ArticleJournal of pathology informatics2018
A Method for the Interpretation of Flow Cytometry Data Using Genetic Algorithms.
Article in Journal of pathology informatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- How artificial intelligence might disrupt diagnostics in hematology in the near future.Oncogene · 2021Review
- Application of machine learning in the management of acute myeloid leukemia: current practice and future prospects.Blood advances · 2020Review
- Article
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Authors and funding
1 author.
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Abstract
backgroundFlow cytometry analysis is the method of choice for the differential diagnosis of hematologic disorders. It is typically performed by a trained hematopathologist through visual examination of bidimensional plots, making the analysis time-consuming and sometimes too subjective. Here, a pilot study applying genetic algorithms to flow cytometry data from normal and acute myeloid leukemia subjects is described. SUBJECTS AND
methodsInitially, Flow Cytometry Standard files from 316 normal and 43 acute myeloid leukemia subjects were transformed into multidimensional FITS image metafiles. Training was performed through introduction of FITS metafiles from 4 normal and 4 acute myeloid leukemia in the artificial intelligence system.
resultsTwo mathematical algorithms termed 018330 and 025886 were generated. When tested against a cohort of 312 normal and 39 acute myeloid leukemia subjects, both algorithms combined showed high discriminatory power with a receiver operating characteristic (ROC) curve of 0.912.
conclusionsThe present results suggest that machine learning systems hold a great promise in the interpretation of hematological flow cytometry data.
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