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...

Full description

Saved in:
Bibliographic Details
Published in:Eye (London) 2023-09, Vol.37 (13), p.2810-2816
Main Authors: Schulz, Christopher B., Clarke, Holly, Makuloluwe, Sarith, Thomas, Peter B., Kang, Swan
Format: Article
Language:English
Subjects:
692/699/3161/3166
692/700/139/1735
Eye diseases
Eyelid
Facial nerve
Laboratory Medicine
Medicine
Medicine & Public Health
Ophthalmology
Paralysis
Pharmaceutical Sciences/Technology
Surface area
Surgery
Surgical Oncology
Validity
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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  
ISSN:0950-222X
1476-5454
DOI:10.1038/s41433-023-02424-z