Activating the levator to elevate the eyelid

An Adjustable Magnetic Levator Prosthesis for Customizable Eyelid Re-Animation in Severe Blepharoptosis: Design and Proof-of-Concept

Purpose

Blepharoptosis is a common oculoplastic condition causing incomplete opening of the upper eyelid. Surgical approaches, the mainstay for correction, often fail to improve blink function. The purpose of this study was to develop a nonsurgical treatment option for severe ptosis that allows blink re-animation.

Methods

Magnetic force required to perform blink re-animation was characterized by evaluation of eye-opening and closing using inter-palpebral fissure (IPF) outcomes with various combinations of eyelid array and box magnets. Optimal size of the spectacle magnet that achieved forces required for optimal blink dynamics was selected using simulation. The adjustable magnetic levator prosthesis (aMLP) included an eyelid array magnet and an adjustable rotating spectacle magnet that allowed change in the magnetic direction, thus changing the net magnetic interactive force between the magnets. The clinical feasibility of aMLP in improving eye opening without limiting eye closing was evaluated in patients with ptosis through a proof-of-concept study using IPF and comfort outcomes.

Results

Optimal eye opening and closing was achieved by a magnet-array combination providing 45 grams of surface force (gF) in the tested ptosis population. The aMLP was able to modulate eye opening and closing with change in rotation of the spectacle magnet in two patients with ptotis. The best fitting of an aMLP improved IPF opening without limiting eye closing and with good comfort reported.

Conclusions

Preliminary results suggest that the an aMLP can correct ptosis without adversely affecting blink function. Further evaluation in a larger patient population is warranted.

Translational Relevance

A nonsurgical, proof of concept, adjustable magnetic treatment option for blink re-animation in patients with severe ptosis is presented.

A clinical decision model based on machine learning for ptosis

Background

To establish a decision model based on two- (2D) and three-dimensional (3D) eye data of patients with ptosis for developing personalized surgery plans.

Methods

Data of this retrospective, case-control study was collected from March 2019 to June 2019 at the Department of Ophthalmology, Shanghai Ninth People’s Hospital, and then the patients were followed up for 3 months. One hundred fifty-two complete feature eyes from 100 voluntary patients with ptosis and satisfactory surgical results were selected, with 48 eyes excluded due to any severe condition or improper collection and shooting angle. Three experimental schemes were set as follows: use 2D distance alone, use 3D distance alone, and use two distances at the same time. The five most common evaluation indicators used in the binary classification problem to test the decision model were accuracy (ACC), precision, recall, F1-score, and area under the curve (AUC).

Results

For diagnostic discrimination, recall of “3D”, “2D” and “Both” schemes were 0.875, 0.875 and 0.938 respectively. And precision of the three schemes were 0.8333, 0.7778 and 1.0000 for the surgical procedure classification. Values of “Both” scheme that combined 2D and 3D data were the highest in two classifications.

Conclusions

In this study, 3D eye data are introduced into clinical practice to construct a decision model for ptosis surgery. Our decision model presents exceptional prediction effect, especially when 2D and 3D data employed jointly.