Objective:
We aimed to develop the first smartphone-based AI-assisted image processing algorithm for eyelid measurement, including MRD1, MRD2, and Levator function.

Material and Methods:
This observational study included 822 eyes of 411 volunteers aged over 18 years from August 1, 2020, to April 30, 2021. We used a smartphone (iPhone 11 Pro Max) to take six orbital photographs, including bilateral primary gaze, up-gaze, and down-gaze.
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. The results of AI prediction were excellent in MRD1 and MRD2 model. The results of LF model were also acceptable, but we believed could become better through collecting more photos. 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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