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Elfert, E.* ; Kaminski, W.E.* ; Matek, C. ; Hoermann, G.* ; Axelsen, E.W.* ; Marr, C. ; Piehler, A.P.*

Expert-level detection of M-proteins in serum protein electrophoresis using machine learning.

Clin. Chem. Lab. Med., DOI: 10.1515/cclm-2024-0222 (2024)
Postprint Research data DOI PMC
Open Access Green
OBJECTIVES: Serum protein electrophoresis (SPE) in combination with immunotyping (IMT) is the diagnostic standard for detecting monoclonal proteins (M-proteins). However, interpretation of SPE and IMT is weakly standardized, time consuming and investigator dependent. Here, we present five machine learning (ML) approaches for automated detection of M-proteins on SPE on an unprecedented large and well-curated data set and compare the performance with that of laboratory experts. METHODS: SPE and IMT were performed in serum samples from 69,722 individuals from Norway. IMT results were used to label the samples as M-protein present (positive, n=4,273) or absent (negative n=65,449). Four feature-based ML algorithms and one convolutional neural network (CNN) were trained on 68,722 randomly selected SPE patterns to detect M-proteins. Algorithm performance was compared to that of an expert group of clinical pathologists and laboratory technicians (n=10) on a test set of 1,000 samples. RESULTS: The random forest classifier showed the best performance (F1-Score 93.2 %, accuracy 99.1 %, sensitivity 89.9 %, specificity 99.8 %, positive predictive value 96.9 %, negative predictive value 99.3 %) and outperformed the experts (F1-Score 61.2 ± 16.0 %, accuracy 89.2 ± 10.2 %, sensitivity 94.3 ± 2.8 %, specificity 88.9 ± 10.9 %, positive predictive value 47.3 ± 16.2 %, negative predictive value 99.5 ± 0.2 %) on the test set. Interestingly the performance of the RFC saturated, the CNN performance increased steadily within our training set (n=68,722). CONCLUSIONS: Feature-based ML systems are capable of automated detection of M-proteins on SPE beyond expert-level and show potential for use in the clinical laboratory.
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Publication type Article: Journal article
Document type Scientific Article
Keywords Artificial Intelligence ; Electrophoresis ; Machine Learning ; Monoclonal Gammopathy ; Myeloma; Classification; Recognition; Algorithm
Language english
Publication Year 2024
HGF-reported in Year 2024
ISSN (print) / ISBN 1434-6621
e-ISSN 1437-4331
Journal Clinical Chemistry and Laboratory Medicine
Publisher de Gruyter
Publishing Place Genthiner Strasse 13, D-10785 Berlin, Germany
Reviewing status Peer reviewed
Institute(s) Institute of AI for Health (AIH)
POF-Topic(s) 30205 - Bioengineering and Digital Health
Research field(s) Enabling and Novel Technologies
PSP Element(s) G-540007-001
Grants European Research Council (ERC)
DOI 10.1515/cclm-2024-0222
WOS ID 001247055700001
Scopus ID 85196657792
PubMed ID 38879789
Erfassungsdatum 2024-07-19
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