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

Honghua Hu1, Wei Xu2,3, Ting Jiang4

  • 1Department of Laboratory Medicine and Sichuan Provincial Key Laboratory for Human Disease Gene Study, Sichuan Provincial Peoples Hospital, University of Electronic Science and Technology of China, Chengdu 610072, China.

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Summary

An AI system accurately recognizes immunofixation electrophoresis (IFE) images for diagnosing plasma cell disorders (PCDs). This AI achieves human-level performance, improving diagnostic efficiency and reliability.

Keywords:
M-proteindeep learningimmunofixation electrophoresisplasma cell disorders

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Area of Science:

  • Medical Diagnostics
  • Artificial Intelligence in Medicine
  • Biomedical Imaging Analysis

Background:

  • Immunofixation electrophoresis (IFE) is crucial for diagnosing plasma cell disorders (PCDs).
  • Manual analysis of IFE images is time-consuming and subjective.
  • Automated and accurate IFE image recognition using AI is highly desirable.

Purpose of the Study:

  • To develop and validate an artificial intelligence (AI) system for automatic recognition of IFE images.
  • To assess the performance of the AI system in detecting diagnostic patterns in IFE images.
  • To evaluate the explainability and generalizability of the AI system.

Main Methods:

  • An ensemble AI model comprising 3 deep neural networks was developed using 12,703 expert-annotated IFE images.
  • The AI model predicts 8 basic IFE patterns and their combinations.
  • Score-based class activation maps (Score-CAMs) were employed for visual explanation of predictions.

Main Results:

  • The AI model achieved high performance metrics: 99.82% accuracy, 93.17% sensitivity, and 99.93% specificity for 8 basic patterns.
  • AI performance surpassed junior experts and was comparable to a senior expert.
  • Score-CAMs provided reasonable visual explanations, highlighting relevant image regions.

Conclusions:

  • The developed AI system demonstrates human-level performance in automatic IFE image recognition.
  • The AI system offers high explainability and generalizability.
  • This AI has the potential to enhance the efficiency and reliability of PCD diagnosis.