Open AccessSmartphone-Based Artificial Intelligence–Assisted Prediction for Eyelid Measurements: Algorithm Development and Observational Validation Study
Author(s) -
Hung-Chang Chen,
Shin-Shi Tzeng,
Yen-Chang Hsiao,
Ruei-Feng Chen,
Erh-Chien Hung,
Oscar K. Lee
Publication year - 2021
Publication title -
jmir mhealth and uhealth
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.356
H-Index - 50
ISSN - 2291-5222
DOI - 10.2196/32444
Subject(s) - algorithm , gold standard (test) , intraclass correlation , computer science , artificial intelligence , eye tracking , correlation coefficient , mathematics , machine learning , reproducibility , statistics
Background Margin reflex distance 1 (MRD1), margin reflex distance 2 (MRD2), and levator muscle function (LF) are crucial metrics for ptosis evaluation and management. However, 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 propose 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 that obtained with the LF model was good ( r =0.73). The intraclass correlation coefficient demonstrated excellent agreement between the gold-standard measurements and the values predicted by the MRD1 and 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 the 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 We developed 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.