Comparison of three machine learning algorithms for classification of B‐cell neoplasms using clinical flow cytometry data

Multiparameter flow cytometry data is visually inspected by expert personnel as part of standard clinical disease diagnosis practice. This is a demanding and costly process, and recent research has demonstrated that it is possible to utilize artificial intelligence (AI) algorithms to assist in the i...

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Bibliographic Details
Published in:Cytometry. Part B, Clinical cytometry Vol. 106; no. 4; pp. 282 - 293
Main Authors: Dinalankara, Wikum, Ng, David P., Marchionni, Luigi, Simonson, Paul D.
Format: Journal Article
Language:English
Published: Hoboken, USA John Wiley & Sons, Inc 01.07.2024
Wiley Subscription Services, Inc
Subjects:
Accuracy
Algorithms
Artificial intelligence
B-Lymphocytes - classification
B-Lymphocytes - immunology
B-Lymphocytes - pathology
B‐cell neoplasms
Classification
deep learning
Flow cytometry
Flow Cytometry - methods
Humans
Immunophenotyping - methods
Learning algorithms
Lymphoma, B-Cell - classification
Lymphoma, B-Cell - diagnosis
Lymphoma, B-Cell - pathology
Machine Learning
Neoplasms
B‐cell neoplasms
deep learning
flow cytometry
machine learning
ISSN:1552-4949, 1552-4957, 1552-4957
Online Access:Get full text
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Summary:Multiparameter flow cytometry data is visually inspected by expert personnel as part of standard clinical disease diagnosis practice. This is a demanding and costly process, and recent research has demonstrated that it is possible to utilize artificial intelligence (AI) algorithms to assist in the interpretive process. Here we report our examination of three previously published machine learning methods for classification of flow cytometry data and apply these to a B‐cell neoplasm dataset to obtain predicted disease subtypes. Each of the examined methods classifies samples according to specific disease categories using ungated flow cytometry data. We compare and contrast the three algorithms with respect to their architectures, and we report the multiclass classification accuracies and relative required computation times. Despite different architectures, two of the methods, flowCat and EnsembleCNN, had similarly good accuracies with relatively fast computational times. We note a speed advantage for EnsembleCNN, particularly in the case of addition of training data and retraining of the classifier.
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ISSN:1552-4949
1552-4957
1552-4957
DOI:10.1002/cyto.b.22177