Rapid screening of COVID‐19 patients using white blood cell scattergrams, a study on 381 patients

Osman, Jennifer; Lambert, Jérôme; Temple, M; Devaux, Floriane; Favre, Rémy; Flaujac, Claire; Bridoux, Delphine; Marque-Juillet, Stéphanie; Bruneel, Fabrice; Mignon, F; Díaz-Flores, Ernesto; Hentgen, Véronique; Greder-Belan, A.; Azarian, R.; Koukabi, M.; Rousselot, Philippe; Raggueneau, Victoria; Manéglier, Benjamin

doi:10.1111/bjh.16943

Cited by 21 publications

(14 citation statements)

References 14 publications

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“…This was only possible because SOM is oriented toward visualization of the relationships between the clusters and by its ability to generate knowledge, especially, in units of map 15, 20 and 25, where 83% of SARS-CoV-2-positive patients were correctly classified.In fact, it has already been reported that RDW is a significant predictor of severe illness in hospitalized COVID-19 patients (Lippi et al (2020); Henry et al (2020)). In line with our findings other group related that subtle differences in white blood cell pattern verified by complete blood count in routine test with differential fluorescence scattergrams can be used to rapid screening of COVID-19 patients (Osman et al (2020)). Moreover, corroborating our hypothesis to use admission blood test for support decision-making during COVID-19 discrimination, an encouraging work also demonstrated that hematological parameters may be used to assist the diagnosis of COVID-19 at emergency room (Usul et al (2020)).…”

Section: Conclusion and Further Worksupporting

confidence: 91%

“…In line with our findings other group related that subtle differences in white blood cell pattern verified by complete blood count in routine test with differential fluorescence scattergrams can be used to rapid screening of COVID-19 patients (Osman et al. ( 2020 )). Moreover, corroborating our hypothesis to use admission blood test for support decision-making during COVID-19 discrimination, an encouraging work also demonstrated that hematological parameters may be used to assist the diagnosis of COVID-19 at emergency room (Usul et al.…”

Section: Conclusion and Further Workmentioning

confidence: 99%

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Simple hemogram to support the decision-making of COVID-19 diagnosis using clusters analysis with self-organizing maps neural network

et al. 2021

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The pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) which is related to new coronavirus disease (COVID-19) has mobilized several scientifics to explore clinical data using soft-computing approaches. In the context of machine learning, previous studies have explored supervised algorithms to predict and support diagnosis based on several clinical parameters from patients diagnosed with and without COVID-19. However, in most of them the decision is based on a "black-box" method, making it impossible to discover the variable relevance in decision making. Hence, in this study, we introduce a non-supervised clustering analysis with neural network self-organizing maps (SOM) as a strategy of decision-making. We propose to identify potential variables in routine blood tests that can support clinician decision-making during COVID-19 diagnosis at hospital admission, facilitating rapid medical intervention. Based on SOM features (visual relationships between clusters and identification of patterns and behaviors), and using linear discriminant analysis , it was possible to detect a group of units of the map with a discrimination power around 83% to SARS-CoV-2-positive patients. In addition, we identified some variables in admission blood tests (Leukocytes, Basophils, Eosinophils, and Red cell Distribution Width) that, in combination had strong influence in the clustering performance, which could assist a possible clinical decision. Thus, although with limitations, we believe that SOM can be used as a soft-computing approach to support clinician decision-making in the context of COVID-19. Keywords Covid-19 diagnostic • SARS-CoV-2 • Self-organizing maps Communicated by Victor Hugo C. de Albuquerque.

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Section: Conclusion and Further Worksupporting

confidence: 91%

Section: Conclusion and Further Workmentioning

confidence: 99%

Simple hemogram to support the decision-making of COVID-19 diagnosis using clusters analysis with self-organizing maps neural network

et al. 2021

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“…Furthermore, a specific "hourglass" appearance on the WBC scattergram on Sysmex analysers, representative of lymphoplasmacytoid lymphocytes, was reported to have a high positive predictive value to detect COVID-19 (36).…”

Section: Discussionmentioning

confidence: 99%

A novel haemocytometric COVID-19 prognostic score developed and validated in an observational multicentre European hospital-based study

Linssen

Ermens

Berrevoets

et al. 2020

eLife

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COVID-19 induces haemocytometric changes. Complete blood count changes, including new cell activation parameters, from 982 confirmed COVID-19 adult patients from 11 European hospitals were retrospectively analysed for distinctive patterns based on age, gender, clinical severity, symptom duration and hospital days. The observed haemocytometric patterns formed the basis to develop a multi-haemocytometric-parameter prognostic score to predict, during the first three days after presentation, which patients will recover without ventilation or deteriorate within a two-week timeframe, needing intensive care or with fatal outcome. The prognostic score, with ROC curve AUC at baseline of 0.753 (95% CI 0.723-0.781) increasing to 0.875 (95% CI 0.806-0.926) on day 3, was superior to any individual parameter at distinguishing between clinical severity. Findings were confirmed in a validation cohort. Aim is that the score and haemocytometry results are simultaneously provided by analyser software, enabling wide applicability of the score as haemocytometry is commonly requested in COVID-19 patients.

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“…Using machine learning-based algorithms, COVID-19 patients were determined to have 92%–95% sensitivity and 92%–96% specificity. In Osman's study on the diagnosis of COVID-19 from scattergram images, COVID-19 was diagnosed with statistical tests [ 8 ]. The use of CT images for suspected patients with negative RT-PCR testing is now the standard.…”

Section: Discussionmentioning

confidence: 99%

“…In a pioneering study that considered whether the white cell distribution could be an indicator of COVID-19, Osman et al emphasized that the presence of plasmacytoid lymphocytes in the white blood cell distribution diagram indicates suspicion of COVID-19. The findings showed that COVID-19 patients had a sensitivity of 85.9% and a specificity of 83.5% [ 8 ]. Mitra et al observed that CBC can be associated with COVID-19 and observed changes in neutrophil cells.…”

Section: Introductionmentioning

confidence: 99%

Scat-NET: COVID-19 diagnosis with a CNN model using scattergram images

Tuncer

Ayyıldız

Kalaycı

et al. 2021

Computers in Biology and Medicine

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The acute respiratory syndrome COVID-19 disease, which is caused by SARS-CoV-2, has infected many people over a short time and caused the death of more than 2 million people. The gold standard in detecting COVID-19 is to apply the reverse transcription polymerase chain reaction (RT-PCR) test. This test has low sensitivity and produces false results of approximately 15%–20%. Computer tomography (CT) images were checked as a result of suspicious RT-PCR tests. If the virus is not infected in the lung, the virus is not observed on CT lung images. To overcome this problem, we propose a 25-depth convolutional neural network (CNN) model that uses scattergram images, which we call Scat-NET. Scattergram images are frequently used to reveal the numbers of neutrophils, eosinophils, basophils, lymphocytes and monocytes, which are measurements used in evaluating disease symptoms, and the relationships between them. To the best of our knowledge, using the CNN together with scattergram images in the detection of COVID-19 is the first study on this subject. Scattergram images obtained from 335 patients in total were classified using the Scat-NET architecture. The overall accuracy was 92.4%. The most striking finding in the results obtained was that COVID-19 patients with negative RT-PCR tests but positive CT test results were positive. As a result, we emphasize that the Scat-NET model will be an alternative to CT scans and could be applied as a secondary test for patients with negative RT-PCR tests.

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Rapid screening of COVID‐19 patients using white blood cell scattergrams, a study on 381 patients

Cited by 21 publications

References 14 publications

Simple hemogram to support the decision-making of COVID-19 diagnosis using clusters analysis with self-organizing maps neural network

Simple hemogram to support the decision-making of COVID-19 diagnosis using clusters analysis with self-organizing maps neural network

A novel haemocytometric COVID-19 prognostic score developed and validated in an observational multicentre European hospital-based study

Scat-NET: COVID-19 diagnosis with a CNN model using scattergram images

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