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Source: DKMS
Every year on May 28, the world observes World Blood Cancer Day – a global initiative founded in 2014 by DKMS to raise awareness of leukemia, lymphoma, and myeloma, and to advocate for earlier diagnosis and improved access to treatment. AI hematology analyzers are transforming how laboratories interpret the complete blood count (CBC) — one of the most frequently ordered tests in clinical medicine. Blood cancers are notoriously difficult to detect early: initial symptoms such as fatigue, fever, and unexplained weight loss are easily mistaken for other conditions. Yet early detection remains one of the most critical factors in determining patient outcomes — making accurate, reliable blood analysis more important than ever.
CBC provides a foundational snapshot of hematological status. But in cases involving abnormal cell morphology, automated analyzers reach the limits of what they can reliably tell us — and the burden falls back on skilled laboratory staff to perform manual smear reviews. In an era of growing workloads and staffing shortages, this dependency has become a structural bottleneck.
Why Conventional Hematology Analyzers Fall Short
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Most hematology analyzers today rely on flow cytometry, classifying blood cells based on size and internal complexity through electrical impedance or light scattering. While fast and high-throughput, this approach does not directly evaluate cell morphology.
This creates a clinically meaningful gap. Lymphocytes and monocytes can produce overlapping signals in flow cytometry, making it difficult to detect subtle morphological changes — the kind an experienced hematologist identifies through nuclear shape, cytoplasmic color, and granule distribution. Blast screening compounds the problem: flag systems tend to over-flag blast-like signals, generating high false positive rates and unnecessary manual review. Without cell images, laboratory staff and clinicians also lack a shared visual basis for diagnostic communication — a gap the ICSH Guidelines on Digital Morphology Analyzers (2019) identify as a key area for improvement in modern hematology workflows.
A Single Workflow for CBC and Morphology: The miLab™ BCM Approach
Source: noul
miLab™ BCM, designed as an AI hematology analyzer, addresses these limitations within a single, fully automated workflow. Using just 5 µL of whole blood, the device completes smear preparation, staining, imaging, and AI-based cell classification — all within a single-use cartridge, with no manual handling required.
Central to this is a dual-imaging strategy: unstained images extract RBC parameters, while stained images provide WBC and platelet data — delivering quantitative CBC results and morphological classification within one test cycle. NGSI (Next-Generation Staining & Immunostaining) solid-state staining minimizes variability from environmental conditions or operator technique, ensuring reproducible output regardless of setting.
The AI algorithm provides a 6-part WBC differential — neutrophils, lymphocytes, monocytes, eosinophils, basophils, and immature granulocytes (IG) — and separately flags blast, abnormal lymphocytes, and atypical lymphocytes as morphology flags. Every result includes AI-annotated cell images, enabling clinicians and laboratory staff to immediately identify flagged cells, review classification rationale, and share findings remotely. Unlike conventional microscopy, which provides raw images without automated classification, miLab™ BCM delivers contextualised morphological findings within the broader CBC result.
Clinical Validation: Two Studies, Two Perspectives
Source: noul
The clinical case for this AI hematology analyzer is built on two studies with different patient populations and clinical objectives.
The first study evaluated CBC and 6-part differential performance across 102 samples — 72 adult and 30 pediatric — compared against the Sysmex XN-series. Strong correlations were observed across all major parameters: WBC (r=0.981), HGB (r=0.959), and PLT (r=0.979), with clinically acceptable agreement across the full differential including IG. Consistent precision was maintained across both adult and pediatric samples — establishing suitability for neonatal and pediatric settings where minimal sample volume is critical. Recognized for its scientific merit, the study was presented at ISLH 2026.
The second study, conducted by researchers at Asan Medical Center, evaluated how closely miLab™ BCM results aligned with expert final diagnoses across 403 samples. The system demonstrated higher concordance with expert manual review than flow cytometry for lymphocytes (r=0.9372) and monocytes (r=0.8911). In blast screening, miLab™ BCM achieved an NPV of 96% — meaning 96% of blast-negative classifications were confirmed by expert review (sensitivity 88%, specificity 80%). This positions miLab™ BCM as a reliable AI hematology analyzer for ruling out blast-negative cases and reducing unnecessary manual review in suspected blood cancer workups. The study was accepted for presentation at EHA 2026.
Together, these studies show that miLab™ BCM’s automated workflow eliminates the need for separate manual smear review while delivering morphological insight that more closely reflects expert judgment than conventional flow cytometry. Clinicians gain AI-annotated cell images — not just raw microscopy images — enabling faster, evidence-based decisions. Laboratory workload decreases, and the window for early detection of hematological disease widens.
Validated Across Populations. Ready for the Field.
Source: noul
At SIOP Europe 2026 in Glasgow, miLab™ BCM was exhibited to pediatric hematology specialists, with on-site evaluation of its small-volume sample handling and abnormal cell screening capabilities. In June, Asan Medical Center data will be presented at EHA 2026 in Stockholm — bringing independently validated evidence to one of the world’s leading hematology conferences.
The clinical data spans adults, pediatric patients, and neonates across multiple institutions — reflecting an AI hematology analyzer designed not just for central laboratories, but for satellite labs, pediatric wards, emergency departments, and any setting where accurate, reproducible blood analysis is needed.
miLab™’s AI-driven digital microscopy vision extends beyond hematology. Built on the same technological foundation, miLab™ MAL addresses malaria diagnostics and miLab™ CER supports cervical cancer cytology screening — each as a dedicated platform for distinct clinical needs.
Achieving both workflow efficiency and diagnostic confidence is no longer a trade-off. The data and real-world performance of this AI hematology analyzer show that this standard is already within reach. To learn more or request a demo, visit noul.com or contact us.
