Accepted for/Published in: JMIR mHealth and uHealth
Date Submitted: Jul 28, 2021
Date Accepted: Sep 19, 2021
Date Submitted to PubMed: Sep 20, 2021
Eyelid Measurements: Smartphone-Based Artificial Intelligence-Assisted Prediction
ABSTRACT
Background:
Margin reflex distance 1(MRD1), margin reflex distance 2 (MRD2), and levator muscle function (LF) are crucial for ptosis evaluation and management. Manual measurements of MRD1, MRD2, and LF are time-consuming, subjective, and prone to human error. Smartphone-based artificial intelligence (AI) image processing is a potential solution to overcome these limitations.
Objective:
We proposed the first smartphone-based AI-assisted image processing algorithm for MRD1, MRD2, and LF measurements.
Methods:
This observational study included 822 eyes of 411 volunteers aged over 18 years from August 1, 2020, to April 30, 2021. Six orbital photographs (bilateral primary gaze, up-gaze, and down-gaze) were taken using a smartphone (iPhone 11 pro max). The gold standard measurements and normalized eye photographs were obtained from these orbital photographs and compiled using AI-assisted software to create MRD1, MRD2 and LF models.
Results:
The Pearson correlation coefficients between the gold standard measurements and the predicted values obtained with the MRD1 and MRD2 models were excellent (r = 0.91, and 0.88, respectively) and with the LF model were good (r = 0.73). The intraclass correlation coefficient results showed excellent agreement between the gold standard measurements and the values predicted by the MRD1and MRD2 models (0.90, and 0.84, respectively), and substantial agreement with the LF model (0.69). The mean absolute errors were 0.35 mm, 0.37 mm, and 1.06 mm for MRD1, MRD2, and LF models, respectively. The 95% limits of agreement were -0.94 to 0.94 mm for the MRD1 model; -0.92 to 1.03 mm for the MRD2 model; and -0.63 to 2.53 mm for the LF model.
Conclusions:
In this study, we proposed the first smartphone-based AI-assisted image processing algorithm for eyelid measurements. MRD1, MRD2, and LF measures can be taken in a quick, objective, and convenient manner. Furthermore, by using a smartphone, the examiner can check these measurements anywhere and at any time, which facilitates data collection.
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