The Effect of Upper Eyelid Muller Muscle Conjunctival Resection Surgery on Lower Eyelid Position.

Purpose: To examine whether Müller's-muscle-conjunctival-resection (MMCR) changes the position of the lower eyelid.Methods: Retrospective controlled-cohort study. All patients who underwent MMCR (study group) or blepharoplasty (control group) between January 2016 and September 2018 were recruited. The data retrieved from the patients' medical records included demographics, visual-acuity, eyelid parameters and dry-eye parameters before and 3 months after surgery. Frontal photographs of the patient's eyes in primary position were taken preoperatively and at 3 months postoperatively. The margin-reflex-distance 1 (MRD1) and MRD2 were evaluated. The preoperative and 3 months postoperative MRD1, MRD2,and dry-eye signs and symptoms were compared.Results: Sixty-nine patients underwent MMCR and 54 patients underwent blepharoplasty during the study period. There were significant changes in MRD2 after MMCR surgery compared to preoperative values (P < .01, paired t-test), but no significant changes in MRD2 after blepharoplasty surgery (P = .091, paired t-test). The mean changes in MRD2 (delta MRD2) were 0.51 in the MMCR group versus (-0.10) in the blepharoplasty group (P = .04, t-test).Conclusions: The position of the lower eyelid was altered significantly in patients that underwent MMCR surgery. This sequela should be discussed with the patients before surgery and should be considered by physicians when planning ptosis surgery.

Smartphone-Based Artificial Intelligence-Assisted Prediction for Eyelid Measurements: Algorithm Development and Observational Validation Study.

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.