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Multitask Interactive Attention Learning Model Based on Hand Images for Assisting Chinese Medicine in Predicting

Qida Wang1, Chenqi Zhao1, Yan Qiang1

  • 1College of Information and Computer, Taiyuan University of Technology, Taiyuan, China.

Computational and Mathematical Methods in Medicine
|November 5, 2021
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Summary

This study introduces a novel deep learning model, MTIALM, for noninvasive diagnosis of acute myocardial infarction (AMI) using palm images. The model shows improved accuracy in detecting key indicators, offering a convenient auxiliary diagnostic tool.

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Area of Science:

  • Cardiology
  • Artificial Intelligence
  • Traditional Chinese Medicine

Background:

  • Acute myocardial infarction (AMI) is a critical cardiovascular disease with increasing incidence, particularly in younger populations.
  • Current diagnostic methods for AMI are often invasive, complex, and carry risks of complications.
  • There is a need for noninvasive and convenient auxiliary diagnostic approaches for AMI.

Purpose of the Study:

  • To develop a noninvasive auxiliary diagnostic method for acute myocardial infarction (AMI) by integrating Traditional Chinese Medicine (TCM) principles with deep learning.
  • To propose a novel deep learning model, MTIALM (Myocardial Infarction Traditional Chinese Medicine Palm Image), for predicting AMI based on palm features.
  • To enhance diagnostic accuracy by utilizing shared networks, task-specific attention branches, and an information interaction module (IIM).

Main Methods:

  • A depth model, MTIALM, was developed using traditional palm images for AMI detection.
  • A shared network architecture was employed to learn comprehensive information across all diagnostic tasks.
  • Task-specific attention branch networks and an information interaction module (IIM) were integrated to refine feature learning and information integration.

Main Results:

  • The MTIALM model achieved an accuracy of 83.16% in detecting metacarpophalangeal joint swelling and 84.15% in identifying palmar thenar hypertrophy.
  • These accuracies represent significant improvements compared to traditional classification methods for AMI auxiliary diagnosis.
  • The model effectively learned and integrated features from different palm regions for improved prediction.

Conclusions:

  • The proposed MTIALM model offers a promising noninvasive and convenient auxiliary diagnostic tool for acute myocardial infarction (AMI).
  • Integrating deep learning with TCM palm diagnosis provides a valuable approach for early detection and risk assessment of AMI.
  • Further research and validation are warranted to establish this method in clinical practice for cardiovascular disease management.