The advent of digital image-based cell counting and application of AI will add more value to patient care, but microscopic examination by experienced technicians is still the gold standard in diagnosis of hematological abnormalities.
Over the past decades, hematology analyzers have experienced great technical advancements, featuring low turnaround time, enhanced precision, and accuracy. The challenges associated with manual methods like immature cells, reliability on results, distribution error, and statistical error are being replaced with precise, safe, and dependable automated hematology systems.
The hematology market in India is highly dependent on imports, the major suppliers being China, Europe, Japan, and the USA. The current fluctuation in the forex rates is one of the major concerns in the Indian hematology segment.
Out of the existing 110,000 laboratories in India, more than 40,000 laboratories are in the Tier-II and Tier-III cities, covering the rural and semi-urban population with limited access to majority of IVD technologies. As per statistics, more than 70 percent of the Indian population lives in the rural areas, and the benefits of IVD technologies remain underserved to the patients in these rural areas. One of the major changes in the hematology segment is the introduction of highly compact 3-part and 5-part hematology systems. These compact systems are well suited for rural India, and are expected to bring in changes in the IVD industry. The penetration of CBC in rural areas will help to assess the prevalence of various anemia among the rural population, and its control, which is an objective of the NRHM, and an affordable and accurate 3-part hematology analyzer will go a long way to achieve this objective.
With COVID-19 pandemic refusing to relent, most laboratories in the country are operating at very low output, resulting in minimal purchases of instruments. The procurement by government labs also is low. With the recent approval of COVID-19 package of Rs 15,000 crore to build on health infrastructure till March 2024, some expenditure is expected on IgG ELISA assay kits used for the qualitative detection of novel coronavirus-infected pneumonia cases, suspected clustering cases, and other new coronaviruses in serum samples through measurement of the COVID-19 IgG antibody. The PCR tests currently used to directly detect the presence of an antigen can be very labor intensive, with several stages at which errors may occur between sampling and analysis. The differential hematology analyzers may help in providing an accurate and precise evaluation and enumeration of the presence of reactive lymphocytes, which play a critical role in management of COVID-19 patients.
The global hematology analyzers and reagents market is expected to reach USD 9.6 billion by 2023 from USD 7.2 billion in 2019, at a CAGR of 7.4 percent, predicts MarketsandMarkets. The ability of a hematology test to effectively measure several blood components has made it an essential screening tool to diagnose a wide range of blood-related disorders, including anemia, blood cancer, hemorrhagic conditions, and blood infections, among others that affect millions of people each year across all age groups. Increasing incidences of hematology disorders and growing awareness related to the availability of a wide range of diagnostic options to treat these targeted disorders are the primary factors driving the growth of the hematology testing market.
Automation, being the primary focus of the hematology testing, the market has been witnessing considerable technological advancements with respect to products that offer effective, accurate, and fast testing results. The availability of automated hematology analyzers has further led to a reduced administrative error, making the process more effective and efficient in disease diagnosis, as compared to manual hematology testing. Furthermore, factors such as technological advancements in hematology analyzers and reagents and integration of flow cytometry techniques with hematology analyzers are expected to support the growth of this market during 2020-2023.
AI and hematology
The evidence-based literature on healthcare is currently expanding exponentially. The opportunities provided by the advancement in artificial intelligence (AI) tools such as machine learning (ML) are appealing in tackling many of the current healthcare challenges. As of 2020, radio-diagnostics is the field where AI has made the most progress to date; 90 percent of the practicing radiologists anticipate that AI will be incorporated in their future practices.
Recently, AI has also been employed in the analysis of hematopathology data for better-informed diagnosis, prognosis, and treatment planning. The foundation knowledge related to benign and malignant hematology is also evolving with the application of AI in hematology. Even the most skilled hematology specialist can overlook the patterns, deviations, and relations among the vast number of blood parameter measures by modern analyzers. In contrast, the AI algorithm can easily analyze hundreds of attributes (parameters) simultaneously with correlation, and recognize a pattern or fingerprint of certain hematological abnormalities.
The AI algorithms of modern hematology analyzers are trained on sufficiently large datasets of clinical cases that include laboratory blood tests, performed and confirmed by hematology specialist using various confirmatory tests.
The ML algorithms can uncover the disease-related patterns in a blood test that may be beyond medical knowledge, resulting in higher diagnostic accuracy as compared to traditional quantitative interpretation, based only over reference ranges. Also, it can lead to a fundamental change in differential diagnosis and result in the modifications in the presently accepted guidelines.
Currently, normal leukocytes can be differentiated, using automated hematology analyzers equipped with optical sensors and mathematical computer-based algorithms. However, because the morphology of dysplastic blood cells in patients with hematological disorders is much more elaborate than that of normal cells, manual microscopic examinations remain the mainstay of diagnosis, which are time-consuming, demanding, and subjective. Thus, many industrial and academic researchers have sought to develop efficient and accurate automated diagnostic systems. Current advances in computer technology have been used to derive automated diagnostic systems for leukemia.
Over the past decade, more than 20 studies have attempted to diagnose hematological malignancies, mainly acute lymphoblastic leukemia (ALL), using various mathematical algorithms to recognize and classify cell images. This process requires several complex steps like preprocessing, segmentation, feature extraction, and classification. Recently, convolutional neural networks (CNNs), advanced forms of deep learning, have been used to optimize the parameters automatically, without the need for mathematical algorithms. CNNs classify cell images more accurately than conventional neural networks or ML systems.
Researchers have successfully developed and tested a deep learning-based algorithm–CNN. This system is applied for differentiating myelodysplastic syndrome from aplastic anemia, with 96.2 percent of sensitivity and 100 percent specificity. This is the first such system for myelodysplastic syndrome (MDS), using peripheral blood smears; it can further be developed for automated diagnosis of various hematological abnormalities. This system recognizes over 100 patterns in cell size and cytoplasmic morphological features for detection and classification of myeloid malignant cells including MDS. Further training of this AI algorithm can improve the accuracy and reproducibility, which may surpass the human eye.
An understanding of AI’s basics will need to become a part of the physician’s statistical literacy in the very near future. As more widespread implementation of clinical AI nears, attention has also turned to the effects this will have on other areas in medicine. AI offers many promising tools to clinicians broadly, and specifically in the practice of hematology. Ongoing research into its various applications will likely result in an increasing utilization of AI by a broader swath of clinicians.
Today, the highest level of automation is scalable and configurable, starting from piercing the sample tubes to conducting variety of determinations, CBC, 6-part, WBC differential, NRBC, RET and immature retic fraction (IRF), automated immature platelet fraction (IPF), automated smear preparation, and staining. These tests are standardized assays that meet performance goals, decrease technologist hands-on time, eliminate batch testing, and provide results faster to physicians. Testing together with hematology-specific middleware product, laboratories are reporting up to 85 percent of their test volume without any operator intervention.
In addition to these, hematology automation platforms offer more than CBC testing from a single EDTA sample. Laboratories that have incorporated HbA1c testing on high-speed hematology lines are performing >90 percent of assays with minimal technologist intervention. Auto validation of HbA1c results can run as high as 90 percent. Further process improvements are coming to the forefront of hematology, such as automation of digital smear review. Now, these newly formed work areas can manage traditional hematology testing as well as the HbA1c, traditionally tested in the chemistry department.
For reagent preparation, hematology technologists frequently required to change 20-liter cubes of the diluent. Recent additions to automated hematology lines address this concern by including units that utilize concentrated reagent, which is diluted using an in-lab water supply. This approach minimizes the time and effort required to prep the analyzers prior to the highest volume run of the day, and enables laboratorians to avoid interruptions in testing owing to the need to frequently change the diluent in high-volume testing settings. Added to these innovations, the work in the direction of pre- and post-analytical sample sorting and archiving, effectively helps in minimizing errors in pre- and post-analytical work.
Other recent technological advances have led to the development and commercialization of innovative automated image analysis systems, which are suited for automation and can hence be directly connected (in series) with hematologic analyzers. These innovative platforms scan the slides (usually at a picture of ×100 objective), and store digitized images of blood smears at high magnification. The images are analyzed by artificial neural networks, based on a preexisting database of blood elements (thus including RBC), which can be locally customized or updated by the users. The images can be transmitted to, and displayed on, computer screens, which can be even placed at long distances from the scanner, for analysis and potential reclassification of blood elements. The operator can also increase the size of the images, or expand single sections of the scan, so obtaining a more accurate view. The operator can then accept and conserve the automatic classification or can move elements from one cell category to another, thus improving the final reclassification.
Although these automated image analysis systems have been originally developed for analysis of white blood cells (WBC), specific information can also be garnered on erythrocyte morphology, thus including the presence of anysocytosis, hypochromia, microcytosis or macrocytosis, spherocytosis, elliptocytosis, ovalocytosis, stomatocytosis, acanthocytosis, echinocytosis, polychromasia, poikilocytosis, and abnormal erythrocytes.
The recent data shows that diagnostic sensitivity of these systems for identifying some critical categories of abnormal erythrocytes is excellent, typically higher than 80 percent, thus making the use of digital image analysis a highly valuable, and probably more accurate and reproducible alternative to optical microscopy.
Notably, the use of these systems may also enable an efficient recognition of parasitoid infections like malaria, as well as the reliable identification of intravascular and spurious hemolysis, which would be otherwise undetectable on whole-blood specimens. Interestingly, most of these automated image analysis systems are also capable of optimizing the identification of rare RBC abnormalities, since morphological erythrocyte alterations can be more efficiently visualized on the computer screen. Finally, the creation of a large personalized database of images of suggestive RBC abnormalities represents a valuable resource for education and training of students and laboratory professionals.
Hematology analyzers are the real work horses of clinical laboratories. The advent of digital image-based cell counting, and application of AI will add more value to patient care, but microscopic examination by experienced personnel is still the gold standard in diagnosis of hematological abnormalities. The detection of abnormal cells on the basis of complex morphology, especially the size and shape of abnormal cells like immature white blood cells or diseased cells. It is important, though, to accurately detect the presence of immature white blood cells since they are often associated with conditions like leukemia, infection, inflammation, or tissue injury. Accordingly, manufacturers are working to decrease the number of manual reviews that are required, through improvements in analyzer accuracy in differential count.
Opportunities in these areas are enormous and this segment will surely go a long way in coming years.
Parameters play a significant role in diagnosis
Dr Preet Kaur
Business Unit Head,
Transasia Bio-Medicals Ltd.
Renowned scientific and clinical publications have now concluded that various biochemistry and hematology parameters play an essential role in the early detection, prognosis, and management of COVID-19.
Significant reduction in absolute lymphocyte count and hemoglobin, associated with the increase in total WBC count, absolute neutrophil count, ESR, prothrombin time, D-dimer, C-reactive protein, and pro-calcitonin. Additionally, various routine biochemical parameters also get deranged and signify the severity and poor prognosis. These lab findings, especially elevated ferritin levels, suggest an imminent cytokine storm, which might lead to complications like acute respiratory distress syndrome (ARDS), pneumonia, and multi-organ failure (MOD). Elevated troponins and CK-MB signify myocardial injury and subsequent fulminant myocarditis, which is associated with adverse outcome.
Important laboratory findings in COVID-19 patients
Neutrophil-to-lymphocyte ratio (NLR) predicts severe illness in patients at an early stage. NLR is a simple parameter to assess the inflammatory status of a patient by early identification of risk factors. In a study, it was identified that normal NLR values in an adult, non-geriatric population in good health are between 0.78 and 3.53. In another study, it was concluded that patients ≥50 years having NRL ≥3.13 are at risk of severe illness, and they should get rapid access to ICU, if necessary.
Dysregulation of Immune response: Novel coronavirus mainly acts on lymphocytes, especially T lymphocytes. Surveillance of NLR and lymphocyte subsets can be helpful in the early screening of critical illness, diagnosis and treatment of COVID-19. Dysregulation of immune response, especially T lymphocytes, is most likely to be involved in the patho-physiology of COVID-19 infection.
Hematologic microscopic parameters. Microscopic peripheral blood film review shows a higher number of patients, who are lymphopenic and have the presence of a few reactive lymphocytes, of which a subset appears to be lymphoplasmocytoid.
Transasia’s latest range of fully automated 3- and 5-part differential hematology analyzers help in providing an accurate and precise evaluation and enumeration of these parameters, which play a critical role in management of COVID-19 patients.
Dr Vandana Khare
Sr. Consultant Pathology,
Pushpawati Singhania Research Institute
One common test that physicians use to monitor patients is the complete blood count (CBC), and though the hematology analyzers are used essentially for blood cell counts and differential leukocyte counts but in addition, these analyzers are now reporting many other useful parameters that are helping the clinicians in screening and diagnosis of many diseases.
Majority of the advanced hematology analyzers are now capable of integrated parameters like reticulocyte hemoglobin (CHr), percentage of hypochromic cells, percentage of microcytic cells, and immature reticulocyte fraction.
CHr is a valuable parameter in assessing the functional iron available for erythropoiesis, and is a sensitive indicator of iron-deficient erythropoiesis. Similarly, the percentage of hypochromic cells is a sensitive indicator for the quantification of hemoglobinization of mature red cells. The value of the percentage of hypochromic red cells >10 percent of all RBCs is highly suggestive of iron-deficient anemia. Immature reticulocyte fraction or IRF, calculated as a ratio of immature reticulocytes to the total number of reticulocytes provides very early and sensitive index of marrow erythropoietic activity.The modern-day analyzers are providing five to seven parts differential white-cell analysis, based on technologies, such as, electrical impedance, light scatter, fluorescent scatter, radiofrequency conductivity, and cytochemistry. In addition, a few also provide information in the form of cell population data (CPD) and lymph index, granularity index, large unstained cell population and hemoparasites, useful in screening of benign and malignant hematological conditions. Similarly, the monocyte CPD has been shown to be useful for screening and differentiation between malaria and dengue infections.
With COVID-19 patients entering the hospitals, we are concerned about the safety of technicians handling the samples. Though. CBC is not used to diagnose COVID-19, but it is frequently done to assess the infected patient’s health.
Modern hematology analyzers are safe to use; they have the ability to pierce the tube of blood and automatically withdraw the tiny sample volume they need to perform the testing. The blood and reagent mixture is automatically rinsed from the machine before the next sample is tested, and at no time is the operator exposed to the blood sample directly.
The aspiration needle and analysis channels are protected from user interference, and at no time is the operator exposed to the blood sample directly. If we are taking necessary steps, providing them with recommended personal protective equipment (PPE) while taking a blood sample from the infected individual, labeling vials of drawn blood, and sending those vials to a lab outside, we will surely protect our staff who are on the front lines, fighting the coronavirus epidemic.
Expectations from modern-day hematology analyzer
Dr Kumud Kukreja
Supreme Hospital Lab
Hematology analyzers were preliminary designed to ease the task of pathologists as all blood counts including hemoglobin and various RbC indices would help to generate a quick and reliable report. With advancement in technology, this sector also saw a boom with more parameters being added, and this additional information certainly helped to reach a more precise diagnosis. The robust big analyzers now have been designed as more user-friendly and more informative so that the task of human intervention is minimized. With the emerging concept of the use of artificial intelligence, new parameters should be more rationally interpreted so that it can help in making the diagnosis.
Modern analyzers work on electrical impedance, radiofrequency conductivity, light scatter, and flow cytometry. An important parameter CHr has proven to be a sensitive indicator of iron-deficient erythropoiesis. Cell population data (CPD) for neutrophils is again a relevant indicator, which can be used as a screening tool for the diagnosis of sepsis in case of neutrophilia. CPD for lymphocytes also helps in the screening of viral, bacterial infections and lymphoid malignancies. Similarly, monocytes CPD has been shown to be useful for the screening and differentiation between malaria and dengue infections.
The immature platelet fraction is an important parameter, which can guide a clinician for a better management of dengue infection.
So, the need of the hour is to procure an analyzer that can give useful information related to all the blood cells–RBCs, WBCs, platelets, HB, and RBC indices–and is able to generate positive flags. In such a scenario, modern-day analyzer should aid substantially in the diagnosis of this potentially fatal disease.