The Human Touch: Using a Webcam to Autonomously Monitor Compliance During Visual Field Assessments.
OpenFace
action units
adherence
affective computing
compliance
computer vision
deep learning
eye gaze
facial expression
glaucoma
head pose
machine learning
measurement error
perimetry
psychophysics
reliability
vigilance
visual fields
Journal
Translational vision science & technology
ISSN: 2164-2591
Titre abrégé: Transl Vis Sci Technol
Pays: United States
ID NLM: 101595919
Informations de publication
Date de publication:
07 2020
07 2020
Historique:
received:
05
11
2019
accepted:
16
03
2020
entrez:
29
8
2020
pubmed:
29
8
2020
medline:
29
8
2020
Statut:
epublish
Résumé
To explore the feasibility of using various easy-to-obtain biomarkers to monitor non-compliance (measurement error) during visual field assessments. Forty-two healthy adults (42 eyes) and seven glaucoma patients (14 eyes) underwent two same-day visual field assessments. An ordinary webcam was used to compute seven potential biomarkers of task compliance, based primarily on eye gaze, head pose, and facial expression. We quantified the association between each biomarker and measurement error, as defined by (1) test-retest differences in overall test scores (mean sensitivity), and (2) failures to respond to visible stimuli on individual trials (stimuli -3 dB or more brighter than threshold). In healthy eyes, three of the seven biomarkers were significantly associated with overall (test-retest) measurement error ( Inexpensive, autonomous measures of task compliance are associated with measurement error in visual field assessments, in terms of both the overall reliability of a test and failures to respond on particular trials ("lapses"). This could be helpful for identifying low-quality assessments and for improving assessment techniques (e.g., by discounting suspect responses or by automatically triggering comfort breaks or encouragement). This study explores a potential way of improving the reliability of visual field assessments, a crucial but notoriously unreliable clinical measure.
Identifiants
pubmed: 32855877
doi: 10.1167/tvst.9.8.31
pii: TVST-19-2093
pmc: PMC7422775
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Pagination
31Informations de copyright
Copyright 2020 The Authors.
Déclaration de conflit d'intérêts
Disclosure: P.R. Jones, None; G. Demaria, None; I. Tigchelaar, None; D.S. Asfaw, None; D.F. Edgar, None; P. Campbell, None; T. Callaghan, None; D.P. Crabb, None
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