Wilmer Glaucoma ML

Our lab focuses on developing and assessing machine learning (ML) techniques that strive to reduce the risk of glaucoma-related vision loss. We are an interdisciplinary group of faculty, research associates, and students from the Wilmer Eye Institute and the Malone Center for Engineering in Healthcare at Johns Hopkins.

Selected Publications

Assessment of linear regression of peripapillary optical coherence tomography retinal nerve fiber layer measurements to forecast glacuoma trajectory

Chris Bradley, Kaihua Hou, Patrick Herbert, and 5 more authors

PloS One 2024

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Linear regression of optical coherence tomography measurements of peripapillary retinal nerve fiber layer thickness is often used to detect glaucoma progression and forecast future disease course. However, current measurement frequencies suggest that clinicians often apply linear regression to a relatively small number of measurements (e.g., less than a handful). In this study, we estimate the accuracy of linear regression in predicting the next reliable measurement of average retinal nerve fiber layer thickness using Zeiss Cirrus optical coherence tomography measurements of average retinal nerve fiber layer thickness from a sample of 6,471 eyes with glaucoma or glaucoma-suspect status. Linear regression is compared to two null models: no glaucoma worsening, and worsening due to aging. Linear regression on the first M ≥ 2 measurements was significantly worse at predicting a reliable M+1st measurement for 2 ≤ M ≤ 6. This range was reduced to 2 ≤ M ≤ 5 when retinal nerve fiber layer thickness measurements were first "corrected" for scan quality. Simulations based on measurement frequencies in our sample-on average 393 ± 190 days between consecutive measurements-show that linear regression outperforms both null models when M ≥ 5 and the goal is to forecast moderate (75th percentile) worsening, and when M ≥ 3 for rapid (90th percentile) worsening. If linear regression is used to assess disease trajectory with a small number of measurements over short time periods (e.g., 1-2 years), as is often the case in clinical practice, the number of optical coherence tomography examinations needs to be increased.
The impact of achieving target intraocular pressure on glaucomatous retinal nerve fiber layer thinning in a treated clinical population

Alex T. Pham, Chris Bradley, Kaihua Hou, and 4 more authors

American Journal of Ophthalmology Nov 2023

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Estimate the effect of being below and above the clinician-set target intraocular pressure (IOP) on rates of glaucomatous retinal nerve fiber layer (RNFL) thinning in a treated real-world clinical population.
Forecasting risk of future rapid glaucoma worsening using early visual field, Optical Coherence Tomography, and clinical data

Patrick Herbert, Kaihua Hou, Chris Bradley, and 5 more authors

Ophthalmol. Glaucoma Mar 2023

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PURPOSE: Assess whether we can forecast future rapid visual field (VF) worsening using deep learning models (DLMs) trained on early VF, OCT, and clinical data. DESIGN: Retrospective cohort study. SUBJECTS: 4,536 eyes from 2,962 patients. 263 (5.80%) of eyes underwent rapid VF worsening (MD slope <-1dB/yr across all VFs). METHODS: We included eyes that met the following criteria: 1) followed for glaucoma or suspect status 2) had at least five longitudinal reliable VFs (VF1, VF2, VF3, VF4, VF5) 3) had one reliable baseline Optical Coherence Tomography (OCT) scan (OCT1) and one set of baseline clinical measurements (Clinical1) at the time of VF1. We designed a DLM to forecast future rapid VF worsening. The input consisted of spatially oriented total deviation values from VF1 (including or not including VF2 and VF3 in some models) and retinal nerve fiber layer thickness values from the baseline OCT. We passed this VF/OCT stack into a vision transformer feature extractor, the output of which was concatenated with baseline clinical data before putting it through a linear classifier to predict that eye’s risk of rapid VF worsening across the five VFs. We compared the performance of models with differing inputs by computing area under receiver operating curve (AUC) in the test set. Specifically, we trained models with the following inputs: Model V: VF1; VC: VF1+ Clinical1; VO: VF1+ OCT1; VOC: VF1+ Clinical1+ OCT1; V2: VF1 + VF2; V2OC: VF1 + VF2 + Clinical1 + OCT1; V3: VF1 + VF2 + VF3; V3OC: VF1 + VF2 + VF3 + Clinical1 + OCT1. MAIN OUTCOME MEASURES: AUC of DLMs when forecasting rapidly worsening eyes. RESULTS: Model V3OC best forecasted rapid worsening with an AUC (95% CI) of 0.87 (0.77, 0.97). Remaining models in descending order of performance and their respective AUC [95% CI] were: Model V3 (0.84 [0.74 to 0.95]), Model V2OC (0.81 [0.70 to 0.92]), Model V2 (0.81 [0.70 to 0.82]), Model VOC (0.77 [0.65, 0.88]), Model VO [0.75 [0.64, 0.88], Model VC (0.75 [0.63, 0.87]), Model V (0.74 [0.62, 0.86]). CONCLUSION: DLMs can forecast future rapid glaucoma worsening with modest to high performance when trained using data from early in the disease course. Including baseline data from multiple modalities and subsequent visits improves performance beyond using VF data alone.