首页|New Findings from Mayo Clinic in the Area of Machine Learning Reported (Computat ional Flow Cytometry Accurately Identifies Sezary Cells Based On Simplified Aber rancy and Clonality Features)
New Findings from Mayo Clinic in the Area of Machine Learning Reported (Computat ional Flow Cytometry Accurately Identifies Sezary Cells Based On Simplified Aber rancy and Clonality Features)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Data detailed on Machine Learning have been presented. According to news reporting originating in Rochester, Minnesota , by NewsRx journalists, research stated, “Flow cytometric identification of cir culating neoplastic cells (Sezary cells) in patients with mycosis fungoides and Sezary syndrome is essential for diagnosis, staging, and prognosis. Although rec ent advances have improved the performance of this laboratory assay, the complex immunophenotype of Sezary cells and overlap with reactive T cells demand a high level of analytic expertise.” The news reporters obtained a quote from the research from Mayo Clinic, “We util ized machine learning to simplify this analysis using only 2 predefined Sezary c ell- gating plots. We studied 114 samples from 59 patients with Sezary syndrome/ mycosis fungoides and 66 samples from unique patients with inflammatory dermatos es. A single dimensionality reduction plot highlighted all TCR constant b chainrestricted (clonal) CD3+/CD4+ + /CD4 + T cells detected by expert analysis. On receiver operator curve analysis, an aberrancy scale feature computed by compari son with controls (area under the curve = 0.98) outperformed loss of CD2 (0.76), CD3 (0.83), CD7 (0.77), and CD26 (0.82) in discriminating Sezary cells from rea ctive CD4+ + T cells. Our results closely mirrored those obtained by exhaustive expert analysis for event classification (positive percentage agreement = 100% , negative percentage agreement = 99%) and Sezary cell quantitation (regression slope = 1.003, R squared = 0.9996).”
RochesterMinnesotaUnited StatesNor th and Central AmericaCyborgsCytometryEmerging TechnologiesHealth and Me dicineMachine LearningMayo Clinic
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