practice. Now, new research highlights the broader potential of protein measurements... Read more


view channel
Image: The Simplexa C. auris direct kit is a real-time polymerase chain reaction assay run on the LIAISON MDX instrument (Photo courtesy of Diasorin)

Novel Molecular Test to Help Prevent and Control Multi Drug-Resistant Fungal Pathogen in Healthcare Settings

Candida auris (C. auris) is a rapidly emerging multi drug-resistant fungal pathogen that is commonly found in healthcare environments, where it presents a challenge due to its ability to asymptomatically... Read more


view channel
Image: The tool can improve precision oncology by accurately predicting molecular subtypes and therapy responses (Photo courtesy of Shutterstock)

Computational Tool Integrates Transcriptomic Data for Improved Breast Cancer Diagnosis and Treatment

Breast cancer is the most commonly diagnosed cancer globally, presenting in various subtypes that require precise identification for effective, personalized treatment. Traditionally, cancer subtyping has... Read more


view channel
Image: Beckman Coulter will utilize the ALZpath pTau217 antibody to detect key biomarker for Alzheimer\'s disease on its DxI 9000 immunoassay analyzer (Photo courtesy of Beckman Coulter)

Beckman Coulter Licenses Alzpath's Proprietary P-tau 217 Antibody to Develop Alzheimer's Blood Test

Cognitive assessments have traditionally been the primary method for diagnosing Alzheimer’s disease, but this approach has its limitations as symptoms become apparent only after significant brain changes... Read more
Copyright © 2000-2024 Globetech Media. All rights reserved.