Detection of intracellular parasites by use of the CellaVision DM96 analyzer during routine screening of peripheral blood smears

Digital pathology and artificial intelligence as the next chapter in diagnostic hematopathology

Digital pathology has a crucial role in diagnostic pathology and is increasingly a technological requirement in the field. Integration of digital slides into the pathology workflow, advanced algorithms, and computer-aided diagnostic techniques extend the frontiers of the pathologist's view beyond the microscopic slide and enable true integration of knowledge and expertise. There is clear potential for artificial intelligence (AI) breakthroughs in pathology and hematopathology. In this review article, we discuss the approach of using machine learning in the diagnosis, classification, and treatment guidelines of hematolymphoid disease, as well as recent progress of artificial intelligence in flow cytometric analysis of hematolymphoid diseases. We review these topics specifically through the potential clinical applications of CellaVision, an automated digital image analyzer of peripheral blood, and Morphogo, a novel artificial intelligence-based bone marrow analyzing system. Adoption of these new technologies will allow pathologists to streamline workflow and achieve faster turnaround time in diagnosing hematological disease.

Automatic identification of malaria and other red blood cell inclusions using convolutional neural networks

Malaria is a serious disease responsible for thousands of deaths each year. Many efforts have been made to aid in the diagnosis of malaria using machine learning techniques, but to date, the presence of other elements that may interfere with the recognition of malaria has not been considered. We have developed the first deep learning model using convolutional neural networks capable of differentiating malaria-infected red blood cells from not only normal erythrocytes but also erythrocytes with other types of inclusions. 6415 images of red blood cells were segmented from digital images of 53 peripheral blood smears using thresholding and watershed transformation techniques. These images were used to train a VGG-16 architecture using transfer learning. Using an independent test set of 23 smears, this model was 99.5% accurate in classifying malaria parasites and other red blood cell inclusions. This model also exhibited sensitivity and specificity values of 100% and 91.7%, respectively, classifying a complete smear as infected or not infected. Our model represents a promising advance for automation in the identification of malaria-infected patients. The differentiation between malaria parasites and other red blood cell inclusions demonstrates the potential utility of our model in a real work environment.

Machine learning and augmented human intelligence use in histomorphology for haematolymphoid disorders

Advances in digital pathology have allowed a number of opportunities such as decision support using artificial intelligence (AI). The application of AI to digital pathology data shows promise as an aid for pathologists in the diagnosis of haematological disorders. AI-based applications have embraced benign haematology, diagnosing leukaemia and lymphoma, as well as ancillary testing modalities including flow cytometry. In this review, we highlight the progress made to date in machine learning applications in haematopathology, summarise important studies in this field, and highlight key limitations. We further present our outlook on the future direction and trends for AI to support diagnostic decisions in haematopathology.

Deep Neural Networks Offer Morphologic Classification and Diagnosis of Bacterial Vaginosis

Bacterial vaginosis (BV) is caused by the excessive and imbalanced growth of bacteria in vagina, affecting 30 to 50% of women. Gram staining followed by Nugent scoring based on bacterial morphotypes under the microscope is considered the gold standard for BV diagnosis; this method is often labor-intensive and time-consuming, and results vary from person to person. We developed and optimized a convolutional neural network (CNN) model and evaluated its ability to automatically identify and classify three categories of Nugent scores from microscope images. The CNN model was first established with a panel of microscopic images with Nugent scores determined by experts. The model was trained by minimizing the cross-entropy loss function and optimized by using a momentum optimizer. The separate test sets of images collected from three hospitals were evaluated by the CNN model. The CNN model consisted of 25 convolutional layers, 2 pooling layers, and a fully connected layer. The model obtained 82.4% sensitivity and 96.6% specificity with the 5,815 validation images when altered vaginal flora and BV were considered the positive samples, which was better than the rates achieved by top-level technologists and obstetricians in China. The capability of our model for generalization was so strong that it exhibited 75.1% accuracy in three categories of Nugent scores on the independent test set of 1,082 images, which was 6.6% higher than the average of three technologists, who are hold bachelor's degrees in medicine and are qualified to make diagnostic decisions. When three technologists ran one specimen in triplicate, the precision of three categories of Nugent scores was 54.0%. One hundred three samples diagnosed by two technologists on different days showed a repeatability of 90.3%. The CNN model outperformed human health care practitioners in terms of accuracy and stability for three categories of Nugent score diagnosis. The deep learning model may offer translational applications in automating diagnosis of bacterial vaginosis with proper supporting hardware.

Performance evaluation of machine learning-based infectious screening flags on the HORIBA Medical Yumizen H550 Haematology Analyzer for vivax malaria and dengue fever

BACKGROUND

Automated detection of malaria and dengue infection has been actively researched for more than two decades. Although many improvements have been achieved, these solutions remain too expensive for most laboratories and clinics in developing countries. The low range HORIBA Medical Haematology Analyzer, Yumizen H550, now provides dedicated flags 'vivax malaria' and 'dengue fever' in routine blood testing, developed through machine learning methods, to be used as a screening tool for malaria and dengue fever in endemic areas. This study sought to evaluate the effectiveness of these flags under real clinical conditions.

METHODS

A total of 1420 samples were tested using the Yumizen H550 Haematology Analyzer, including 1339 samples from febrile patients among whom 202 were infected with malaria parasites (Plasmodium vivax only: 182, Plasmodium falciparum only: 18, both: 2), 210 were from febrile dengue infected patients, 3 were from afebrile dengue infected patients and 78 were samples from healthy controls, in an outpatient laboratory clinic in Mumbai, India. Microscopic examination was carried out as the confirmatory reference method for detection of malarial parasite, species identification and assessing parasitaemia based on different stages of parasite life cycle. Rapid diagnostic malarial antigen tests were used for additional confirmation. For dengue infection, NS1 antigen detection by ELISA was used as a diagnostic marker.

RESULTS

For the automated vivax malaria flag, the original manufacturer's cut off yielded a sensitivity and specificity of 65.2% and 98.9% respectively with the ROC AUC of 0.9. After optimization of cut-off value, flag performance improved to 72% for sensitivity and 97.9% specificity. Additionally it demonstrated a positive correlation with increasing levels of parasitaemia. For the automated dengue fever flag it yielded a ROC AUC of 0.82 with 79.3% sensitivity and 71.5% specificity.

CONCLUSIONS

The results demonstrate a possibility of the effective use of automated infectious flags for screening vivax malaria and dengue infection in a clinical setting.

Development of a quantitative, portable, and automated fluorescent blue-ray device-based malaria diagnostic equipment with an on-disc SiO2 nanofiber filter

There is an urgent need to develop an automated malaria diagnostic system that can easily and rapidly detect malaria parasites and determine the proportion of malaria-infected erythrocytes in the clinical blood samples. In this study, we developed a quantitative, mobile, and fully automated malaria diagnostic system equipped with an on-disc SiO₂ nanofiber filter and blue-ray devices. The filter removes the leukocytes and platelets from the blood samples, which interfere with the accurate detection of malaria by the blue-ray devices. We confirmed that the filter, which can be operated automatically by centrifugal force due to the rotation of the disc, achieved a high removal rate of leukocytes (99.7%) and platelets (90.2%) in just 30 s. The automated system exhibited a higher sensitivity (100%) and specificity (92.8%) for detecting Plasmodium falciparum from the blood of 274 asymptomatic individuals in Kenya when compared to the common rapid diagnosis test (sensitivity = 98.1% and specificity = 54.8%). This indicated that this system can be a potential alternative to conventional methods used at local health facilities, which lack basic infrastructure.

Artificial Intelligence and Digital Microscopy Applications in Diagnostic Hematopathology

Digital Pathology is the process of converting histology glass slides to digital images using sophisticated computerized technology to facilitate acquisition, evaluation, storage, and portability of histologic information. By its nature, digitization of analog histology data renders it amenable to analysis using deep learning/artificial intelligence (DL/AI) techniques. The application of DL/AI to digital pathology data holds promise, even if the scope of use cases and regulatory framework for deploying such applications in the clinical environment remains in the early stages. Recent studies using whole-slide images and DL/AI to detect histologic abnormalities in general and cancer in particular have shown encouraging results. In this review, we focus on these emerging technologies intended for use in diagnostic hematology and the evaluation of lymphoproliferative diseases.

Sequential classification system for recognition of malaria infection using peripheral blood cell images

Aims

Morphological recognition of red blood cells infected with malaria parasites is an important task in the laboratory practice. Nowadays, there is a lack of specific automated systems able to differentiate malaria with respect to other red blood cell inclusions. This study aims to develop a machine learning approach able to discriminate parasitised erythrocytes not only from normal, but also from other erythrocyte inclusions, such as Howell-Jolly and Pappenheimer bodies, basophilic stippling as well as platelets overlying red blood cells.

Methods

A total of 15 660 erythrocyte images from 87 smears were segmented using histogram thresholding and watershed techniques, which allowed the extraction of 2852 colour and texture features. Dataset was split into a training and assessment sets. Training set was used to develop the whole system, in which several classification approaches were compared with obtain the most accurate recognition. Afterwards, the recognition system was evaluated with the assessment set, performing two steps: (1) classifying each individual cell image to assess the system’s recognition ability and (2) analysing whole smears to obtain a malaria infection diagnosis.

Results

The selection of the best classification approach resulted in a final sequential system with an accuracy of 97.7% for the six groups of red blood cell inclusions. The ability of the system to detect patients infected with malaria showed a sensitivity and specificity of 100% and 90%, respectively.

Conclusions

The proposed method achieves a high diagnostic performance in the recognition of red blood cell infected with malaria, along with other frequent erythrocyte inclusions.

Diagnostic performance of CellaVision DM96 for Plasmodium vivax and Plasmodium falciparum screening in peripheral blood smears

Microscopic examination of blood smears is the standard method for malaria diagnosis but is labor-intensive and requires expert staff. CellaVision DM96 (CellaVision, Lund, Sweden) is a digital hematology analyzer available for advanced morphological analysis of blood films including intracellular parasites. Here, we evaluated the CellaVision DM96 Advanced RBC Application for malaria detection in stained peripheral blood (PB) smears. Two hundred and twenty thin PB smear slides (84 P. vivax, 14 P. falciparum, 122 negative controls) were stained with Wright-Giemsa using automated slidemaker/strainers of Beckman Coulter hematology systems (LH780, Beckman Coulter Inc., Miami, FL). The slides were automatically analyzed by CellaVision, and images were manually reviewed by experts. The results of automatic and manual detection by CellaVision were compared to those of microscopic examination. The sensitivity and specificity of automatic detection by CellaVision were 23.5% (23/98) and 81.1% (99/122), respectively. When CellaVision images were manually reviewed, the sensitivity and specificity increased to 65.3% (64/98) and 90.2% (110/122), respectively. The detection of P. falciparum showed the highest sensitivity by both the automated (33.3%) and the manual (85.7%) method. CellaVision misinterpreted malaria parasites as Howell-Jolly bodies in 57.1%, as Pappenheimer bodies in 84.7%, and as basophilic stipplings in 75.5% of the slides. Malaria diagnosis using CellaVision DM96 requires further improvements. Manual review improves CellaVision performance, but confirmation by conventional microscopy remains essential.

Review of Telemicrobiology

CONTEXT

Microbiology laboratories are continually pursuing means to improve quality, rapidity, and efficiency of specimen analysis in the face of limited resources. One means by which to achieve these improvements is through the remote analysis of digital images. Telemicrobiology enables the remote interpretation of images of microbiology specimens. To date, the practice of clinical telemicrobiology has not been thoroughly reviewed.

OBJECTIVE

To identify the various methods that can be employed for telemicrobiology, including emerging technologies that may provide value to the clinical laboratory.

DATA SOURCES

Peer-reviewed literature, conference proceedings, meeting presentations, and expert opinions pertaining to telemicrobiology have been evaluated.

CONCLUSIONS

A number of modalities have been employed for telemicroscopy, including static capture techniques, whole slide imaging, video telemicroscopy, mobile devices, and hybrid systems. Telemicrobiology has been successfully implemented for several applications, including routine primary diagnosis, expert teleconsultation, and proficiency testing. Emerging areas of telemicrobiology include digital plate reading of bacterial cultures, mobile health applications, and computer-augmented analysis of digital images. To date, static image capture techniques have been the most widely used modality for telemicrobiology, despite newer technologies being available that may produce better quality interpretations. Telemicrobiology adds value, quality, and efficiency to the clinical microbiology laboratory, and increased adoption of telemicrobiology is anticipated.