About

Linda M. Zangwill is a Professor In Residence in the Department of Ophthalmology at UCSD. Her research focuses on glaucoma and vision science, with extensive involvement in clinical trials and translational research related to ocular hypertension, glaucoma progression, and diagnostic innovations. She has played a principal role in numerous NIH-funded projects, including long-term follow-up studies and investigations into the structural and functional assessment of the optic nerve and retinal nerve fiber layer. Her work emphasizes the development and application of advanced imaging techniques, such as OCT, and the integration of artificial intelligence models for glaucoma detection and progression prediction. Dr. Zangwill's contributions significantly advance understanding of glaucoma pathophysiology, risk assessment, and early detection strategies.

Research topics

• Medicine
• Computer Science
• Ophthalmology
• Artificial Intelligence
• Optometry
• Biology
• Machine Learning
• Internal medicine
• Computer vision
• Evolutionary biology
• Demography
• Cell biology
• Engineering
• Algorithm
• Genetics
• Endocrinology
• Neuroscience
• Biochemistry
• Surgery
• Materials science

Selected publications

• A Manual of Procedures for the Generation of the AI-Ready and Exploratory Atlas for Diabetes Insights (AI-READI) Database

  medRxiv · 2026-04-04

  articleOpen access

  The ability to understand and affect the course of complex, multi-system diseases like diabetes has been limited by a lack of well-designed, high-quality and large multimodal datasets. The NIH Bridge2AI AI-READI project (aireadi.org) aims to address this shortfall by generating an AI-ready dataset to support AI discoveries in type 2 diabetes mellitus (T2DM). This manual of procedures provides a detailed description of the AI-READI protocol.

  PublisherOA PDFDOI

• Glaucomatous Remodeling of the Lamina Cribrosa: Association With Visual Field Progression

  Investigative Ophthalmology & Visual Science · 2026-04-01

  articleOpen access

  Purpose: To determine whether rates of change in lamina cribrosa (LC) depth and curvature in POAG patients are associated with rates of visual field (VF) progression. Methods: We used 24 radial B-scan spectral domain optic coherence tomographic images to define anterior LC surface depth (ALCSD) and LC curvature index (LCCI), which were segmented by deep learning. Linear mixed effects models tested the associations between VF progression rate, ALCSD, LCCI, and demographic and clinical characteristics. Results: Progressive posterior remodeling of LC (i.e., increasing depth; -0.095 dB/year/10 microns; P < 0.001) and increasing LCCI (i.e., posterior bowing; -0.110 dB/year/unit; P < 0.001) each independently predicted faster rates of VF progression. The same degree of ALCSD change was associated with faster progression in eyes that were younger (P = 0.038), had higher central corneal thickness (CCT) (P < 0.001), or had higher mean IOP (P = 0.030). Similarly, the same amount of LCCI change was correlated with greater baseline age (P < 0.001) and lower CCT (P < 0.001). Thus, functionally progressive glaucoma was associated with less change in ALCSD and LCCI in older subjects and subjects with thicker CCT and lower mean IOP over the follow-up period. Conclusions: Glaucomatous remodeling of the LC is associated with VF progression in POAGs. The association between longitudinal structural remodeling and functional progression varies with age such that older individuals exhibit shallower remodeling (less posterior change in ALCSD and LCCI). Subjects with a higher IOP and thinner CCT showed greater posterior remodeling of the LC as glaucoma functionally progressed, indicating that these factors are mechanistically relevant to glaucomatous remodeling.

  PublisherOA PDFDOI

• Association of Deep Optic Nerve Head Structural Remodeling with Choroidal Microvasculature Dropout in Glaucoma with and without Myopia

  American Journal of Ophthalmology · 2026-04-15

  articleSenior author
  PublisherDOI

• Retinal nerve fibre layer optical texture analysis: retinal nerve fibre bundle defect patterns and the extent of macular involvement across different stages of glaucoma

  British Journal of Ophthalmology · 2025-11-04 · 2 citations

  articleOpen access

  BACKGROUND/AIMS: To apply retinal nerve fibre layer (RNFL) optical texture analysis (ROTA) to investigate (1) the patterns of RNFL bundle defects, and (2) the frequency of papillomacular and papillofoveal bundle involvement across early, moderate and advanced glaucoma. METHODS: All eyes underwent 24-2 visual field (VF) testing and optical coherence tomography (OCT) for ROTA. The borders of RNFL defects were delineated from ROTA, and the involvement of the arcuate, papillomacular and papillofoveal bundles was determined for each eye. 24-2 VF stimulus projections were mapped onto the corresponding topographic areas of ROTA images. Multilevel logistic regression analysis was applied to evaluate the structure-function association. RESULTS: Papillomacular bundle defects were highly prevalent in glaucoma, increasing from 87.7% in early to 95.35% in moderate and 100% in advanced glaucoma. Papillofoveal bundle defects were also common, increasing from 29.7% in early to 36.05% in moderate and 60.98% in advanced glaucoma. Central four 24-2 test locations that projected onto the trajectories of papillomacular or papillofoveal RNFL bundle defects demonstrated significantly increased likelihood of VF sensitivity abnormality (ORs of 22.42 at PDP<5% and 20.26 at TDP<5%, respectively, p<0.001 for both). CONCLUSION: ROTA uncovers a wide spectrum of RNFL bundle defects spanning the entire glaucoma continuum. It also provides visualisation of the preserved RNFL bundles in advanced glaucoma. Papillomacular and papillofoveal RNFL bundle defects are present in a considerable proportion of eyes with early, moderate and advanced glaucoma, and, when detected, they significantly increase the likelihood of abnormality in the corresponding central 24-2 test locations.

  PublisherOA PDFDOI

• Optic Disc Size and Circumpapillary Retinal Nerve Fiber Layer Thinning in Glaucoma

  Ophthalmology Glaucoma · 2025-02-21 · 2 citations

  article
  PublisherDOI

• Longitudinal Measurement of Optic Disc Vessel Density to Detect Glaucoma Progression in High Myopia

  Ophthalmology · 2025-08-05 · 1 citations

  articleSenior author
  PublisherDOI

• Impact of Physical Activity Levels on Visual Field Progression in Individuals With Glaucoma

  Journal of Glaucoma · 2025-04-18 · 1 citations

  articleCorresponding

  PRÉCIS: Higher self-reported physical activity level was associated with a slower rate of visual field mean deviation loss in patients with primary open angle glaucoma. PURPOSE: To determine the impact of physical activity (PA) on visual field (VF) progression rates in patients with primary open angle glaucoma (POAG). METHODS: In this longitudinal study, POAG patients were included who had ≥5 visits, ≥2 years of follow-up VFs and underwent PA questionnaire at the baseline. PA levels were assessed using the physical activity index (PAI), metabolic equivalents of task (MET)-minutes, and walking pace. Univariable and multivariable linear mixed-effects models were used to determine the impact of PA levels on the rates of VF mean deviation (MD) loss. RESULTS: One hundred thirty-one eyes from 80 POAG patients were included over a median follow-up of 4.9 (IQR: 4.0-6.7) years. The median age of patients was 68.6 (IQR: 59.3-77.8) years and the median baseline VF MD was -3.5 (IQR: -8.3 to -1.3). In the univariable analysis, slower VF MD loss was associated with active PAI category (0.30 [95% CI: 0.01-0.58] dB/year vs. inactive PAI category; P =0.041) and higher PA amount (0.14 [95% CI: 0.01-0.27] dB/year per 1000 MET-minutes; P =0.036). Significant association with the rate of VF MD loss was not found for baseline VF MD ( P =0.263) and walking pace ( Ps >0.05). In the multivariable analysis including glaucoma severity and other covariates, slower VF MD loss was associated with higher PA amounts (0.15 [95% CI: 0.02-0.28] dB/year per 1000 MET-minutes; P =0.024). CONCLUSIONS: Higher PA amounts are an independent predictor of a slower rate of VF MD loss. Further research is needed to explore whether increased PA protects against glaucoma progression.

  PublisherDOI

• A Novel Multimodal Implementation of a Foundation Artificial Intelligence Model Using Optic Nerve Head Fundus Photographs and OCT Imaging for Glaucoma Detection

  Ophthalmology Science · 2025-11-20

  articleOpen access

  Purpose: To compare the performance of unimodal and multimodal implementation of the self-supervised learning model RETFound in detecting glaucoma using color fundus photographs (CFPs) and OCT images, and to assess its generalizability across different ethnicities, age groups, and disease severities. Design: Evaluation of a diagnostic technology. Subjects Participants and Controls: Fourteen thousand five hundred ten CFPs and 32 640 OCTs from 1948 eyes of 1098 participants (60.8% glaucoma, 39.2% healthy) from the Diagnostic Innovations in Glaucoma Study and the African Descent and Glaucoma Evaluation Study were included. Glaucoma was defined as photograph-based glaucomatous optic neuropathy with or without repeatable glaucoma visual field damage. Methods: A multimodal RETFound model was developed using paired CFPs and OCT images. The model was compared to unimodal RETFound models using solely CFP or OCT images. Performance was also stratified by race (Black vs. White), age (<60 vs. ≥60 years), and disease severity (mild vs. moderate-to-severe glaucoma). Main Outcome Measures: Diagnostic accuracy of unimodal and multimodal RETFound models using CFP and OCT for detecting glaucoma was assessed using the area under the receiver operating characteristic curve (AUC), precision, and recall. Results: = 0.005) models. Conclusions: The multimodal RETFound model demonstrated improved diagnostic ability compared with the CFP unimodal model but did not significantly outperform the OCT unimodal model in glaucoma detection. As clinical implementation of a unimodal artificial intelligence (AI) model is easier than a multimodal counterpart, our results suggest unimodal OCT AI models may be sufficient for detecting glaucoma. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

  PublisherDOI

• 1048-P: Impact of Social Determinants of Health on Diabetes Status—A Multivariable Analysis Using Artificial Intelligence Ready and Equitable Atlas for Diabetes Insights (AI-READI) Dataset

  Diabetes · 2025-06-13

  article

  Introduction and Objective: Social determinants of health (SDoH) are non-medical factors that influence health outcomes. The purpose of this study is to investigate the relationship between diabetes severity and SDoH indicators such as access to healthcare and education. Methods: AI-READI is an ongoing data generation project in type 2 diabetes mellitus. Diabetes severity was defined as, in order, healthy, pre-diabetes, oral medication/non-insulin controlled, and insulin controlled. Financial access to healthcare and education were evaluated using PhenX surveys. Ordinal regression was performed using diabetes status as the outcome and SDoH as predictors, which were adjusted by age, waist-to-hip ratio and systemic diseases such as stroke. Results: A total of 808 participants (Median age 60) were included in the analysis. Financial barriers to healthcare access, such as challenges in affording prescriptions and medical care, were significantly associated with higher diabetes severity (Figure). Small differences in educational levels were noted among diabetes status but varied in statistical significance (Figure). Conclusion: Several indicators of higher financial barriers to healthcare access and slightly lower levels of education appear to be associated with higher levels of diabetes severity. Disclosure A. Motoyoshi: None. Y. Jiang: None. S.L. Baxter: Consultant; Topcon. L.M. Zangwill: Consultant; AbbVie Inc, Topcon Medical Systems. Research Support; Heidelberg Engineering, Carl Zeiss Meditec, Optomed, Icare Inc, Optovue. Stock/Shareholder; AI Sight Health. G. McGwin: None. C. Owsley: Consultant; Johnson &amp; Johnson Medical Devices Companies, Sanofi-Aventis U.S. A.Y. Lee: Consultant; Genentech, Santen, Sanofi, Johnson and Johnson, Boehringer Ingelheim. Research Support; iCareWorld, Topcon, Carl Zeiss Medictec, Optomed, Heidelberg, Microsoft, Amazon, Meta. C.S. Lee: None. Funding National Institute of Health grants (OT2OD032644, R01AG060942); The Karalis Johnson Retina Center Research to Prevent Blindness to University of Washington; University of California San Diego; University of Alabama at Birmingham

  PublisherDOI

• Glaucoma detection in myopic eyes using deep learning autoencoder-based regions of interest

  Frontiers in Ophthalmology · 2025-08-04 · 2 citations

  articleOpen accessSenior authorCorresponding

  Purpose: To evaluate the diagnostic accuracy of a deep learning autoencoder-based model utilizing regions of interest (ROI) from optical coherence tomography (OCT) texture enface images for detecting glaucoma in myopic eyes. Methods: This cross-sectional study included a total of 453 eyes from 315 participants from the multi-center "Swept-Source OCT (SS-OCT) Myopia and Glaucoma Study", composed of 268 eyes from 168 healthy individuals and 185 eyes from 147 glaucomatous individuals. All participants underwent swept-source optical coherence tomography (SS-OCT) imaging, from which texture enface images were constructed and analyzed. The study compared four methods: (1) global RNFL thickness, (2) texture enface image, (3) a single autoencoder model trained only on healthy eyes, and (4) a dual autoencoder model trained on both healthy and glaucomatous eyes. Diagnostic accuracy was assessed using the area under the receiver operating curves (AUROC) and precision recall curves (AUPRC). Results: The dual autoencoder model achieved the highest AUROC (95% CI) (0.92 [0.88, 0.95]), significantly outperforming the single autoencoder model trained only on healthy eyes (0.86 [0.83, 0.88], p = 0.01), the global RNFL thickness model (0.84 [0.80, 0.86], p = 0.003), and the texture enface model (0.83 [0.79, 0.85], p = 0.005). Using AUPRC (95% CI), the dual autoencoder model (0.86 [0.83, 0.89]) also outperformed the single autoencoder model trained only on healthy eyes (0.80 [0.78, 0.82], p = 0.02), the global RNFL thickness model (0.74 [0.70, 0.76], p = 0.001), and the texture enface model (0.71 [0.68, 0.73], p<0.001). No significant difference was observed between the global RNFL thickness measurement and the texture enface measurement (p = 0.47). Discussion: The dual autoencoder model, which integrates reconstruction errors from both healthy and glaucomatous training data, demonstrated superior diagnostic accuracy compared to the single autoencoder model, global RNFL thickness and texture enface-based approaches. These findings suggest that deep learning models leveraging ROI-based reconstruction error from texture enface images may enhance glaucoma classification in myopic eyes, providing a robust alternative to conventional structural thickness metrics.

  PublisherOA PDFDOI

Recent grants

• NIH Grant R01EY011008

  NIH · $9.9M · 2017

• Multimodal Artificial Intelligence to Predict Glaucomatous Progression and Surgical Intervention

  NIH · $1.8M · 2022–2026

• Bridge2AI: Salutogenesis Data Generation Project

  NIH · $32.7M · 2022–2026

• African Descent and Glaucoma Evaluation (ADAGES) IV: Alterations of the lamina cribrosa in progression

  NIH · $2.6M · 2017–2022

• NIH Grant U10EY014267

  NIH · $13.2M · 2013

Frequent coauthors

• Robert N. Weinreb

  University of California, San Diego

  997 shared

• Christopher Bowd

  Fleet Science Center

  489 shared

• Felipe A. Medeiros

  University of Miami

  436 shared

• Sasan Moghimi

  University of California, San Diego

  268 shared

• Jeffrey M. Liebmann

  243 shared

• Christopher A. Girkin

  University of Alabama at Birmingham

  238 shared

• Pamela A. Sample

  University of California, San Diego

  218 shared

• Akram Belghith

  University of California, San Diego

  174 shared

Labs

• UCSD OphthalmologyPI

Education

• Ph.D., Ophthalmology

  University of California, San Diego

  1990

• M.D.

  University of California, San Diego

  1985

• B.A., Psychology

  University of California, San Diego

  1981