Augmenting Kalman Filter Machine Learning Models with Data from OCT to Predict Future Visual Field Loss: An Analysis Using Data from the African Descent and Glaucoma Evaluation Study and the Diagnostic Innovation in Glaucoma Study.
Ophthalmol Sci
; 2(1): 100097, 2022 Mar.
Article
en En
| MEDLINE
| ID: mdl-36246178
AD, African descent; ADAGES, African Descent and Glaucoma Evaluation Study; Algorithm bias; CI, confidence interval; D, diopter; DIGS, Diagnostic Innovation in Glaucoma Study; ED, European descent; Glaucoma; IOP, intraocular pressure; KF, Kalman filter; KF-TP, Kalman filter with tonometry and perimetry data; KF-TPO, Kalman filter with tonometry, perimetry, and global retinal nerve fiber layer data; Kalman filter; LR1, linear regression model 1; LR2, linear regression model 2; MAE, mean absolute error; MD, mean deviation; Machine learning; OAG, open-angle glaucoma; OCT; PSD, pattern standard deviation; RMSE, root mean square error; RNFL, retinal nerve fiber layer; SD, standard deviation; VF, visual field
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Tipo de estudio:
Diagnostic_studies
/
Observational_studies
/
Prognostic_studies
/
Risk_factors_studies
Idioma:
En
Revista:
Ophthalmol Sci
Año:
2022
Tipo del documento:
Article