Complete Blood Counts at the Bedside

New hematology point-of-care devices perform as well as clinical lab testing

Catherine Crawford-Brown, MSc, MScComm

Catherine Crawford-Brown, MSc, MScComm, is a health science and research writer with a master’s in science communication from Laurentian University. She also has a master’s of science in pathology and...

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Published:Jul 06, 2021
|Updated:Nov 09, 2022
|5 min read

Complete blood count (CBC) is one of the most common clinical laboratory assays and comprises a multitude of tests that evaluate circulating blood cells.1 This assessment can be used to monitor general health status, scan for and diagnose conditions or diseases, and monitor the effects of certain treatments.

With traditional CBC, patients can wait hours or even days before receiving results because their samples need to be sent off to the lab and analyzed. Point-of-care testing (POCT) is an attractive solution that gets results to patients faster, making it possible to start interventions sooner and potentially improving patient outcomes.2

POCT has been robustly developed for glucose monitoring, pregnancy, infectious diseases, and other applications. However, this convenient testing option has lagged for CBC because of the complexity involved in identifying different cell types in blood samples. As a result, it has been difficult to design an easy-to-use POCT system that can replicate the CBCs currently performed using larger platforms in clinical labs. However, new technological advancements are now making CBCs at the bedside a reality.

Challenges of performing CBC at the point of care

In CBC, blood cell types need to be differentiated based on nuances such as size and morphology. To complicate this process, the same cell types can look different in a single sample if they are at different stages of maturity. Analytical hematology can also be negatively impacted by interferences that affect the measurements. These can include hemolysis, clumps of platelets, and nucleated red blood cells.3

"These easy-to-use devices can provide patients with results in the same clinic visit, allowing for faster treatment decisions, which could improve patient outcomes."

The current gold standard for CBC in the clinical lab is the Sysmex XN series.4 This FDA-approved, modular hematology platform comprehensively assesses whole blood samples. The Sysmex XN series is capable of testing for 27 parameters including white blood cell count, red blood cell count, platelet count, hemoglobin, hematocrit, and many others. The entire platform is automated, including sample mixing, aspiration, and analysis to eliminate potential user error. Each module can analyze up to 100 samples per hour and as many as nine modules can be lined up to create a high-throughput system.

Many of the current point-of-care (POC) CBC analyzers used today are miniaturized versions of the clinical lab standard. To accommodate the smaller size, sacrifices have been made in performance, so these devices analyze fewer parameters, report smaller ranges, flag fewer abnormal cells, and have reduced accuracy. Like the larger versions, they also require liquid reagents, complex calibration and set-up procedures, and frequent quality control, which makes them difficult to use in near-patient settings.5

Two platforms have emerged this year that could overcome the current limitations of POC CBC analyzers—HemoScreen and Sight OLO.6,7 Rather than adapting existing clinical lab platforms, both devices were built specifically for POCT CBC, so no compromises in performance had to be made. As a result, they can conduct the full, 19-parameter, five-part white blood cell differential CBC that is the standard in clinical labs. In clinical studies, HemoScreen and Sight OLO both performed as well as the clinical lab standard Sysmex XN series for all parameters.3,5


Produced by PixCell Medical, HemoScreen is the first FDA-approved POC hematology analyzer that integrates flow cytometry and machine vision in a single platform.3 The technology uses single-use cartridges that are designed to automatically prepare the sample for analysis by replicating lab protocols. As a result, neither the user nor the device comes into direct contact with the reagents, making the instrument easier to use and maintain.

Machine vision refers to a combination of hardware and software that allows a computer to perceive the environment. Systems that use machine vision can perform automated image acquisition, processing, segmentation, and pattern recognition. Their components include sensors, imagers, lighting, processors, software, and output devices. Machine vision systems are often used in industrial settings for material inspection and quality assurance.

Each cartridge contains a sealed reagent chamber, valves, and a microfluidic measurement chamber. The microfluidic chamber takes advantage of a naturally occurring phenomenon called viscoelastic focusing. As a suspension of cells flows through the chamber, the cells migrate toward the center of the flow until they reach a steady state and create a single cell layer that is essentially a flowing blood smear.

As the cells flow, machine vision analyzes thousands of images, extracting hundreds of features for each cell and producing a high number of high-resolution measurements. Artificial intelligence analyzes these details to differentiate between subtypes of cells and to identify abnormal cells. The algorithm can also identify interferences and potential failures, preventing the sharing of incorrect results.

Sight OLO

Sight OLO by Sight Diagnostics is another FDA-cleared hematological platform that takes advantage of the latest developments in artificial intelligence and machine vision.5 This device also uses disposable, single-use test kits for creating and staining blood smears that can accommodate both finger prick and venous blood samples. 

Unlike HemoScreen, Sight OLO relies on a novel method for monolayer formation where the sample is drawn into the imaging chamber through capillary action. A combination of brightfield and fluorescence microscopy is then used to identify the different populations of cells based on their morphology and chemical signatures. The automated fluorescence microscope takes more than 1,000 high-quality multispectral micrographs per blood sample.

For each image, the analyzer quantifies and characterizes the cell populations present. Different analysis pipelines are used to identify red blood cells, white blood cells, and platelets. The algorithm generates candidates based on the fluorescent and morphologic characteristics and then performs a more in-depth characterization to confirm the result. Additionally, Sight OLO scans for abnormal cells as well as interferences that could lead to incorrect results.

Towards multi-sample POCT

For specific clinical applications, including managing cancer patients, HemoScreen and Sight OLO could have a major impact on care. These easy-to-use devices can provide patients with results in the same clinic visit, allowing for faster treatment decisions, which could improve patient outcomes.9

While these developments are exciting, HemoScreen and Sight OLO cannot yet replace the services offered by the clinical lab. For high-throughput scenarios, relying solely on these devices for CBC could lead to a backlog of samples.10 Single-use cartridges, while convenient, can become expensive when used in large quantities. In this situation, it would likely be faster, more economical, and a better use of resources to send the samples to the clinical lab for analysis. Developing multi-sample devices for POCT could help overcome these limitations.

Correction: An earlier version of this article indicated that Sight OLO by Sight Diagnostics was FDA-approved for POCT when it has in fact only received FDA 510(k) clearance and is not yet approved for POCT in the US. Updated September 17, 2021.


1.    “Complete Blood Count (CBC).” Lab Tests Online, 19 February 2021,

2.    Nichols, J.H. "Chapter 19 - Point-of-care testing." Contemporary Practice in Clinical Chemistry 4th ed., edited by William Clarke and Mark A. Marzinke. Academic Press, 2020, 323–336.

3.    Bransky, Avishay, et al. “A novel approach to hematology testing at the point of care.” Journal of Applied Laboratory Medicine 6.2 (2021): 532–542.

4.    U.S. Food and Drug Administration. “510(k) Substantial Equivalence Determination Decision Summary.”

5.    Bachar, Neta, et al. “An artificial intelligence-assisted diagnostic platform for rapid near-patient hematology.” medRxiv 27 April 2021.

6.    “Hemoscreen™” PixCell Medical, 25 May 2021,

7.    “OLO.” Sight Diagnostics,

8.    Golnabi, H. and Asadpour, A. “Design and application of industrial machine vision systems.” Robotics and Computer-Integrated Manufacturing 23.6 (2007): 630–637.

9.    Kristian Kur, D., et al. “The HemoScreen hematology point-of-care device is suitable for rapid evaluation of acute leukemia patients.” International Journal of Laboratory Hematology 43.1 (2020): 52–60.

10.    McCoy, J., et al. “Point-of-care testing vs. laboratory testing during high patient volume situations.” Open Journal of Emergency Medicine 7 (2019): 49–56.

Catherine Crawford-Brown, MSc, MScComm

Catherine Crawford-Brown, MSc, MScComm, is a health science and research writer with a master’s in science communication from Laurentian University. She also has a master’s of science in pathology and molecular medicine from Queen’s University where she worked on developing a liquid biopsy for breast cancer. She was formerly the digital media editor for Lab Manager.


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