Tapio Lahtiharju, Lassi Paavolainen, Janne Suvisaari, Pasi Nokelainen, Emmi Rotgers, Mikko Anttonen, Outi Itkonen
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引用次数: 0
Abstract
Background and aims: Serum protein electrophoresis interpretation requires a substantial amount of manual work. In 2020, Chabrun et al. created a machine learning method called SPECTR for the task. We aimed to validate and test the SPECTR method against our results of more precise immunofixation electrophoresis.
Materials and methods: We gathered 34 625 patients and their first serum protein electrophoresis sample in Helsinki University Hospital. We trained three neural network models: (1) a fractionation model to fractionate electropherograms; (2) a classification model to classify samples to normal, ambiguous, and abnormal (i.e. containing paraprotein); (3) an integration model to predict concentration and location of paraproteins.
Results: The fractionation model demonstrated an error rate of ≤0.33 g/L in 95 % samples. The classification model achieved an area under the curve of 97 % in receiver operating characteristic analysis. The integration model demonstrated a coefficient of determination (R2) of 0.991 and a root-mean-square error of 1.37 g/L in linear regression.
Conclusion: The neural network models proved to be suitable for partial automation in serum protein electrophoresis reporting, i.e. classification of normal electropherograms. Furthermore, the models can accurately suggest the location and concentration of paraproteins.
期刊介绍：
The Official Journal of the International Federation of Clinical Chemistry and Laboratory Medicine (IFCC)
Clinica Chimica Acta is a high-quality journal which publishes original Research Communications in the field of clinical chemistry and laboratory medicine, defined as the diagnostic application of chemistry, biochemistry, immunochemistry, biochemical aspects of hematology, toxicology, and molecular biology to the study of human disease in body fluids and cells.
The objective of the journal is to publish novel information leading to a better understanding of biological mechanisms of human diseases, their prevention, diagnosis, and patient management. Reports of an applied clinical character are also welcome. Papers concerned with normal metabolic processes or with constituents of normal cells or body fluids, such as reports of experimental or clinical studies in animals, are only considered when they are clearly and directly relevant to human disease. Evaluation of commercial products have a low priority for publication, unless they are novel or represent a technological breakthrough. Studies dealing with effects of drugs and natural products and studies dealing with the redox status in various diseases are not within the journal''s scope. Development and evaluation of novel analytical methodologies where applicable to diagnostic clinical chemistry and laboratory medicine, including point-of-care testing, and topics on laboratory management and informatics will also be considered. Studies focused on emerging diagnostic technologies and (big) data analysis procedures including digitalization, mobile Health, and artificial Intelligence applied to Laboratory Medicine are also of interest.
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