Automated extraction of clinical measures from videos of oculofacial disorders using machine learning: feasibility, validity and reliability.

OBJECTIVES

To determine the feasibility, validity and reliability of automatically extracting clinically meaningful eyelid measurements from consumer-grade videos of individuals with oculofacial disorders.

METHODS

A custom computer program was designed to automatically extract clinical measures from consumer-grade videos. This program was applied to publicly available videos of individuals with oculofacial disorders, and age-matched controls. The primary outcomes were margin reflex distance 1 (MRD1) and 2 (MRD2), blink lagophthalmos, and ocular surface area exposure. Test-retest reliability was evaluated using Bland-Altman analysis to compare the agreement in obtained measures between separate videos of the same individual taken within 48 h of each other.

RESULTS

MRD1 was reduced in individuals with ptosis versus controls (2.2 mm versus 3.4 mm, p < 0.001), and increased in individuals with facial nerve palsy (FNP) (3.9 mm, p = 0.049) and thyroid eye disease (TED) (4.1 mm; p = 0.038). Blink lagophthalmos was increased in individuals with FNP (3.7 mm); p < 0.001) and those with TED (0.1 mm, p = 0.003) versus controls (0.0 mm). Ocular surface exposure was reduced in individuals with ptosis compared with controls (12.2 mm versus 13.1 mm; p < 0.001) and increased in TED (13.7 mm; p 0.002). Bland-Altmann analysis demonstrated 95% limits of agreement for video-derived measures: median MRD1: -1.1 to 1.1 mm; median MRD2: -0.9 to 1.0 mm; blink lagophthalmos: -3.5 to 3.7 mm; and average ocular surface area exposure: -1.6 to 1.6 mm.

CONCLUSIONS

The presented program is capable of taking consumer grade videos of patients with oculofacial disease and providing clinically meaningful and reliable eyelid measurements that show promising validity.