Recognition of peripheral blood cell images using convolutional neural networks

2019 ◽  
Vol 180 ◽  
pp. 105020 ◽  
Author(s):  
Andrea Acevedo ◽  
Santiago Alférez ◽  
Anna Merino ◽  
Laura Puigví ◽  
José Rodellar
Keyword(s):  
Neural Networks ◽  
Blood Cell ◽  
Convolutional Neural Networks ◽  
Peripheral Blood ◽  
Peripheral Blood Cell

scholarly journals Peripheral Blood Cell Gene Expression Diagnostic for Identifying Symptomatic Transthyretin Amyloidosis Patients: Male and Female Specific Signatures

Theranostics ◽  
2016 ◽  
Vol 6 (11) ◽  
pp. 1792-1809 ◽  
Author(s):  
Sunil M. Kurian ◽  
Marta Novais ◽  
Thomas Whisenant ◽  
Terri Gelbart ◽  
Joel N. Buxbaum ◽  
...  
Keyword(s):  
Gene Expression ◽  
Blood Cell ◽  
Peripheral Blood ◽  
Peripheral Blood Cell ◽  
Transthyretin Amyloidosis ◽  
Male And Female ◽  
Cell Gene Expression ◽  
Female Specific ◽  
Cell Gene

scholarly journals The Association between Peripheral Blood Cells and the Frailty Syndrome in Patients with Cardiovascular Diseases

2020 ◽  
Vol 20 (9) ◽  
pp. 1419-1433
Author(s):  
Constantin Bodolea ◽  
Elisabeta I. Hiriscau ◽  
Elena-Cristina Buzdugan ◽  
Alin I. Grosu ◽  
Laurențiu Stoicescu ◽  
...  
Keyword(s):  
Cardiovascular Diseases ◽  
Blood Cell ◽  
Peripheral Blood ◽  
Blood Cells ◽  
Peripheral Blood Cell ◽  
Neutrophil To Lymphocyte Ratio ◽  
Frailty Syndrome ◽  
Distribution Width ◽  
Lymphocyte Ratio ◽  
Frail Patients

Background: Frailty syndrome is characterized by multisystem dysregulation frequently found in older individuals or even in younger patients with chronic disabling diseases such as cardiovascular diseases. Objective: To determine whether peripheral blood cell count, and its subpopulations, red blood cell and platelets, morphology and different ratios (neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio and red blood distribution width-to-platelet ratio) are associated with cardiac frail patients, and through this to improve the prediction of frailty status in patients with cardiovascular diseases. Methods: An observational, retrospective, cohort study enrolling 179 patients with cardiovascular disease divided into two groups: non-frail group (100 pts) and frail group (79 pts), a cohort detached from the Frail.RO study. The frailty was evaluated based on the Fried criteria; haematological markers, sociodemographic data, and variables related to cardiovascular diseases and comorbidities were also recorded. Results: Lower lymphocytes, platelet count, and neutrophil-to-lymphocyte ratio were significantly associated with a more severe frailty syndrome. Regarding red blood cells, haemoglobin concentration and red cell distribution width significantly correlated with the severity of the frailty syndrome. Receiver operating characteristic curve analysis for these markers associated with the frailty syndrome revealed an acceptable sensitivity of 66 % and specificity of 65% to identify frail individuals. Malnutrition and hypercholesterolemia are relevant predictors for identifying frailty in hospitalized cardiovascular patients. Conclusion: The evaluation of peripheral blood cell composition routinely measured in clinical practice can represent a valuable, but limited indicator, to diagnose frailty syndrome and eventually, the effects of interventions in frail patients with cardiovascular diseases.

scholarly journals Red blood cell classification in lensless single random phase encoding using convolutional neural networks

2020 ◽  
Vol 28 (22) ◽  
pp. 33504 ◽  
Author(s):  
Timothy O’Connor ◽  
Christopher Hawxhurst ◽  
Leslie M. Shor ◽  
Bahram Javidi
Keyword(s):  
Neural Networks ◽  
Blood Cell ◽  
Convolutional Neural Networks ◽  
Red Blood Cell ◽  
Random Phase ◽  
Cell Classification ◽  
Phase Encoding ◽  
Random Phase Encoding
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