Achieving a New Artificial Intelligence System forSerum Protein Electrophoresis to Recognize M‑Spikes

Purpose: In order to accurately identify the low-concentration M-spikes in serum protein electrophoresis (SPE) patterns, a new artificial intelligence (AI) system is explored. Methods: 166,003 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. Results: AIRSPE 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 (<i>P</i> > 0.05). AIRSPE and IFE results showed a concordance (<i>k</i> = 0.832) that implies an almost perfect agreement, which was higher than that between manual interpretation and IFE (<i>k</i> = 0.699). The M-spikes that were identified by IFE as positive in the internal test data sets that were detected by AIRSPE but were not detected by manual interpretation were mainly concentrated in the γ-fraction, with M-spike concentrations lower than 0.5 g/L. Conclusions: AIRSPE, 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.