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A Deep-Learning-Based Collaborative Edge-Cloud Telemedicine System for Retinopathy of Prematurity.

Zeliang Luo1, Xiaoxuan Ding2, Ning Hou2

  • 1College of Electro-Mechanical Engineering, Zhuhai City Polytechnic, Zhuhai 519090, China.

Sensors (Basel, Switzerland)
|January 8, 2023
PubMed
Summary

A new telemedicine system uses artificial intelligence to screen premature infants for retinopathy of prematurity. This deep learning approach improves diagnosis in remote areas, reducing blindness risk.

Keywords:
artificial intelligencedeep learningedge–cloud collaborationobject detectionretinopathy of prematurity (ROP)telemedicine

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Retinopathy of prematurity (ROP) is a leading cause of blindness in premature infants.
  • Timely fundus screening is crucial for ROP diagnosis and treatment.
  • Remote areas often lack expert medical staff for effective ROP screening.

Purpose of the Study:

  • To develop a deep learning-based collaborative edge-cloud telemedicine system for ROP screening.
  • To address the challenges of ROP diagnosis in underserved remote regions.
  • To improve access to timely and effective ROP screening for premature infants.

Main Methods:

  • A ResNet101 deep learning model was employed for image classification.
  • Undersampling and resampling techniques were used to handle data imbalance.
  • A collaborative edge-cloud architecture was implemented for a comprehensive telemedicine system.
  • The system was embedded in a mobile terminal and deployed in Guangdong Province.

Main Results:

  • The system achieved 75% accuracy (ACC) and 60% area under the curve (AUC).
  • The AI-powered telemedicine system demonstrated feasibility for remote ROP screening.
  • The solution addresses the shortage of medical expertise in rural areas.

Conclusions:

  • The proposed deep learning telemedicine system offers a viable solution for ROP screening in remote areas.
  • This technology can significantly mitigate the impact of uneven medical resource distribution.
  • Further development and deployment can improve outcomes for premature infants at risk of ROP.