Inferred retinal sensitivity in recessive Stargardt disease using machine learning.
Sci Rep
; 11(1): 1466, 2021 01 14.
Article
de En
| MEDLINE
| ID: mdl-33446864
ABSTRACT
Spatially-resolved retinal function can be measured by psychophysical testing like fundus-controlled perimetry (FCP or 'microperimetry'). It may serve as a performance outcome measure in emerging interventional clinical trials for macular diseases as requested by regulatory agencies. As FCP constitute laborious examinations, we have evaluated a machine-learning-based approach to predict spatially-resolved retinal function ('inferred sensitivity') based on microstructural imaging (obtained by spectral domain optical coherence tomography) and patient data in recessive Stargardt disease. Using nested cross-validation, prediction accuracies of (mean absolute error, MAE [95% CI]) 4.74 dB [4.48-4.99] were achieved. After additional inclusion of limited FCP data, the latter reached 3.89 dB [3.67-4.10] comparable to the test-retest MAE estimate of 3.51 dB [3.11-3.91]. Analysis of the permutation importance revealed, that the IS&OS and RPE thickness were the most important features for the prediction of retinal sensitivity. 'Inferred sensitivity', herein, enables to accurately estimate differential effects of retinal microstructure on spatially-resolved function in Stargardt disease, and might be used as quasi-functional surrogate marker for a refined and time-efficient investigation of possible functionally relevant treatment effects or disease progression.
Texte intégral:
1
Collection:
01-internacional
Base de données:
MEDLINE
Sujet principal:
Rétine
/
Tests du champ visuel
/
Maladie de Stargardt
Type d'étude:
Diagnostic_studies
/
Prognostic_studies
Limites:
Adult
/
Female
/
Humans
/
Male
/
Middle aged
Langue:
En
Journal:
Sci Rep
Année:
2021
Type de document:
Article
Pays d'affiliation:
Allemagne