首页|Data on Machine Learning Reported by Yaw O. N. Ansong- Ansongton and Colleagues (Computing Sickle Erythrocyte Health Index Based on Quantitative Phase Imaging and Machine Learning)
Data on Machine Learning Reported by Yaw O. N. Ansong- Ansongton and Colleagues (Computing Sickle Erythrocyte Health Index Based on Quantitative Phase Imaging and Machine Learning)
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New research on Machine Learning is the subject of a report. According to news originating from New Haven, United States, by NewsRx correspondents, research stated, “Sickle cell disease (SCD) is a genetic disorder characterized by abnormal hemoglobin and deformation of red blood cells (RBCs), leading to complications and reduced life expectancy. This study developed an in-vitro assessment, the Sickle Erythrocyte Health Index, using quantitative phase imaging (QPI) and machine learning to model the health of RBCs in people with SCD.” Our news journalists obtained a quote from the research, “The Health Index combines assessment of cell deformation, sickle-shaped classification, and membrane flexibility to evaluate erythrocyte health. Using QPI and image processing, the percentage of sickle-shaped cells and membrane flexibility were quantified. Statistically significant differences were observed between individuals with and without SCD, indicating the impact of underlying pathophysiology on erythrocyte health. Additionally, sodium metabisulfite led to an increase in sickle-shaped cells and a decrease in flexibility for the sickle cell blood samples. Based on these findings, two approaches were used to calculate the Index: one using hand-crafted features and one using learned features from deep learning models. Both indices showed significant differences between non-SCD and SCD groups and sensitivity to changes induced by sodium metabisulfite. The Sickle Erythrocyte Health Index has important clinical implications for SCD management and could be used by providers when making treatment decisions.”
New HavenUnited StatesNorth and Central AmericaBlood CellsCell ResearchCyborgsEmerging TechnologiesErythrocytesErythroid CellsHealth and MedicineImmunologyMachine Learning
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