| TECHNICAL NOTE |
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| Year : 2018 | Volume
: 9
| Issue : 1 | Page : 16 |
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A method for the interpretation of flow cytometry data using genetic algorithms
Cesar Angeletti
Logical Cytometry, Atlanta GA, USA
Correspondence Address:
Dr. Cesar Angeletti
Logical Cytometry, 3324 Peachtree Rd #1408, Atlanta, GA 30326
USA
 Source of Support: None, Conflict of Interest: None  | Check |
DOI: 10.4103/jpi.jpi_76_17
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Background: Flow 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 Methods: Initially, 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. Results: Two 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. Conclusions: The present results suggest that machine learning systems hold a great promise in the interpretation of hematological flow cytometry data.
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