Navigation überspringen
Universitätsbibliothek Heidelberg
Status: Bibliographieeintrag

Verfügbarkeit
Standort: ---
Exemplare: ---
heiBIB
 Online-Ressource
Verfasst von:Elfert, Eike [VerfasserIn]   i
 Kaminski, Wolfgang E. [VerfasserIn]   i
 Matek, Christian [VerfasserIn]   i
 Hoermann, Gregor [VerfasserIn]   i
 Axelsen, Eyvind W. [VerfasserIn]   i
 Marr, Carsten [VerfasserIn]   i
 Piehler, Armin [VerfasserIn]   i
Titel:Expert-level detection of M-proteins in serum protein electrophoresis using machine learning
Verf.angabe:Eike Elfert, Wolfgang E. Kaminski, Christian Matek, Gregor Hoermann, Eyvind W. Axelsen, Carsten Marr, Armin P. Piehler
E-Jahr:2024
Jahr:17. Juni 2024
Umfang:9 S.
Fussnoten:Gesehen am 25.11.2024
Titel Quelle:Enthalten in: Clinical chemistry and laboratory medicine
Ort Quelle:Berlin [u.a.] : De Gruyter, 1998
Jahr Quelle:2024
Band/Heft Quelle:62(2024), 12, Seite 2498-2506
ISSN Quelle:1437-4331
Abstract: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.
DOI:doi:10.1515/cclm-2024-0222
URL:Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.

Volltext: https://doi.org/10.1515/cclm-2024-0222
 Volltext: http://www.degruyter.com/document/doi/10.1515/cclm-2024-0222/html?srsltid=AfmBOopfwq9P37mcbHUoMZvFP1NKZrdVzJfe4QIUjoTwbh ...
 DOI: https://doi.org/10.1515/cclm-2024-0222
Datenträger:Online-Ressource
Sprache:eng
Sach-SW:artificial intelligence
 electrophoresis
 machine learning
 monoclonal gammopathy
 myeloma
K10plus-PPN:190946385X
Verknüpfungen:→ Zeitschrift

Permanenter Link auf diesen Titel (bookmarkfähig):  https://katalog.ub.uni-heidelberg.de/titel/69276367   QR-Code
zum Seitenanfang