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ACCV: automatic classification algorithm of cataract video based on deep learning.

Shenming Hu1, Xinze Luan2, Hong Wu3

  • 1College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110016, China.

Biomedical Engineering Online
|August 6, 2021
PubMed
Summary

A new real-time algorithm uses cataract videos for automatic grading, achieving high accuracy (94%) and efficiency. This method enhances cataract screening, especially for non-specialists in underserved regions.

Keywords:
Automatic cataract gradingDeep learningYOLOv3

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Cataract grading is crucial for timely intervention.
  • Current methods can be subjective and time-consuming.
  • Automated analysis of medical videos offers potential for improved diagnostics.

Purpose of the Study:

  • To develop a real-time automatic cataract-grading algorithm using slit-lamp videos.
  • To enhance the accuracy and efficiency of cataract screening.

Main Methods:

  • A retrospective study utilized cataract videos from 76 eyes.
  • YOLOv3 was employed for lens positioning and classification post-color space conversion.
  • 1520 images were extracted and divided into training, validation, and test sets (7:2:1 ratio).

Main Results:

  • The algorithm achieved an accuracy of 0.9400, AUC of 0.9880, and F1 score of 0.9388 on the test set.
  • Frame-by-frame detection was completed within 29 microseconds, demonstrating high efficiency.
  • The color space recognition method contributed to the algorithm's performance.

Conclusions:

  • The proposed algorithm offers an efficient and effective approach to cataract grading using video analysis.
  • It improves screening accuracy and aligns with clinical diagnosis processes.
  • The tool can empower non-ophthalmologists and increase access to eye care in remote areas.