Do We Know Why We Make Errors in Morphological Diagnosis? An Analysis of Approach and Decision-Making in Haematological Morphology

Performance of the new MC-80 automated digital cell morphology analyser in detection of normal and abnormal blood cells: Comparison with the CellaVision DM9600

INTRODUCTION

Mindray MC-80 is an automated system for digital imaging of white blood cells (WBCs) and their pre-classification. The objective of this work is to analyse its performance comparing it with the CellaVision® DM9600.

METHODS

A total of 445 samples were used, 194 normal and 251 abnormal: acute leukaemia (100), myelodysplastic syndromes/myeloproliferative neoplasms (33), lymphoid neoplasms (50), plasma cell neoplasms (14), infections (49) and thrombocytopenia (5). WBC pre-classification values with the MC-80 and DM9600 were compared with (1) the microscope, (2) Mindray BC-6800Plus differentials in only normal samples, and (3) confirmed or reclassified images (post-classification). Pearson's correlation, Lin's concordance, Passing-Bablok regression, and Bland-Altman plots were used. Sensitivity, specificity, positive (PPV) and negative (NPV) predictive values for abnormal cells using the MC-80 were calculated.

RESULTS

The PPV and NPV were above 98% and 99%, for normal samples. For immature granulocytes (IG), NPV and PPV were 100% and 74.2%. When comparing the WBC differentials using the MC-80, the microscope and the BC-6800Plus, no differences were found except for basophils and IG. Our results showed good agreement between the pre- and post-classification of normal WBC, including IG, quantified by high correlation and concordance values (0.91-1). Sensitivity and specificity for blasts were 0.984 and 0.640. The MC-80 detected abnormal lymphocytes in 30% of the smears from patients with lymphoid neoplasm. Plasma cell identification was better using the DM9600. The sensitivity and specificity for erythroblast detection were 1 and 0.890.

CONCLUSION

We found that the MC-80 shows high performance for WBC differentials for both normal samples and patients with haematological diseases.

An overview of image-based phenotyping as an adaptive 4.0 technology for studying plant abiotic stress: A bibliometric and literature review

Improving the tolerance of crop species to abiotic stresses that limit plant growth and productivity is essential for mitigating the emerging problems of global warming. In this context, imaged data analysis represents an effective method in the 4.0 technology era, where this method has the non-destructive and recursive characterization of plant phenotypic traits as selection criteria. So, the plant breeders are helped in the development of adapted and climate-resilient crop varieties. Although image-based phenotyping has recently resulted in remarkable improvements for identifying the crop status under a range of growing conditions, the topic of its application for assessing the plant behavioral responses to abiotic stressors has not yet been extensively reviewed. For such a purpose, bibliometric analysis is an ideal analytical concept to analyze the evolution and interplay of image-based phenotyping to abiotic stresses by objectively reviewing the literature in light of existing database. Bibliometricy, a bibliometric analysis was applied using a systematic methodology which involved data mining, mining data improvement and analysis, and manuscript construction. The obtained results indicate that there are 554 documents related to image-based phenotyping to abiotic stress until 5 January 2023. All document showed the future development trends of image-based phenotyping will be mainly centered in the United States, European continent and China. The keywords analysis major focus to the application of 4.0 technology and machine learning in plant breeding, especially to create the tolerant variety under abiotic stresses. Drought and saline become an abiotic stress often using image-based phenotyping. Besides that, the rice, wheat and maize as the main commodities in this topic. In conclusion, the present work provides information on resolutive interactions in developing image-based phenotyping to abiotic stress, especially optimizing high-throughput sensors in image-based phenotyping for the future development.

White blood cell differentials performance of a new automated digital cell morphology analyzer: Mindray MC-80

INTRODUCTION

The manual differential count has been recognized for its disadvantages, including large interobserver variability and labor intensiveness. In this light, automated digital cell morphology analyzers have been increasingly adopted in hematology laboratories for their robustness and convenience. This study aims to evaluate the white blood cell differential performance of the Mindray MC-80, the new automated digital cell morphology analyzer.

METHODS

The cell identification performance of Mindray MC-80 was evaluated for sensitivity and specificity using pre-classification and post-classification of each cell class. The method comparison study used manual differentials as the gold standard for calculating Pearson correlation, Passing-Bablok regression, and Bland-Altman analysis. In addition, the precision study was performed and evaluated.

RESULTS

The precision was within the acceptable limit for all cell classes. Overall, the specificity of cell identification was higher than 95% for all cell classes. The sensitivity was greater for 95% for most cell classes, except for myelocytes (94.9%), metamyelocytes (90.9%), reactive lymphocytes (89.7%), and plasma cells (60%). Pre-classification and post-classification results correlated well with the manual differential results for all the cell types investigated. The regression coefficients were greater than 0.9 for most cell classes except for promyelocytes, metamyelocytes, basophils, and reactive lymphocytes.

CONCLUSION

The performance of Mindray MC-80 for white blood cell differentials is reliable and seems to be acceptable even in abnormal samples. However, the sensitivity is less than 95% for certain abnormal cell types, so the user should be aware of this limitation where such cells are suspected.

Explainable CAD System for Classification of Acute Lymphoblastic Leukemia Based on a Robust White Blood Cell Segmentation

Leukemia is a significant health challenge, with high incidence and mortality rates. Computer-aided diagnosis (CAD) has emerged as a promising approach. However, deep-learning methods suffer from the "black box problem", leading to unreliable diagnoses. This research proposes an Explainable AI (XAI) Leukemia classification method that addresses this issue by incorporating a robust White Blood Cell (WBC) nuclei segmentation as a hard attention mechanism. The segmentation of WBC is achieved by combining image processing and U-Net techniques, resulting in improved overall performance. The segmented images are fed into modified ResNet-50 models, where the MLP classifier, activation functions, and training scheme have been tested for leukemia subtype classification. Additionally, we add visual explainability and feature space analysis techniques to offer an interpretable classification. Our segmentation algorithm achieves an Intersection over Union (IoU) of 0.91, in six databases. Furthermore, the deep-learning classifier achieves an accuracy of 99.9% on testing. The Grad CAM methods and clustering space analysis confirm improved network focus when classifying segmented images compared to non-segmented images. Overall, the proposed visual explainable CAD system has the potential to assist physicians in diagnosing leukemia and improving patient outcomes.

Retracted: White blood cell differentials performance of a new automated digital cell morphology analyzer: Mindray MC-80

White blood cell differentials performance of a new automated digital cell morphology analyzer: Mindray MC-80, K. Paisooksantivatana; N. Khongjaroensakun; P. Chinudomwong; N. Chaothai; L. Chamchomdao; K. Suriyachand, International Journal of Laboratory Hematology, 10.1111/ijlh.13995 The above article, published online on 18 November 2022, in Wiley Online Library (wileyonlinelibrary.com), had been retracted by agreement between the authors, the journal's Editors-in-Chief, Giuseppe D'Onofrio and Ian Mackie, and John Wiley & Sons. The authors contacted the journal after publication to propose extensive changes to the data presented in the accepted article such that it would no longer reflect the version that was peer reviewed. As a result, this retraction has been undertaken.

Raman microspectroscopy identifies fibrotic tissues in collagen-related disorders via deconvoluted collagen type I spectra

Fibrosis is a consequence of the pathological remodeling of extracellular matrix (ECM) structures in the connective tissue of an organ. It is often caused by chronic inflammation, which over time, progressively leads to an excess deposition of collagen type I (COL I) that replaces healthy tissue structures, in many cases leaving a stiff scar. Increasing fibrosis can lead to organ failure and death; therefore, developing methods that potentially allow real-time monitoring of early onset or progression of fibrosis are highly valuable. In this study, the ECM structures of diseased and healthy human tissue from multiple organs were investigated for the presence of fibrosis using routine histology and marker-independent Raman microspectroscopy and Raman imaging. Spectral deconvolution of COL I Raman spectra allowed the discrimination of fibrotic and non-fibrotic COL I fibers. Statistically significant differences were identified in the amide I region of the spectral subpeak at 1608 cm^-1, which was deemed to be representative for structural changes in COL I fibers in all examined fibrotic tissues. Raman spectroscopy-based methods in combination with this newly discovered spectroscopic biomarker potentially offer a diagnostic approach to non-invasively track and monitor the progression of fibrosis. STATEMENT OF SIGNIFICANCE: Current diagnosis of fibrosis still relies on histopathological examination with invasive biopsy procedures. Although, several non-invasive imaging techniques such as positron emission tomography, single-photon emission computed tomography and second harmonic generation are gradually employed in preclinical or clinical studies, these techniques are limited in spatial resolution and the morphological interpretation highly relies on individual experience and knowledge. In this study, we propose a non-destructive technique, Raman microspectroscopy, to discriminate fibrotic changes of collagen type I based on a molecular biomarker. The changes of the secondary structure of collagen type I can be identified by spectral deconvolution, which potentially can provide an automatic diagnosis for fibrotic tissues in the clinical applicaion.

A Physician-in-the-Loop Approach by Means of Machine Learning for the Diagnosis of Lymphocytosis in the Clinical Laboratory

CONTEXT.—

The goal of the lymphocytosis diagnosis approach is its classification into benign or neoplastic categories. Nevertheless, a nonnegligible percentage of laboratories fail in that classification.

OBJECTIVE.—

To design and develop a machine learning model by using objective data from the DxH 800 analyzer, including cell population data, leukocyte and absolute lymphoid counts, hemoglobin concentration, and platelet counts, besides age and sex, with classification purposes for lymphocytosis diagnosis.

DESIGN.—

A total of 1565 samples were included from 10 different lymphoid categories grouped into 4 diagnostic categories: normal controls (458), benign causes of lymphocytosis (567), neoplastic lymphocytosis (399), and spurious causes of lymphocytosis (141). The data set was distributed in a 60-20-20 scheme for training, testing, and validation stages. Six machine learning models were built and compared, and the selection of the final model was based on the minimum generalization error and 10-fold cross validation accuracy.

RESULTS.—

The selected neural network classifier rendered a global 10-class classification validation accuracy corresponding to 89.9%, which, considering the aforementioned 4 diagnostic categories, presented a diagnostic impact accuracy corresponding to 95.8%. Finally, a prospective proof of concept was performed with 100 new cases with a global diagnostic accuracy corresponding to 91%.

CONCLUSIONS.—

The proposed machine learning model was feasible, with a high benefit-cost ratio, as the results were obtained within the complete blood count with differential. Finally, the diagnostic impact with high accuracies in both model validation and proof of concept encourages exploration of the model for real-world application on a daily basis.

The Use and Effectiveness of an Online Diagnostic Support System for Blood Film Interpretation: Comparative Observational Study

Background

The recognition and interpretation of abnormal blood cell morphology is often the first step in diagnosing underlying serious systemic illness or leukemia. Supporting the staff who interpret blood film morphology is therefore essential for a safe laboratory service. This paper describes an open-access, web-based decision support tool, developed by the authors to support morphological diagnosis, arising from earlier studies identifying mechanisms of error in blood film reporting. The effectiveness of this intervention was assessed using the unique resource offered by the online digital morphology Continuing Professional Development scheme (DM scheme) offered by the UK National External Quality Assessment Service for Haematology, with more than 3000 registered users. This allowed the effectiveness of decision support to be tested within a defined user group, each of whom viewed and interpreted the morphology of identical digital blood films.

Objective

The primary objective of the study was to test the effectiveness of the decision support system in supporting users to identify and interpret abnormal morphological features. The secondary objective was to determine the pattern and frequency of use of the system for different case types, and to determine how users perceived the support in terms of their confidence in decision-making.

Methods

This was a comparative study of identical blood films evaluated either with or without decision support. Selected earlier cases from the DM scheme were rereleased as new cases but with decision support made available; this allowed a comparison of data sets for identical cases with or without decision support. To address the primary objectives, the study used quantitative evaluation and statistical comparisons of the identification and interpretation of morphological features between the two different case releases. To address the secondary objective, the use of decision support was assessed using web analytical tools, while a questionnaire was used to assess user perceptions of the system.

Results

Cases evaluated with the aid of decision support had significantly improved accuracy of identification for relevant morphological features (mean improvement 9.8%) and the interpretation of those features (mean improvement 11%). The improvement was particularly significant for cases with higher complexity or for rarer diagnoses. Analysis of website usage demonstrated a high frequency of access for web pages relevant to each case (mean 9298 for each case, range 2661-24,276). Users reported that the decision support website increased their confidence for feature identification (4.8/5) and interpretation (4.3/5), both within the context of training (4.6/5) and also in their wider laboratory practice (4.4/5).

Conclusions

The findings of this study demonstrate that directed online decision support for blood morphology evaluation improves accuracy and confidence in the context of educational evaluation of digital films, with effectiveness potentially extending to wider laboratory use.

High-throughput multiparametric imaging flow cytometry: toward diffraction-limited sub-cellular detection and monitoring of sub-cellular processes

We present a sheathless, microfluidic imaging flow cytometer that incorporates stroboscopic illumination for blur-free fluorescence detection at ultra-high analytical throughput. The imaging platform is capable of multiparametric fluorescence quantification and sub-cellular localization of these structures down to 500 nm with microscopy image quality. We demonstrate the efficacy of the approach through the analysis and localization of P-bodies and stress granules in yeast and human cells using fluorescence and bright-field detection at analytical throughputs in excess of 60,000 and 400,000 cells/s, respectively. Results highlight the utility of our imaging flow cytometer in directly investigating phase-separated compartments within cellular environments and screening rare events at the sub-cellular level for a range of diagnostic applications.

Abnormal characteristic "round bottom flask" shape volume-based scattergram as a trigger to suspect persistent polyclonal B-cell lymphocytosis

BACKGROUND AND AIMS

The diagnosis of persistent polyclonal B-cell lymphocytosis (PPBL) is often challenging because of the lack of features and the overlap with the peripheral expression of splenic marginal zone lymphomas (SMZL). To obtain new clues for PPBL detection and diagnosis, all data provided by the DxH 800 analyzer (including scatter and cell population data (CPD)) was exploited and combined using a machine learning (ML) approach.

MATERIALS AND METHODS

A total 211 samples from 101 normal controls and 110 patients (PPBL and SMZL) were assessed. Age, gender, full blood count, CPD, scatter, flags and CellaVision differentials were also considered. A ML model was built for true classification purposes.

RESULTS

PPBL and SMZL shared increased absolute lymphoid counts, atypical lymphoid flag presence and CPD values (8 out of 14). A typical "round-bottom-flask" shape scattergram was described for the first time for PPBL which was also present in 51.4% of SMZL cases. The developed ML model render a global classification accuracy of 93.4%, allowing the detection of all pathological cases, with mean misclassification rates of 12% among PPBL and SMZL.

CONCLUSION

Our ML model represents a new unbiased tool than can be widely applied in the laboratory as an aid for detection of PPBL.

Laboratory practices for manual blood film review: Results of an IQMH patterns of practice survey

INTRODUCTION

Examination of a blood film is the second most common hematology test, after the complete blood count. Interpretation of a peripheral blood film by trained laboratory professionals provides valuable diagnostic information. The Institute for Quality Management in Healthcare (IQMH) Hematology Scientific Committee developed a questionnaire to gather information regarding current practices for manual blood film review and reporting from laboratories participating in IQMH Morphology proficiency testing (PT) surveys.

METHODS

An online survey was distributed to 174 laboratories, 97% submitted results.

RESULTS

Of the respondents, the majority (82%) indicated affiliation with small- or medium-sized hospitals (<500 beds). 80% of respondents had core laboratory technologists performing manual blood film reviews, while only 2% utilized dedicated hematology technologists with morphology expertise. All respondents had a policy for manual blood film review by a technologist, 70% did not have blood films reviewed by a senior/charge technologist prior to review by a physician. The majority (88%) of participants included morphological findings in their critical result list; of these, 98% include malaria and 88% include the first-time finding of blasts as critical results. 59% of participants indicated that they have a procedure in place to ensure that interpretation and confirmation of first-time potentially significant morphological findings are available from a physician at all times.

CONCLUSION

This survey identified significant variation in blood film review and reporting practices across participating laboratories. The IQMH Hematology Scientific Committee will develop best practice recommendations to guide and standardize practice.

Peripheral blood morphology review and diagnostic proficiency evaluation by a new Spanish EQAS during the period 2011-2019

INTRODUCTION

The Spanish Haematology and Haemotherapy Society organizes peripheral blood smear review scheme, focused on the evaluation of diagnostic proficiency of participants by blood cell morphology analysis. The objective was to evaluate the efficacy of this scheme as an educational tool to improve the diagnostic proficiency of the participants.

METHODS

During 2011-2019, 54 peripheral blood smears, alongside with patient details such as age, sex, blood cell counts and relevant clinical information, were sent to an average of 125 ± 13 laboratories per year. A number of 44 shipments were selected to analyse whether successive surveys of the same disease may lead to an improvement in the diagnostic success rate proposed by the laboratories. Participants were asked to select the most relevant morphological abnormalities, alongside the diagnostic orientation. Agreement of participant responses with RR was evaluated.

RESULTS

Spanish laboratories showed a diagnostic proficiency greater than 80% in acute myeloid leukaemia, including acute promyelocytic leukaemia, mature B-cell neoplasms (hairy cell leukaemia and splenic marginal zone lymphoma), chronic myeloid leukaemia, sickle cell disease, Bernard-Soulier syndrome and infectious mononucleosis. It was important to note the significant improvement over the time in the successive shipments of the same disease, with a 31% and 13% increase in their diagnostic orientation success rate for acute myeloid leukaemia and acute promyelocytic leukaemia cases, respectively, 15% for mantle cell lymphoma and 6% for sickle cell disease.

CONCLUSIONS

The present study provides evidence that peripheral blood smear review scheme can be a valid educational tool to improve the clinical pathologist skills in blood morphology and haematological diagnosis.

Expert knowledge for the recognition of leukemic cells

This work shows the advantage of expert knowledge for leukemic cell recognition. In the medical area, visual analysis of microscopic images has regularly used biological samples to recognize hematological disorders. Nowadays, techniques of image recognition are needed to achieve an adequate identification of blood tissues. This paper presents a procedure to acquire expert knowledge from blood cell images. We apply Gaussian mixtures, evolutionary computing, and standard techniques of image processing to extract knowledge. This information feeds a support vector machine or multilayer perceptron to classify healthy or leukemic cells. Additionally, convolutional neural networks are used as a benchmark to compare our proposed method with the state of the art. We use a public database of 260 healthy and leukemic cell images. Results show that our traditional pattern recognition methodology matches deep learning accuracy since the recognition of blood cells achieves 99.63%, whereas the convolutional neural networks reach 97.74% on average. Moreover, the computational effort of our approach is minimal, while meeting the requirement of being explainable.

Web-Based Virtual Microscopy of Digitized Blood Slides for Malaria Diagnosis: An Effective Tool for Skills Assessment in Different Countries and Environments

Background

Morphological examination of blood films remains the reference standard for malaria diagnosis. Supporting the skills required to make an accurate morphological diagnosis is therefore essential. However, providing support across different countries and environments is a substantial challenge.

Objective

This paper reports a scheme supplying digital slides of malaria-infected blood within an Internet-based virtual microscope environment to users with different access to training and computing facilities. The feasibility of the approach was established, allowing users to test, record, and compare their own performance with that of other users.

Methods

From Giemsa stained thick and thin blood films, 56 large high-resolution digital slides were prepared, using high-quality image capture and 63x oil-immersion objective lens. The individual images were combined using the photomerge function of Adobe Photoshop and then adjusted to ensure resolution and reproduction of essential diagnostic features. Web delivery employed the Digital Slidebox platform allowing digital microscope viewing facilities and image annotation with data gathering from participants.

Results

Engagement was high with images viewed by 38 participants in five countries in a range of environments and a mean completion rate of 42/56 cases. The rate of parasite detection was 78% and accuracy of species identification was 53%, which was comparable with results of similar studies using glass slides. Data collection allowed users to compare performance with other users over time or for each individual case.

Conclusions

Overall, these results demonstrate that users worldwide can effectively engage with the system in a range of environments, with the potential to enhance personal performance through education, external quality assessment, and personal professional development, especially in regions where educational resources are difficult to access.