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Smartphone-Based Artificial Intelligence-Assisted Prediction for Eyelid Measurements: Algorithm Development and

Hung-Chang Chen1,2, Shin-Shi Tzeng1,2, Yen-Chang Hsiao1,2

  • 1Department of Plastic and Reconstructive Surgery, Chang Gung Memorial Hospital, Taoyuan, Taiwan.

JMIR Mhealth and Uhealth
|September 20, 2021
PubMed
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This summary is machine-generated.

This study introduces a novel smartphone AI algorithm for measuring eyelid ptosis metrics like MRD1, MRD2, and LF. The AI tool offers a quick, objective, and convenient method for evaluating ptosis, improving clinical data collection.

Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Manual measurement of Margin Reflex Distance 1 (MRD1), Margin Reflex Distance 2 (MRD2), and Levator function (LF) is crucial for ptosis evaluation.
  • Current manual methods are time-consuming, subjective, and prone to errors.
  • Smartphone-based AI offers a potential solution to these limitations.

Purpose of the Study:

  • To develop and validate the first smartphone-based AI-assisted image processing algorithm for MRD1, MRD2, and LF measurements.
  • To assess the accuracy and reliability of AI-driven ptosis measurements compared to gold standards.

Main Methods:

  • An observational study involving 822 eyes from 411 volunteers.
  • Smartphone (iPhone 11 Pro Max) captured orbital photographs in different gazes.
Keywords:
AIalgorithmartificial intelligencedeep learningeyeimagelevator muscle functionlimitmargin reflex distance 1margin reflex distance 2measurementobservationalpredictionprocessingsmartphone

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  • AI-assisted software processed images to create MRD1, MRD2, and LF models based on gold-standard measurements.

    • Main Results:

    • Excellent correlation (r=0.91 for MRD1, r=0.88 for MRD2) and good correlation (r=0.73 for LF) were found between AI predictions and gold standards.
    • High agreement was observed: intraclass correlation coefficients were 0.90 (MRD1), 0.84 (MRD2), and 0.73 (LF).
    • Mean absolute errors were low: 0.35 mm (MRD1), 0.37 mm (MRD2), and 1.06 mm (LF).

    Conclusions:

    • The first smartphone-based AI algorithm for eyelid measurements (MRD1, MRD2, LF) has been developed.
    • This AI tool enables quick, objective, and convenient ptosis measurements.
    • Smartphone accessibility facilitates anytime, anywhere data collection and ptosis assessment.