Quantification of expected information gain in visual acuity and contrast sensitivity tests.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
05 10 2023
05 10 2023
Historique:
received:
06
06
2023
accepted:
29
09
2023
medline:
1
11
2023
pubmed:
6
10
2023
entrez:
5
10
2023
Statut:
epublish
Résumé
We make use of expected information gain to quantify the amount of knowledge obtained from measurements in a population. In the first application, we compared the expected information gain in the Snellen, ETDRS, and qVA visual acuity (VA) tests, as well as in the Pelli-Robson, CSV-1000, and qCSF contrast sensitivity (CS) tests. For the VA tests, ETDRS generated more expected information gain than Snellen. Additionally, the qVA test with 15 rows (or 45 optotypes) generated more expected information gain than ETDRS, whether scored with VA threshold alone or with both VA threshold and VA range. Regarding the CS tests, CSV-1000 generated more expected information gain than Pelli-Robson, and the qCSF test with 25 trials generated more expected information gain than CSV-1000, whether scored with AULCSF or with CSF at six spatial frequencies. The active learning-based qVA and qCSF tests have the potential to generate more expected information gain than traditional paper chart tests. Although we have specifically applied it to compare VA and CS tests, expected information gain is a general concept that can be used to compare measurements in any domain.
Identifiants
pubmed: 37798305
doi: 10.1038/s41598-023-43913-1
pii: 10.1038/s41598-023-43913-1
pmc: PMC10556053
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Langues
eng
Sous-ensembles de citation
IM
Pagination
16795Subventions
Organisme : NEI NIH HHS
ID : EY017491
Pays : United States
Commentaires et corrections
Type : UpdateOf
Informations de copyright
© 2023. Springer Nature Limited.
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