Clinician and Automated Assessments of Facial Function Following Eyelid Weight Placement.

Abstract

Importance: Quantitative assessment of facial function is difficult, and historic grading scales such as House-Brackmann have well-recognized limitations. The electronic, clinician-graded facial function scale (eFACE) allows rapid regional analysis of static, dynamic, and synkinetic facial function in patients with unilateral facial palsy within the course of a clinical encounter, but it relies on clinician assessment. A newly developed, machine-learning algorithm (Emotrics) provides automated, objective facial measurements but lacks clinical input (ie, recognizing laterality of facial palsy or synkinesis).

Objectives: To compare the sensitivity of a clinician-based tool (eFACE) to a well-established intervention for facial palsy (eyelid weight placement) with an automated facial-measurement algorithm (Emotrics).

Design, setting, and participants: A retrospective review was conducted of the most recent 53 patients with unilateral facial palsy who received an eyelid weight at the Massachusetts Eye and Ear Infirmary Facial Nerve Center from 2014 to 2017. Preoperative and postoperative photographs were deidentified and randomized. The entire cohort was analyzed by 3 clinicians, as well as by the Emotrics program.

Main outcomes and measures: eFACE scores of the palpebral fissure at rest (0, wide; 100, balanced; 200, narrow), with gentle eyelid closure (0, incomplete; 100, complete), and with forceful eyelid closure (0, incomplete; 100, complete) before and after eyelid weight placement were compared with palpebral fissure measurements by Emotrics.

Results: Of the 53 participants, 33 were women, and mean (SD) age was 44.7 (18) years. The mean (SD) eFACE scores and Emotrics measurements (in millimeters) before vs after eyelid weight placement of the palpebral fissure at rest (eFACE, 84.3 [15.9] vs 109.7 [21.4]; Emotrics, 10.3 [2.2] vs 9.1 [1.8]), with gentle eyelid closure (eFACE, 65.9 [28.0] vs 92.1 [15.4]; Emotrics, 4.4 [2.7] vs 1.3 [2.0]), and with forceful eyelid closure (eFACE, 75.1 [28.6] vs 97.0 [10.7]; Emotrics, 3.0 [3.1] vs 0.5 [1.3]) all significantly improved. Subgroup analysis of patients with expected recovery (eg, Bell palsy) (n = 40) demonstrated significant development of ocular synkinesis on eFACE (83.9 [22.7] vs 98.9 [4.4]) after weight placement, which could also explain the improvement in eyelid function. The scores of patients with no expected recovery (n = 13) improved in both eFACE and Emotrics analysis following eyelid weight placement, though results did not reach significance, likely limited by the small subgroup size.

Conclusions and relevance: The eFACE tool agrees well with automated, objective facial measurements using a machine-learning based algorithm such as Emotrics. The eFACE tool is sensitive to spontaneous recovery and surgical intervention, and may be used for rapid regional facial function assessment from a clinician's perspective following recovery and/or surgical intervention.

Level of evidence: 4.