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A Fully Automatic Estimation of Tear Meniscus Height Using Artificial Intelligence.

Shaopan Wang1,2, Xin He2,3, Jiezhou He1

  • 1Institute of Artificial Intelligence, Xiamen University, Xiamen, Fujian, China.

Investigative Ophthalmology & Visual Science
|October 4, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces an AI system for automatically measuring tear meniscus height (TMH), crucial for diagnosing dry eye disease. The developed AI system demonstrates high accuracy and strong correlation with manual measurements, offering a reliable tool for clinical screening.

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Accurate tear meniscus height (TMH) measurement is essential for diagnosing dry eye disease.
  • Current clinical methods for TMH measurement are manual and subjective.
  • There is a need for automated and objective methods for TMH evaluation.

Purpose of the Study:

  • To develop and validate a novel artificial intelligence (AI) system for automatic measurement of tear meniscus height (TMH).
  • To improve the accuracy and efficiency of dry eye diagnosis through automated TMH quantification.

Main Methods:

  • A modified UNet-like neural network was utilized for segmenting corneal and tear meniscus regions.
  • Data augmentation techniques were employed to enhance the training dataset, resulting in 3,500 images.
  • The system includes a calculation module to report TMH based on segmented regions.

Main Results:

  • The AI model achieved high performance with Dice Similarity Coefficient (DSC) of 0.92 and Intersection over Union (IoU) of 0.86 for tear meniscus segmentation.
  • The automatically measured TMH strongly correlated with manual measurements by ophthalmologists on the test set.
  • The system demonstrated excellent performance in corneal segmentation with DSC/IoU of 0.99/0.98.

Conclusions:

  • A fully automated and reliable AI system for TMH measurement has been successfully developed.
  • The AI system shows potential for widespread clinical application in dry eye screening.
  • This technology offers an objective and efficient approach to aid in dry eye diagnosis.