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Related Experiment Video

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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Lower limb edema detection and grading classification using deep learning and image enhancement technologies.

Ting-Wei Hu1, Chao-Hung Wang2,3, Min-Hui Liu2

  • 1Programs of Artificial Intelligence Technology, Innovation Frontier Institute of Research for Science and Technology, National Taipei University of Technology, Taipei, Taiwan.

Frontiers in Medicine
|March 27, 2026
PubMed
Summary

This study introduces an AI system for automatic lower limb edema detection and grading. The deep learning framework achieves high accuracy, improving clinical diagnosis and enabling precision medicine for chronic disease management.

Keywords:
YOLOdeep learningedema gradingimage enhancementlower limb edema

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Deep Learning

Background:

  • Lower limb edema is a common symptom of chronic diseases like heart failure, liver, and kidney dysfunction.
  • Current edema assessment methods (visual inspection, palpation) are subjective and lack consistency.
  • Standardized and precise diagnostics are needed for effective clinical management of edema.

Purpose of the Study:

  • To develop and validate a deep learning framework for automatic detection and grading of lower limb edema.
  • To overcome limitations of traditional subjective edema assessment methods.
  • To enhance accuracy and consistency in edema severity classification.

Main Methods:

  • A multistage deep learning framework integrating YOLO object detection and image classification was proposed.
  • Image enhancement techniques and data augmentation (random rotation) were employed.
  • Automatic background elimination and cropping were used to improve classification performance.

Main Results:

  • The system achieved 87-93% classification accuracy across different edema severity levels.
  • Recall rates ranged from 90-94%, and precision rates were 93-97%.
  • Results validate the system's feasibility and effectiveness for edema assessment.

Conclusions:

  • The proposed AI system offers a reliable tool for objective lower limb edema assessment.
  • It supports clinical decision-making and facilitates home-based self-care for patients.
  • The system advances smart and precision medicine by enabling accurate edema status monitoring.