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Artificial intelligence-assisted tear meniscus height measurement: a multicenter study.

Kesheng Wang1, Kunhui Xu2, Xiaoyu Chen2

  • 1College of Mathematical Medicine, Zhejiang Normal University, Jinhua, China.

Quantitative Imaging in Medicine and Surgery
|May 19, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces an AI model for accurate tear meniscus height (TMH) measurement, improving dry eye diagnosis. The AI algorithm demonstrates strong generalization and reliability, surpassing human specialists.

Keywords:
Tear meniscus height (TMH)deep learningdry eyeimage gradientmulticenter

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Tear meniscus height (TMH) is crucial for diagnosing dry eye disease (DED).
  • Manual TMH measurement by doctors is prone to errors and requires specialized skills.
  • Existing AI models for TMH segmentation lack generalization due to limited datasets and single imaging modalities.

Purpose of the Study:

  • To develop an automatic TMH measurement method using convolutional neural networks (CNNs).
  • To create an AI model capable of handling diverse TMH image datasets for improved diagnostic accuracy.

Main Methods:

  • A multicenter retrospective study involving 3,894 TMH images from five centers.
  • Development of an attention-limiting neural network (ALNN) using a gradient information-guided human-computer collaborative annotation method.
  • Validation using internal datasets (color and infrared images) and an external validation set to assess generalizability.

Main Results:

  • High accuracy in AI segmentation for color images (MIoU 0.9578) and infrared images (MIoU 0.9290).
  • Strong correlation between AI-derived TMH measurements and ground truth on both test and external validation sets (r=0.935-0.957 for color, r=0.803-0.855 for infrared).
  • The AI algorithm demonstrated superior reliability compared to manual measurements by specialists.

Conclusions:

  • The developed AI algorithm offers strong generalization capabilities for TMH segmentation and quantitative analysis.
  • AI-driven TMH measurements show high consistency with ground truth, exceeding the reliability of human specialists.
  • This AI tool provides significant support for the accurate diagnosis of dry eye disease.