Automated Clinical Assessment of Corneal Guttae in Fuchs Endothelial Corneal Dystrophy.
Adult
Aged
Aged, 80 and over
Algorithms
Automation
Descemet Membrane
/ pathology
Descemet Stripping Endothelial Keratoplasty
Diagnosis, Computer-Assisted
Extracellular Matrix
/ pathology
Female
Fuchs' Endothelial Dystrophy
/ classification
Humans
Image Processing, Computer-Assisted
Male
Middle Aged
Photography
Reproducibility of Results
Visual Acuity
Journal
American journal of ophthalmology
ISSN: 1879-1891
Titre abrégé: Am J Ophthalmol
Pays: United States
ID NLM: 0370500
Informations de publication
Date de publication:
01 2021
01 2021
Historique:
received:
06
07
2019
revised:
18
07
2020
accepted:
21
07
2020
pubmed:
31
7
2020
medline:
30
1
2021
entrez:
31
7
2020
Statut:
ppublish
Résumé
To describe the validation and implementation of an automated system for the detection and quantification of guttae in Fuchs endothelial corneal dystrophy (FECD). Observational reliability study. Patients with FECD underwent retroillumination corneal photography, followed by determination of the distributions and sizes of corneal guttae by an automated image analysis algorithm. Performance of the automated system was assessed via (1) validation against manual guttae segmentation, (2) reproducibility studies to ensure consistency, and (3) evaluation for agreement with the Krachmer scale. It was then deployed to perform large-scale guttae assessment with anatomic subregion analysis in a batch of 40 eyes. Compared to manual segmentation, the automated system was reasonably accurate in identifying the correct number of guttae (mean count of 78 guttae per 1 × 1 mm test frame, overestimation: +10 per frame), but had a tendency to significantly overestimate guttae size (mean guttae size 1073 μm Automated guttae assessment facilitates the precise identification and quantification of guttae characteristics in FECD patients. This can be used clinically as a personalized descemetorrhexis zone for Descemet stripping only and/or Descemet membrane transplantation.
Identifiants
pubmed: 32730910
pii: S0002-9394(20)30387-1
doi: 10.1016/j.ajo.2020.07.029
pii:
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Validation Study
Langues
eng
Sous-ensembles de citation
IM
Pagination
260-272Informations de copyright
Copyright © 2020 Elsevier Inc. All rights reserved.