Blood cell morphology analysis is a clinical test used to test the number and volume distribution of red blood cells and platelets in the blood, the total number of white blood cells, and classify them. This includes blood cell image capture, visual observation and description, white blood cell count, morphological description of red blood cells, platelet determination, and more. The study of blood cell morphology supports pathologists in their clinical diagnosis of patients who may be at risk of Heart Failure, Anemia, Cancers, and other Toxicological Blood Disorders.
Modern blood analyzers can classify cells by flow cytometry or chemical staining, but at present, manual classification and verification are still required.
In the past, researchers have relied on manual approaches that involve traditional morphological analysis of the blood sample. These samples are collected and tests are conducted individually by pathologists with manual light microscopy, based on linear measurement provided by an ocular micrometer.
While this method is the gold standard, it has the disadvantages of being labor-intensive, requiring comprehensive and continuous training of personnel, and also being subject to a large variability from different observers. In addition, it is difficult to archive images and use secondary consultations for the final confirmation.
The future is leading blood pathology researchers to streamline their work with digital
imaging systems using a computer system-coupled camera to scan and take images in a digital format. Digital images of individual cells and tissue staining are then used as the input material for an AI (Artificial Intelligence)-aided classification based on parameters, such as geometry, color, and texture features, which are established by multiple experienced pathologists with the most updated knowledge of abnormality of cells and tissues. Future morphological inspection systems will be increasingly more automated, minimizing human intervention and error.
Technical advantages of digital imaging:
Highly consistent output
Combination and integration of the knowledge of multiple experienced pathologists
Artificial Intelligence aided classification with a high accuracy
Computer-based deep learning and training for optimizing standardization
Operational advantages of digital imaging:
High efficiency and streamline collaboration among pathologists at different locations
Automatically identifies and takes images with Optimized Workload Management
Avoid the variability from different observers
Enhances training/cross-training for medical personnel with a large and customizable digital images
Convenient archive of images
Enables remote consultation
Diagnostic test equipment needs to have the ability to correctly identify the diseased cells versus unaffected cells. However, factors such as image capturing camera type, environment and light source, non-adequate illumination in digital imaging can lead to variation in detection and analysis, which could affect the accuracy of the measurement results. Therefore, the development of a blood cell analyzer with the required sensitivity, specificity, accuracy, positive predictive value and negative predictive value will be no easy task.
OMEC Medical is developing instrumentation and software for life sciences.
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