ArticleACS omega2025
Achieving a New Artificial Intelligence System for Serum Protein Electrophoresis to Recognize M-Spikes.
Article in ACS omega, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- Clinical Significance of Serum Protein Electrophoresis in Rapid Progression of Multiple Myeloma: A Case Report.Clinics and practice · 2026Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeIn order to accurately identify the low-concentration M-spikes in serum protein electrophoresis (SPE) patterns, a new artificial intelligence (AI) system is explored.
methods166,003 SPE data sets, which were equally divided into 4 training sets and 1 optimal set, were utilized to establish and evaluate the AI system, namely, "AIRSPE". 10,014 internal test sets and 1861 external test sets with immunofixation electrophoresis (IFE) results as gold standard were used to assess the performance of AIRSPE including sensitivity, negative predictive value, and concordance. In the internal test group with different concentrations of M-spikes, the consistencies of AIRSPE and manual interpretation with IF-positive results were compared.
resultsAIRSPE selected MobileNetv2, which performed with F1-score of 84.60%, precision of 76.20%, recall of 95.20%, loss of 26.80%, accuracy of 89.48%, and interpretation time of 14 ms. In internal test sets, the sensitivity and negative predictive values of AIRSPE were 95.21% and 97.65%, respectively, with no significant difference in performance compared to the external test set (
conclusionsAIRSPE, established through AI deep learning and validated by IFE results, significantly outperforms manual interpretation in detecting low-concentration M-spikes, demonstrating its potential to assist with clinical screening for M-spikes.
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Registered trials
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