Expert-Level Immunofixation Electrophoresis Image Recognition based on Explainable and Generalizable Deep Learning

Background Immunofixation electrophoresis (IFE) is important for diagnosis of plasma cell disorders (PCDs). Manual analysis of IFE images is time-consuming and potentially subjective. An artificial intelligence (AI) system for automatic and accurate IFE image recognition is desirable. Methods In total, 12 703 expert-annotated IFE images (9182 from a new IFE imaging system and 3521 from an old one) were used to develop and test an AI system that was an ensemble of 3 deep neural networks. The model takes an IFE image as input and predicts the presence of 8 basic patterns (IgA-kappa, IgA-lambda, IgG-kappa, IgG-lambda, IgM-kappa, IgM-lambda, light chain kappa and lambda) and their combinations. Score-based class activation maps (Score-CAMs) were used for visual explanation of the model's prediction. Results The AI model achieved an average accuracy, sensitivity, and specificity of 99.82%, 93.17%, and 99.93%, respectively, for detection of the 8 basic patterns, which outperformed 4 junior experts with <1 year's experience and was comparable to a senior expert with 5 years' experience. The Score-CAMs gave a reasonable visual explanation of the prediction by highlighting the target aligned regions in the bands and indicating potentially unreliable predictions. When trained with only the new system images, the model's performance was still higher than junior experts on both the new and old IFE systems, with average accuracy of 99.91% and 99.81%, respectively. Conclusions Our AI system achieved human-level performance in automatic recognition of IFE images, with high explainability and generalizability. It has the potential to improve the efficiency and reliability of diagnosis of PCDs.