Structure and function evaluation (SAFE): I. criteria for glaucomatous visual field loss using standard automated perimetry (SAP) and short wavelength automated perimetry (SWAP)11Internet Advance publication at ajo.com June 17, 2002.

2002 ◽  
Vol 134 (2) ◽  
pp. 177-185 ◽  
Author(s):  
Chris A Johnson ◽  
Pamela A Sample ◽  
George A Cioffi ◽  
Jeffrey R Liebmann ◽  
Robert N Weinreb
Keyword(s):  
Visual Field ◽  
Short Wavelength ◽  
Visual Field Loss ◽  
Structure And Function ◽  
Function Evaluation ◽  
Automated Perimetry ◽  
Standard Automated Perimetry ◽  
And Function

scholarly journals Estimating Rates of Progression and Predicting Future Visual Fields in Glaucoma Using a Deep Variational Autoencoder

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Samuel I. Berchuck ◽  
Sayan Mukherjee ◽  
Felipe A. Medeiros
Keyword(s):  
Visual Field ◽  
Learning Algorithm ◽  
Visual Fields ◽  
Visual Field Loss ◽  
Absolute Error ◽  
Rate Of Change ◽  
Mean Deviation ◽  
Automated Perimetry ◽  
Standard Automated Perimetry ◽  
Deep Learning Algorithm

AbstractIn this manuscript we develop a deep learning algorithm to improve estimation of rates of progression and prediction of future patterns of visual field loss in glaucoma. A generalized variational auto-encoder (VAE) was trained to learn a low-dimensional representation of standard automated perimetry (SAP) visual fields using 29,161 fields from 3,832 patients. The VAE was trained on a 90% sample of the data, with randomization at the patient level. Using the remaining 10%, rates of progression and predictions were generated, with comparisons to SAP mean deviation (MD) rates and point-wise (PW) regression predictions, respectively. The longitudinal rate of change through the VAE latent space (e.g., with eight dimensions) detected a significantly higher proportion of progression than MD at two (25% vs. 9%) and four (35% vs 15%) years from baseline. Early on, VAE improved prediction over PW, with significantly smaller mean absolute error in predicting the 4th, 6th and 8th visits from the first three (e.g., visit eight: VAE8: 5.14 dB vs. PW: 8.07 dB; P < 0.001). A deep VAE can be used for assessing both rates and trajectories of progression in glaucoma, with the additional benefit of being a generative technique capable of predicting future patterns of visual field damage.

Structure and function evaluation (SAFE): II. comparison of optic disk and visual field characteristics

2003 ◽  
Vol 135 (2) ◽  
pp. 148-154 ◽  
Author(s):  
Chris A Johnson ◽  
Pamela A Sample, PhD ◽  
Linda M Zangwill ◽  
Cristiana G Vasile ◽  
George A Cioffi ◽  
...  
Keyword(s):  
Visual Field ◽  
Structure And Function ◽  
Optic Disk ◽  
Function Evaluation ◽  
And Function

scholarly journals Estimating visual field loss from monoscopic optic disc photography using deep learning model

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Jinho Lee ◽  
Yong Woo Kim ◽  
Ahnul Ha ◽  
Young Kook Kim ◽  
Ki Ho Park ◽  
...  
Keyword(s):  
Deep Learning ◽  
Visual Field ◽  
Optic Disc ◽  
Correlation Coefficient ◽  
Characteristic Curve ◽  
Visual Field Loss ◽  
Absolute Error ◽  
Mean Deviation ◽  
Automated Perimetry ◽  
Standard Automated Perimetry

AbstractVisual field assessment is recognized as the important criterion of glaucomatous damage judgement; however, it can show large test–retest variability. We developed a deep learning (DL) algorithm that quantitatively predicts mean deviation (MD) of standard automated perimetry (SAP) from monoscopic optic disc photographs (ODPs). A total of 1200 image pairs (ODPs and SAP results) for 563 eyes of 327 participants were enrolled. A DL model was built by combining a pre-trained DL network and subsequently trained fully connected layers. The correlation coefficient and mean absolute error (MAE) between the predicted and measured MDs were calculated. The area under the receiver operating characteristic curve (AUC) was calculated to evaluate the detection ability for glaucomatous visual field (VF) loss. The data were split into training/validation (1000 images) and testing (200 images) sets to evaluate the performance of the algorithm. The predicted MD showed a strong correlation and good agreement with the actual MD (correlation coefficient = 0.755; R2 = 57.0%; MAE = 1.94 dB). The model also accurately predicted the presence of glaucomatous VF loss (AUC 0.953). The DL algorithm showed great feasibility for prediction of MD and detection of glaucomatous functional loss from ODPs.

SITA-Standard perimetry has better performance than FDT2 matrix perimetry for detecting glaucomatous progression

2018 ◽  
Vol 102 (10) ◽  
pp. 1396-1401
Author(s):  
Michael Wall ◽  
Chris A Johnson ◽  
K D Zamba
Keyword(s):  
Visual Field ◽  
Visual Field Loss ◽  
Detection Algorithm ◽  
Data Series ◽  
Automated Perimetry ◽  
Standard Automated Perimetry ◽  
Full Data ◽  
Visual Field Progression ◽  
Standard Testing ◽  
Humphrey Field Analyzer

PurposeThe Humphrey Matrix (FDT2) may be more sensitive in detecting glaucomatous visual field loss than SITA standard automated perimetry (SAP) performed on the Humphrey Field Analyzer (HFA). Therefore, FDT may be a good candidate to determine disease progression in patients with glaucoma. Our aim was to test the hypothesis that automated perimetry using the FDT2 would be equal to, or more effective than, HFA SITA-Standard, in identifying glaucomatous progression.MethodsOne hundred and twenty patients with glaucoma were tested twice at baseline and every 6 months for 4 years with HFA SITA-Standard and FDT2. FDT2 values were standardised to HFA SAP values. We used pointwise linear regression (PLR) over the full data series to identify glaucomatous progression and generated an array of results using three different criteria: (1) three or more clustered test locations progressing, (2) three or more non-clustered test locations progressing and (3) total number of progressing test locations. We compared HFA SAP and FDT2 for the number of locations signalled by the PLR detection algorithm.ResultsRegardless of the criteria, HFA SAP with SITA-Standard testing detected visual field progression at a higher rate than the FDT2 overall (P<0.001).ConclusionHFA SAP identifies glaucomatous visual field progression at a rate at least as high if not higher than FDT2.

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