Measuring visual field progression in the central 10 degrees using additional information from central 24 degrees visual fields and 'lasso regression'

Age prediction using fundus parameters of normal eyes from the Kumejima population study

PURPOSE

Artificial intelligence can predict the age of an individual using color fundus photographs (CFPs). This study aimed to investigate the accuracy of age prediction in the Kumejima study using fundus parameters and to clarify age-related changes in the fundus.

METHODS

We used nonmydriatic CFPs obtained from the Kumejima population study, including 1,646 right eyes of healthy participants with reliable fundus parameter measurements. The tessellation fundus index was calculated as R/(R + G + B) using the mean value of the red-green-blue intensity in eight locations around the optic disc and foveal region. The optic disc ovality ratio, papillomacular angle, and retinal vessel angle were quantified as previously described. Least absolute shrinkage and selection operator regression with leave-one-out cross-validation was used to predict age. The relationship between the actual and predicted ages was investigated using Pearson's correlation coefficient.

RESULTS

The mean age of included participants (834 males and 812 females) was 53.4 ± 10.1 years. The mean predicted age based on fundus parameters was 53.4 ± 8.9 years, with a mean absolute error of 3.64 years, and the correlation coefficient between actual and predicted age was 0.88 (p < 0.001). Older patients had greater red and green intensities and weaker blue intensities in the peripapillary area (p < 0.001).

CONCLUSIONS

Age could be predicted using the CFP parameters, and there were notable age-related changes in the peripapillary color intensity. The age-related changes in the fundus may aid the understanding of the mechanism of fundus diseases such as age-related macular degeneration.

Predicting the Extent of Damage in the Humphrey Field Analyzer 24-2 Visual Fields Using 10-2 Test Results in Patients With Advanced Glaucoma

Purpose

To predict Humphrey Field Analyzer 24-2 test (HFA 24-2) results using 10-2 results.

Methods

A total of 175 advanced glaucoma eyes (175 patients) with HFA 24-2 mean deviation (MD24-2) of < -20 dB were prospectively followed up for five years using HFA 10-2 and 24-2 (twice and once in a year, respectively). Using all the HFA 24-2 and 10-2 test result pairs measured within three months (350 pairs from 85 eyes, training dataset), a formula to predict HFA 24-2 result using HFA 10-2 results was constructed using least absolute shrinkage and selection operator regression (LASSO). Using 90 different eyes (testing dataset), the absolute differences between the actual and LASSO-predicted MD24-2 and that between the slopes calculated using five actual and LASSO-predicted MD24-2 values, were adopted as the prediction error. Similar analyses were performed for the mean total deviation values (mTD) of the superior (or inferior) hemifield [hemi-mTDsup.24-2(-hemi-mTDinf.24-2)].

Results

The prediction error for the LASSO-predicted MD24-2 and its slope were 2.98 (standard deviation [SD] = 1.90) dB and 0.32 (0.33) dB/yr, respectively. The LASSO-predicted hemi-mTDsup.24-2 (hemi-mTDinf.24-2), and its slope were 3.02 (2.89) and 3.76 (2.72) dB, and 0.37 (0.41) and 0.44 (0.38) dB/year, respectively. These prediction errors were within two times SD of repeatability of the simulated stable HFA 24-2 VF parameter series.

Conclusions

HFA 24-2 results could be predicted using the paired HFA 10-2 results with reasonable accuracy using LASSO in patients with advanced glaucoma.

Translational Relevance

It is useful to predict HFA24-2 test from HFA10-2 test, when the former is not available, in advanced glaucoma.

Three-year changes in sex judgment using color fundus parameters in elementary school students

PURPOSE

In a previous cross-sectional study, we reported that the sexes can be distinguished using known factors obtained from color fundus photography (CFP). However, it is not clear how sex differences in fundus parameters appear across the human lifespan. Therefore, we conducted a cohort study to investigate sex determination based on fundus parameters in elementary school students.

METHODS

This prospective observational longitudinal study investigated 109 right eyes of elementary school students over 4 years (age, 8.5 to 11.5 years). From each CFP, the tessellation fundus index was calculated as red/red + green + blue (R/[R+G+B]) using the mean value of red-green-blue intensity in eight locations around the optic disc and macular region. Optic disc area, ovality ratio, papillomacular angle, and retinal vessel angles and distances were quantified according to the data in our previous report. Using 54 fundus parameters, sex was predicted by L2 regularized binomial logistic regression for each grade.

RESULTS

The right eyes of 53 boys and 56 girls were analyzed. The discrimination accuracy rate significantly increased with age: 56.3% at 8.5 years, 46.1% at 9.5 years, 65.5% at 10.5 years and 73.1% at 11.5 years.

CONCLUSIONS

The accuracy of sex discrimination by fundus photography improved during a 3-year cohort study of elementary school students.

Prediction of visual field progression in glaucoma: existing methods and artificial intelligence

Timely treatment is essential in the management of glaucoma. However, subjective assessment of visual field (VF) progression is not recommended, because it can be unreliable. There are two types of artificial intelligence (AI) strong and weak (machine learning). Weak AIs can perform specific tasks. Linear regression is a method of weak AI. Using linear regression in the real-world clinic has enabled analyzing and predicting VF progression. However, caution is still required when interpreting the results, because whenever the number of VF data sets investigated is small, the predictions can be inaccurate. Several other non-ordinal, or modern AI methods have been constructed to improve prediction accuracy, such as clustering and more modern AI methods of Analysis with Non-Stationary Weibull Error Regression and Spatial Enhancement (ANSWERS), Variational Bayes Linear Regression (VBLR), Kalman Filter and sparse modeling (The least absolute shrinkage and selection operator regression: Lasso). It is also possible to improve the prediction performance using retinal thickness measured with optical coherence tomography by using machine learning methods, such as multitask learning.

Sex determination using color fundus parameters in older adults of Kumejima population study

PURPOSE

Deep learning artificial intelligence can determine the sex using only fundus photographs. However, the factors used by deep learning to determine the sex are not visible. Therefore, the purpose of the study was to determine whether the sex of an older individual can be determined by regression analysis of their color fundus photographs (CFPs).

METHODS

Forty-two parameters were analyzed by regression analysis using 1653 CFPs of normal subjects in the Kumajima study. The parameters included the mean values of red, green, and blue intensities; the tessellation fundus index; the optic disc ovality ratio; the papillomacular angle; and the retinal vessel angles. Finally, the L2 regularized binomial logistic regression was used to predict the sex using all the parameters, and the diagnostic ability was assessed through the leave-one-cross-validation.

RESULTS

The mean age of the 838 men and 815 women were 52.8 and 54.0 years, respectively. The ovality ratio and retinal artery angles in women were significantly smaller than that in men. The green intensity at all locations for the women were significantly higher than that of men (P < 0.001). The discrimination accuracy rate assessed by the area-under-the-curve was 80.4%.

CONCLUSIONS

Our methods can determine the sex from the CFPs of the adult with an accuracy of 80.4%. The ovality ratio, retinal vessel angles, tessellation, and the green intensities of the fundus are important factors to identify the sex in individuals over 40 years old.

The number of examinations required for the accurate prediction of the progression of the central 10-degree visual field test in glaucoma

The purpose of the study was to investigate the number of examinations required to precisely predict the future central 10-degree visual field (VF) test and to evaluate the effect of fitting non-linear models, including quadratic regression, exponential regression, logistic regression, and M-estimator robust regression model, for eyes with glaucoma. 180 eyes from 133 open angle glaucoma patients with a minimum of 13 Humphrey Field Analyzer 10-2 SITA standard VF tests were analyzed in this study. Using trend analysis with ordinary least squares linear regression (OLSLR), the first, second, and third future VFs were predicted in a point-wise (PW) manner using a varied number of prior VF sequences, and mean absolute errors (MAE) were calculated. The number of VFs needed to reach the minimum 95% confidence interval (CI) of the MAE of the OLSLR was investigated. We also examined the effect of applying other non-linear models. When predicting the first, second, and third future VFs using OLSLR, the minimum MAE was obtained using VF1-12 (2.15 ± 0.98 dB), VF1-11 (2.33 ± 1.10 dB), and VF1-10 (2.63 ± 1.36 dB), respectively. To reach the 95% CI of these MAEs, 10, 10, and 8 VFs were needed for the first, second and third future VF predictions, respectively. No improvement was observed by applying non-linear regression models. As a conclusion, approximately 8-10 VFs were needed to achieve an accurate prediction of PW VF sensitivity of the 10-degree central VF.

A Joint Multitask Learning Model for Cross-sectional and Longitudinal Predictions of Visual Field Using OCT

Purpose

We constructed a multitask learning model (latent space linear regression and deep learning [LSLR-DL]) in which the 2 tasks of cross-sectional predictions (using OCT) of visual field (VF; central 10°) and longitudinal progression predictions of VF (30°) were performed jointly via sharing the deep learning (DL) component such that information from both tasks was used in an auxiliary manner (The Association for Computing Machinery's Special Interest Group on Knowledge Discovery and Data Mining [SIGKDD] 2021). The purpose of the current study was to investigate the prediction accuracy preparing an independent validation dataset.

Design

Cohort study.

Participants

Cross-sectional training and testing data sets included the VF (Humphrey Field Analyzer [HFA] 10-2 test) and an OCT measurement (obtained within 6 months) from 591 eyes of 351 healthy people or patients with open-angle glaucoma (OAG) and from 155 eyes of 131 patients with OAG, respectively. Longitudinal training and testing data sets included 7984 VF results (HFA 24-2 test) from 998 eyes of 592 patients with OAG and 1184 VF results (HFA 24-2 test) from 148 eyes of 84 patients with OAG, respectively. Each eye had 8 VF test results (HFA 24-2 test). The OCT sequences within the observation period were used.

Methods

Root mean square error (RMSE) was used to evaluate the accuracy of LSLR-DL for the cross-sectional prediction of VF (HFA 10-2 test). For the longitudinal prediction, the final (eighth) VF test (HFA 24-2 test) was predicted using a shorter VF series and relevant OCT images, and the RMSE was calculated. For comparison, RMSE values were calculated by applying the DL component (cross-sectional prediction) and the ordinary pointwise linear regression (longitudinal prediction).

Main Outcome Measures

Root mean square error in the cross-sectional and longitudinal predictions.

Results

Using LSLR-DL, the mean RMSE in the cross-sectional prediction was 6.4 dB and was between 4.4 dB (VF tests 1 and 2) and 3.7 dB (VF tests 1–7) in the longitudinal prediction, indicating that LSLR-DL significantly outperformed other methods.

Conclusions

The results of this study indicate that LSLR-DL is useful for both the cross-sectional prediction of VF (HFA 10-2 test) and the longitudinal progression prediction of VF (HFA 24-2 test).

Predicting intraocular pressure using systemic variables or fundus photography with deep learning in a health examination cohort

The purpose of the current study was to predict intraocular pressure (IOP) using color fundus photography with a deep learning (DL) model, or, systemic variables with a multivariate linear regression model (MLM), along with least absolute shrinkage and selection operator regression (LASSO), support vector machine (SVM), and Random Forest: (RF). Training dataset included 3883 examinations from 3883 eyes of 1945 subjects and testing dataset 289 examinations from 289 eyes from 146 subjects. With the training dataset, MLM was constructed to predict IOP using 35 systemic variables and 25 blood measurements. A DL model was developed to predict IOP from color fundus photographs. The prediction accuracy of each model was evaluated through the absolute error and the marginal R-squared (mR2), using the testing dataset. The mean absolute error with MLM was 2.29 mmHg, which was significantly smaller than that with DL (2.70 dB). The mR2 with MLM was 0.15, whereas that with DL was 0.0066. The mean absolute error (between 2.24 and 2.30 mmHg) and mR2 (between 0.11 and 0.15) with LASSO, SVM and RF were similar to or poorer than MLM. A DL model to predict IOP using color fundus photography proved far less accurate than MLM using systemic variables.

Predicting the central 10 degrees visual field in glaucoma by applying a deep learning algorithm to optical coherence tomography images

We aimed to develop a model to predict visual field (VF) in the central 10 degrees in patients with glaucoma, by training a convolutional neural network (CNN) with optical coherence tomography (OCT) images and adjusting the values with Humphrey Field Analyzer (HFA) 24–2 test. The training dataset included 558 eyes from 312 glaucoma patients and 90 eyes from 46 normal subjects. The testing dataset included 105 eyes from 72 glaucoma patients. All eyes were analyzed by the HFA 10-2 test and OCT; eyes in the testing dataset were additionally analyzed by the HFA 24-2 test. During CNN model training, the total deviation (TD) values of the HFA 10-2 test point were predicted from the combined OCT-measured macular retinal layers’ thicknesses. Then, the predicted TD values were corrected using the TD values of the innermost four points from the HFA 24-2 test. Mean absolute error derived from the CNN models ranged between 9.4 and 9.5 B. These values reduced to 5.5 dB on average, when the data were corrected using the HFA 24-2 test. In conclusion, HFA 10-2 test results can be predicted with a OCT images using a trained CNN model with adjustment using HFA 24-2 test.

Sex judgment using color fundus parameters in elementary school students

PURPOSES

Recently, artificial intelligence has been used to determine sex using fundus photographs alone. We had earlier reported that sex can be distinguished using known factors obtained from color fundus photography (CFP) in adult eyes. However, it is not clear when the sex difference in fundus parameters begins. Therefore, we conducted this study to investigate sex determination based on fundus parameters using binominal logistic regression in elementary school students.

METHODS

This prospective observational cross-sectional study was conducted on 119 right eyes of elementary school students (aged 8 or 9 years, 59 boys and 60 girls). Through CFP, the tessellation fundus index was calculated as R/(R + G + B) using the mean value of red-green-blue intensity in the eight locations around the optic disc. Optic disc ovality ratio, papillomacular angle, retinal artery trajectory, and retinal vessel were quantified based on our earlier reports. Regularized binomial logistic regression was applied to these variables to select the decisive factors. Furthermore, its discriminative performance was evaluated using the leave-one-out cross-validation method. Sex difference in the parameters was assessed using the Mann-Whitney U test.

RESULTS

The optimal model yielded by the Ridge binomial logistic regression suggested that the ovality ratio of girls was significantly smaller, whereas their nasal green and blue intensities were significantly higher, than those of boys. Using this approach, the area under the receiver-operating characteristic curve was 63.2%.

CONCLUSIONS

Although sex can be distinguished using CFP even in elementary school students, the discrimination accuracy was relatively low. Some sex difference in the ocular fundus may begin after the age of 10 years.

Factors in Color Fundus Photographs That Can Be Used by Humans to Determine Sex of Individuals

Purpose

Artificial intelligence (AI) can identify the sex of an individual from color fundus photographs (CFPs). However, the mechanism(s) involved in this identification has not been determined. This study was conducted to determine the information in CFPs that can be used to determine the sex of an individual.

Methods

Prospective observational cross-sectional study of 112 eyes of 112 healthy volunteers. The following characteristics of CFPs were analyzed: the color of peripapillary area expressed by the mean values of red, green, and blue intensities, and the tessellation expressed by the tessellation fundus index (TFI). The optic disc ovality ratio, papillomacular angle, retinal artery trajectory, and retinal vessel angles were also quantified. Their differences between the sexes were assessed by Mann-Whitney U tests. Regularized binomial logistic regression was used to select the decisive factors. In addition, its discriminative performance was evaluated through the leave-one-out cross validation.

Results

The mean age of 76 men and 36 women was 25.8 years. The regularized binomial logistic regression delivered the optimal model for sex selected variables of peripapillary temporal green and blue intensities, temporal TFI, supratemporal TFI, optic disc ovality ratio, artery trajectory, and supratemporal retinal artery angle. With this approach, the discrimination accuracy rate was 77.9%.

Conclusions

Human-assessed characteristics of CFPs are useful in investigating the new theme proposed by AI, the sex of an individual.

Translational Relevance

This is the first report to approach the thinking process of AI by humans and can be a new approach to medical AI research.

Prediction of Glaucoma Progression with Structural Parameters: Comparison of Optical Coherence Tomography and Clinical Disc Parameters

PURPOSE

To test the hypothesis that baseline optical coherence tomography (OCT) measures predict visual field (VF) progression in a cohort of patients with suspected or established glaucoma and to compare their performance to semiquantitative optic disc measures.

DESIGN

This was an observational cohort study.

METHODS

The setting of this study was an academic institution. The study population included 171 eyes of 95 patients with good-quality baseline retinal nerve fiber layer (RNFL) and macular OCT images and disc photographs with >2 years of follow-up and ≥5 VFs. The observation procedures were baseline macular and RNFL OCT measures and cup-to-disc ratio and disc damage likelihood score. The main outcome measure was prediction of glaucomatous VF deterioration according to trend and event analyses.

RESULTS

Median (interquartile range) baseline mean deviation and follow-up were -2.9 (-6.4 to -1.1) dB and 54 (44-65) months, respectively. Seventeen and 25 eyes progressed by final visit based on pointwise event analysis and trend analysis of visual field index (VFI), respectively. Thinner central corneal thickness (P = .005), female gender (P = .015), and thinner average peripapillary RNFL (P = .001) predicted VF progression on proportional hazard models. Thinner RNFL at baseline (P = .006) or thinner average ganglion cell-inner plexiform layer (P = .028) along with higher baseline VFI (P = .018 and .048, respectively) predicted VFI progression. Neither optic disc measures predicted VF progression in any of the explored models.

CONCLUSIONS

Baseline structural OCT measures predicted subsequent VF progression in contrast to semi-quantitative optic disc measures. OCT-based structural measures should be included in prognostic models of glaucomatous VF deterioration.

Early Detection of Glaucomatous Visual Field Progression Using Pointwise Linear Regression With Binomial Test in the Central 10 Degrees

PURPOSE

We previously reported that it was beneficial to apply binomial pointwise linear regression (PLR) to detect 24-2 glaucomatous visual field (VF) progression, compared to mean deviation (MD) trend analysis and permutation analysis of PLR (PoPLR). The purpose of the current study was to validate the usefulness of the binomial PLR method to detect VF progression in the central 10 degrees in glaucoma patients.

DESIGN

Reliability assessment.

METHODS

A series of 15 VFs (Humphrey Field Analyzer 10-2 SITA-standard) from 97 eyes in 69 primary open-angle glaucoma patients, obtained over 8.5 ± 1.3 years (mean ± SD), were investigated. PLR was performed by regressing the total deviation of all test points on the series of 15 VFs. VF progression was determined from the analyses of VF test points using the binomial test (1-sided, P < .025). The time needed to detect VF progression was also investigated. The results were compared with PoPLR and MD trend analyses.

RESULTS

The binomial PLR was comparable to PoPLR and MD trend analyses in the positive predictive value (0.19 to 0.80), the negative predictive value (0.86 to 1.0), and the false positive rate (0.0 to 0.13) to evaluate glaucomatous VF progression. The time needed to detect VF progression (4.2 ± 1.8 years) was significantly shorter with the binomial PLR method compared with PoPLR and MD trend analysis (P = .04, P = .012, respectively).

CONCLUSIONS

The binomial PLR method detected glaucomatous VF progression in the central 10 degrees significantly earlier than PoPLR and MD trend analyses.

Comparing 10-2 and 24-2 Visual Fields for Detecting Progressive Central Visual Loss in Glaucoma Eyes with Early Central Abnormalities

Purpose

To compare the ability of 10-2 visual field tests and central 12 locations of the 24-2 tests (C24-2) to detect central visual field progression in glaucoma eyes with early central visual field abnormalities.

Design

Observational cohort study.

Participants

Three-hundred eyes of 180 participants with glaucoma or ocular hypertension.

Methods

Participants with both 10-2 and 24-2 tests performed on ≥3 visits over ≥1-year period were included to estimate the longitudinal variability of 10-2 and C24-2 visual field mean deviation (MD). The variability estimates were then used to reconstruct real-world visual field results by computer simulations, in a scenario where eyes had a baseline 10-2 and C24-2 MD was -2 dB and exhibited various rates of change (-0.25, -0.50, -0.75 and -1.00 dB/year), and the time to detect these changes were evaluated using trend-based analyses.

Main Outcome Measures

Time required to detect progression.

Results

Overall, the time to detect central visual field progression was reduced by 7-9% using the 10-2 compared to C24-2 MD values, equivalent to a total reduction of 0.1-0.3 dB lost. For example, 90% of eyes with a central 10-2 or C24-2 MD loss of -0.50 dB/year would be detected after 5.0 and 5.5 years of semi-annual testing respectively, or after 3.4 and 3.7 years respectively for eyes with a -1.00 dB/year loss.

Conclusions

Trend-based analyses using 10-2 MD resulted in a mild reduction (7-9%) in the time to detect central visual field progression compared to C24-2 MD in glaucoma eyes with early central visual field abnormalities. Further studies are needed to determine whether other progression analyses can better exploit the increased sampling of 10-2 tests. These findings provide evidence-based guidance on the potential value-add of 10-2 testing in the clinical management of glaucoma patients.

Applying "Lasso" Regression to Predict Future Glaucomatous Visual Field Progression in the Central 10 Degrees

PURPOSE OF THE STUDY

We recently reported that it is beneficial to apply least absolute shrinkage and selection operator (Lasso) regression to predict future 24-2 visual field (VF) progression. The purpose of the current study was to investigate the usefulness of Lasso regression to predict VF progression in the central 10 degrees (10-2) in glaucoma patients.

METHODS

Series of 10 VFs (Humphrey Field Analyzer 10-2 SITA-standard) from each of 149 eyes in 110 open angle glaucoma patients, obtained over 5.7±1.4 years (mean±SD) were investigated. Mean deviation values of the 10th VF were predicted using varying numbers of VFs (ranging from the first to third VFs to the first to ninth VFs), applying ordinary least square regression (OLSLR) and Lasso regression. Absolute prediction errors were then compared.

RESULTS

With OLSLR, prediction error varied between 5.4±5.0 (using first to third VFs) and 1.1±1.6 dB (using first to ninth VFs). Significantly smaller prediction errors were obtained with Lasso regression, in particular with small numbers of VFs (from 2.1±2.8: first to third VFs, to 1.0±1.6 dB: first to ninth VFs). A large λ value, which is an index showing the degree of penalty in Lasso regression, was observed when a small number of VFs were used for prediction.

CONCLUSION

Mean deviation prediction using OLSLR with a small number of VFs resulted in large prediction errors. It was useful to apply Lasso regression when predicting future progression of the central 10 degrees, compared to OLSLR.

Predicting Future Self-Reported Motor Vehicle Collisions in Subjects with Primary Open-Angle Glaucoma Using the Penalized Support Vector Machine Method

PURPOSE

We predict the likelihood of a future motor vehicle collision (MVC) from visual function data, attitudes to driving, and past MVC history using the penalized support vector machine (pSVM) in subjects with primary open-angle glaucoma (POAG).

METHODS

Patients with POAG were screened prospectively for eligibility and 185 were analyzed in this study. Self-reported MVCs of all participants were recorded for 3 years from the baseline using a survey questionnaire every 12 months. A binocular integrated visual field (IVF) was calculated for each patient by merging a patient's monocular Humphrey Field Analyzer (HFA) visual fields (VFs). The IVF was divided into six regions, based on eccentricity and the right or left hemifield, and the average of the total deviation (TD) values in each of these six areas was calculated. Then, the future MVCs were predicted using various variables, including age, sex, 63 variables of 52 TD values, mean of the TD values, visual acuities (VAs), six sector average TDs with (pred_{penSVM_all}) and without (pred_{penSVM_basic}) the attitudes in driving, and also past MVC history, using the pSVM method, applying the leave-one-out cross validation.

RESULTS

The relationship between pred_{penSVM_basic} and the future MVC approached significance (odds ratio = 1.15, [0.99-1.29], P = 0.064, logistic regression). A significant relationship was observed between pred_{penSVM_all} and the future MVC (odds ratio = 1.21, P = 0.0015).

CONCLUSIONS

It was useful to predict future MVCs in patients with POAG using visual function metrics, patients' attitudes to driving, and past MVC history, using the pSVM.

TRANSLATIONAL RELEVANCE

Careful consideration is needed when predicting future MVCs in POAG patients using visual function, and without driving attitude and MVC history.

Validating the Usefulness of the "Random Forests" Classifier to Diagnose Early Glaucoma With Optical Coherence Tomography

PURPOSE

To validate the usefulness of the "Random Forests" classifier to diagnose early glaucoma with spectral-domain optical coherence tomography (SDOCT).

METHODS

design: Comparison of diagnostic algorithms.

SETTING

Multiple institutional practices.

STUDY PARTICIPANTS

Training dataset included 94 eyes of 94 open-angle glaucoma (OAG) patients and 84 eyes of 84 normal subjects and testing dataset included 114 eyes of 114 OAG patients and 82 eyes of 82 normal subjects. In both groups, OAG eyes with mean deviation (MD) values better than -5.0 dB were included.

OBSERVATION PROCEDURE

Using the training dataset, classifiers were built to discriminate between glaucoma and normal eyes using 84 OCT measurements using the Random Forests method, multiple logistic regression models based on backward or bidirectional stepwise model selection, a least absolute shrinkage and selection operator regression (LASSO) model, and a Ridge regression model.

MAIN OUTCOME MEASURES

Diagnostic accuracy.

RESULTS

With the testing data, the area under the receiver operating characteristic curve (AROC) with the Random Forests method (93.0%) was significantly (P < .05) larger than those with other models of the stepwise model selections (71.9%), LASSO model (89.6%), and Ridge model (89.2%).

CONCLUSION

It is useful to analyze multiple SDOCT parameters concurrently using the Random Forests method to diagnose glaucoma in early stages.