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Automated detection of immunofluorescently labeled cytomegalovirus‐infected cells in isolated peripheral blood leukocytes using decision tree analysis
Author(s) -
Ladanyi Andras,
Sher Andrew C.,
Herlitz Anna,
Bergsrud Danielle E.,
Kraeft StineKathrein,
Kepros Jane,
McDaid Gizelda,
Ferguson David,
Landry Marie L.,
Chen Lan Bo
Publication year - 2004
Publication title -
cytometry part a
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.316
H-Index - 90
eISSN - 1552-4930
pISSN - 1552-4922
DOI - 10.1002/cyto.a.20016
Subject(s) - decision tree , human cytomegalovirus , cytometry , peripheral blood , dipstick , cytomegalovirus , immunomagnetic separation , computer science , artificial intelligence , immunology , biology , flow cytometry , medicine , human immunodeficiency virus (hiv) , herpesviridae , viral disease , virus , microbiology and biotechnology , urine
Background Cytomegalovirus (CMV) infection continues to be a major problem for immunocompromised patients. Detection of viral antigens in leukocytes (antigenemia assay) is widely used for the diagnosis of CMV infection and for guiding antiviral therapy. The antigenemia technique, contingent upon the manual microscopic analysis of rare cells, is a laborious task that is subject to human error. In this study, we combine automated microscopy with artificial intelligence for reliable detection of fluorescently labeled CMV‐infected cells. Methods Cytospin preparations of peripheral blood leukocytes were immunofluorescently labeled for the CMV lower matrix phosphoprotein (pp65) and scanned in the Rare Event Imaging System (REIS), a fully automated image cytometer. The REIS detected potential positive objects and digitally recorded 49 measured cellular features for each identified case. The measurement data of these objects were analyzed by the See5 decision tree (DT) algorithm to ascertain whether they were true‐positive detections. Results The DT was built from the measurement data of 2,047 true‐ and 2,028 false‐positive detections, collected from 32 patient samples. By designating misclassifications of false‐negatives three times more costly, the 10‐fold cross‐validation sensitivity, specificity, and misclassification error of the assay was 94.3%, 56.2%, and 25%, respectively. The method was also validated using an independent test set of 21 patient samples, in which similar results were obtained. Conclusions To our knowledge, this study represents the first attempt to improve the accuracy of rare event image cytometry through the implementation of artificial intelligence methodology. Results suggest that cost‐sensitive decision tree analysis of digitally measured cellular features vastly improves the performance of rare event image cytometry. © 2004 Wiley‐Liss, Inc.