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A Novel System for Measuring Eyeball Rotation Angle Based on Color Fundus Photographs in Natural Head Position.

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An AI system accurately measures eyeball rotation angles for diagnosing eye diseases. This automated tool enhances clinical diagnostics by providing reliable measurements comparable to expert assessments.

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Area of Science:

  • Ophthalmology
  • Medical Image Processing
  • Artificial Intelligence

Background:

  • Eyeball rotation angle is a critical indicator for assessing eye disease severity.
  • Accurate measurement of eyeball rotation is challenging in clinical practice.
  • Existing methods for measuring eyeball rotation lack automation and consistency.

Purpose of the Study:

  • To develop an artificial intelligence (AI)-based system for precise measurement of eyeball rotation angles.
  • To accurately segment the optic disc and macula for subsequent angle computation.
  • To provide a reliable tool for clinical diagnostics of eye diseases.

Main Methods:

  • The system employs three modules: optic disc segmentation, macular segmentation, and measurement.
  • Efficient-UNet3+ is used for optic disc segmentation, addressing sample imbalance and edge detection.
  • Dual Attention network-based Efficient-UNet (DA-EUNet) enhances macular recognition and suppresses background noise.
  • Eyeball rotation angle is calculated by determining the angle between the line connecting optic disc and macula centers and the horizontal vector.

Main Results:

  • The AI system achieved a high correlation coefficient of 0.94 compared to expert measurements.
  • Statistical analysis showed no significant difference between AI-based and expert assessments (P = 0.26).
  • The segmentation techniques significantly improved feature recognition and measurement accuracy.

Conclusions:

  • The developed AI system demonstrates high accuracy and reliability for clinical diagnostics.
  • Advanced segmentation techniques enhance the performance of eyeball rotation measurement.
  • This automated system offers a valuable tool for ophthalmologists, improving diagnostic consistency and reducing workload.