About

Shyamanga Borooah is an Associate Professor of Clinical Ophthalmology at UC San Diego School of Medicine, located at 9500 Gilman Drive, La Jolla, CA. His research focuses on various aspects of retinal diseases, including retinal dystrophies, age-related macular degeneration, diabetic retinopathy, and inherited retinal conditions. Borooah's work involves the application of artificial intelligence and multimodal imaging techniques to diagnose, characterize, and understand the progression of retinal disorders. His contributions include studying the natural history of retinal diseases, evaluating clinical utility of large language models in ophthalmology, and investigating structural and functional retinal changes through advanced imaging methods. His research aims to improve diagnostic accuracy and develop effective treatments for retinal degenerative diseases.

Research topics

• Medicine
• Biology
• Ophthalmology
• Pathology
• Internal medicine
• Optometry
• Genetics
• Surgery
• Neuroscience
• Pediatrics
• Demography

Selected publications

• Reply

  Ophthalmology Science · 2026-01-09

  articleOpen accessSenior author
  PublisherOA PDFDOI

• Comprehensive Ocular Characteristics in Cystinosis after Hematopoietic Stem-Cell Gene Therapy Over 24 Months

  American Journal of Ophthalmology · 2026-02-09

  article
  PublisherDOI

• Bi-allelic variants in FSD1L cause retinitis pigmentosa with or without neurological involvement

  The American Journal of Human Genetics · 2026-02-19

  articleOpen access

  Retinitis pigmentosa (RP) is an inherited retinal disease (IRD) characterized usually by progressive photoreceptor degeneration, leading to night blindness, peripheral visual field loss, and can progress to central vision impairment in some individuals. Despite advances in genomic diagnostics, many individuals with RP remain without a molecular diagnosis. We identified bi-allelic ultra-rare variants in fibronectin type II and Spry domain-containing protein 1-like (FSD1L) in six individuals with RP with or without neurological features from four unrelated families. FSD1L encodes a cytoplasmic protein, variants of which have not previously been associated with Mendelian disease. The gene is expressed in both human and mouse retinas that are enriched in cone and rod photoreceptors. Immunofluorescence and ultrastructure expansion microscopy show that FSD1L localizes along the photoreceptor microtubule axoneme, including the connecting cilium and outer segment, supporting a possible role in intracellular trafficking. A retina-enriched isoform of FSD1L includes an alternatively spliced exon (exon 10b), which we characterize as absent in minigene assays and affected individual-derived lymphocytes due to a deep intronic 26 nt deletion. Together, these findings support the association between bi-allelic disruption of FSD1L and IRD.

  PublisherDOI

• Pointwise Structure–Function Analysis of the Ellipsoid Zone in Retinitis Pigmentosa Using an Artificial Intelligence-Assisted OCT and Microperimetry Overlay

  Ophthalmology Science · 2025-07-22

  articleOpen accessSenior author

  Objective: To perform a pointwise structure-function analysis of the ellipsoid zone (EZ) in retinitis pigmentosa (RP) using an artificial intelligence-based overlay to understand EZ structure-function relationships. Design: A single-center retrospective study. Subjects: Patients with clinically confirmed RP. Methods: Same-day spectral-domain OCT (SD-OCT) and microperimetry near-infrared images were overlaid in patients with confirmed RP. Overlay used a coarse alignment artificial intelligence model. Each locus, on a 68-point microperimetry grid spanning the central 20° of the macula, was identified on individual SD-OCT B-scans. Ellipsoid zone structure was graded at each locus on a 3-point scale: grade 0 = EZ not visible; grade 1 = EZ attenuated; grade 2 = EZ normal. Ellipsoid zone grades were correlated with microperimetry sensitivity scores recorded in decibels (dB). Main Outcome Measures: Correlation of EZ integrity on SD-OCT with microperimetric retinal sensitivity. Results: < 0.001). Correlation between EZ grade and sensitivity was 0.65 (0.64-0.67), whereas correlation of sensitivity with distance from the fovea was -0.41 (-0.43 to -0.39). Focusing on grade 0 loci, 57.5% had sensitivity scores >0 dB, and 4% had scores ≥20 dB, suggesting that these points had function despite no observable EZ on SD-OCT. Conclusions: We identified local EZ structure-function incongruencies in RP using a pointwise analysis of structure-function overlay. These loci of interest may be overlooked in analyses that average across the visual field. Preserved photoreceptor function, in the absence of visibly intact EZ, warrants further investigation. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

  PublisherOA PDFDOI

• Expanding the Phenotype of Syndromic <scp> <i>SLC30A9</i> </scp> ‐Associated Disease

  American Journal of Medical Genetics Part A · 2025-11-26

  articleSenior author

  SLC30A9 mutations are linked to Birk-Landau-Perez syndrome, which is characterized by neurodevelopmental and renal disease, thought to result from impaired zinc homeostasis. In this report, we describe a patient with a homozygous likely pathogenic SLC30A9 variant with atypical chorio-retinal degeneration, suggesting retinal involvement in SLC30A9-associated diseases. The patient has bilateral sensorineural hearing loss, developmental delay, intellectual disability, abnormal balance, and Tourette syndrome. Ophthalmic manifestations include vascular attenuation, optic disc pallor, and pigmentation. In addition, the patient is noted to have high myopia. Our case highlights the importance of broad genetic testing in diagnosing rare multi-systemic disorders. Further research into the molecular mechanisms by which SLC30A9 results in photoreceptor disease is essential to understand its role in retinal degeneration and to develop potential therapeutic strategies.

  PublisherDOI

• Evaluating the clinical utility of multimodal large language models for detecting age-related macular degeneration from retinal imaging

  Scientific Reports · 2025-09-26 · 3 citations

  articleOpen accessSenior author

  This single-center retrospective study evaluated the performance of four multimodal large language models (MLLMs) (ChatGPT-4o, Claude 3.5 Sonnet, Google Gemini 1.5 Pro, Perplexity Sonar Large) in detecting and grading the severity of age-related macular degeneration (AMD) from ultrawide field fundus images. Images from 76 patients (136 eyes; mean age 81.1 years; 69.7% female) seen at the University of California San Diego were graded independently for AMD severity by two junior retinal specialists (and an adjudicating senior retina specialist for disagreements) using the Age-Related Eye Disease Study (AREDS) classification. The cohort included 17 (12.5%) eyes with 'No AMD', 18 (13.2%) with 'Early AMD', 50 (36.8%) with 'Intermediate AMD', and 51 (37.5%) with 'Advanced AMD'. Between December 2024 and February 2025, each MLLM was prompted with single images and standardized queries to assess the primary outcomes of accuracy, sensitivity, and specificity in binary disease classification, disease severity grading, open-ended diagnosis, and multiple-choice diagnosis (with distractor diseases). Secondary outcomes included precision, F1 scores, Cohen's kappa, model performance comparisons, and error analysis. ChatGPT-4o demonstrated the highest accuracy for binary disease classification [mean 0.824 (95% confidence interval (CI)): 0.743, 0.875)], followed by Perplexity Sonar Large [mean 0.815 (95% CI: 0.744, 0.879)], both of which were significantly more accurate (P < 0.00033) Than Gemini 1.5 Pro [mean 0.669 (95% CI: 0.581, 0.743)] and Claude 3.5 Sonnet [mean 0.301 (95% CI: 0.221, 0.375)]. For severity grading, Perplexity Sonar Large was most accurate [mean 0.463 (95% CI: 0.368, 0.537)], though differences among models were not statistically significant. ChatGPT-4o led in open-ended and multiple-choice diagnostic tasks. In summary, while MLLMs show promise for automated AMD detection and grading from fundus images, their current reliability is insufficient for clinical application, highlighting the need for further model development and validation.

  PublisherOA PDFDOI

• USING ARTIFICIAL INTELLIGENCE TO ASSESS MACULAR EDEMA TREATMENTS IN RETINITIS PIGMENTOSA

  Retina · 2025-07-31

  articleSenior author

  PURPOSE: This study validates a deep learning-based artificial intelligence tool for quantifying macular edema (ME) intraretinal fluid (IRF) volumes in retinitis pigmentosa, and through longitudinal analysis of IRF, provides new insight into treatment efficacy and disease natural history. METHODS: This retrospective, longitudinal study identified patients with retinitis pigmentosa with ME. A commercially available retinal analysis tool quantified IRF and was validated for segmentation of ME using spectral-domain optical coherence tomography volume scans. Baseline analysis of IRF versus traditional central subfield thickness and longitudinal analyses of IRF versus treatment and best-corrected visual acuity were performed. RESULTS: Forty-four patients were identified. For treatment studies, 52 eyes were in the treated group and 14 eyes in the untreated ME group. Mean follow-up was 5.3 exams (3.7, 6.9) over 2.3 years (1.7, 3.0). Software validation compared automated and manual IRF segmentation of 490 image pairs, finding a Dice coefficient of 0.928 (95% confidence interval: 0.92‒0.99). Cohort mean IRF volume was 230.85 nL (57.42, 403.91) at baseline. Intraretinal fluid change in eyes treated with topical carbonic anhydrase inhibitors was -2.1 nL/year ( P = 0.81). Oral acetazolamide-treated eyes had significant IRF reduction (-33.6 nL/year, P = 0.009) and significant improvements in best-corrected visual acuity (logMAR/year; Early Treatment Diabetic Retinopathy Study letters equivalent) (-0.041; +2 letters) ( P = 0.025). CONCLUSION: A deep learning tool was able to rapidly and accurately quantify IRF in retinitis pigmentosa-associated ME. Using this analysis tool, we confirmed that treatment with acetazolamide led to significant reduction in long-term IRF. Structural changes (IRF) only translated to significant functional improvements (best-corrected visual acuity) in eyes treated with acetazolamide.

  PublisherDOI

• Characterization of Pentosan Polysulfate Sodium Maculopathy with Fluorescence Lifetime Imaging Ophthalmoscopy

  2025-01-01

  articleOpen access
  PublisherDOI

• Universal Wavelet Units in 3D Retinal Layer Segmentation

  ArXiv.org · 2025-07-22

  preprintOpen access

  This paper presents the first study to apply tunable wavelet units (UwUs) for 3D retinal layer segmentation from Optical Coherence Tomography (OCT) volumes. To overcome the limitations of conventional max-pooling, we integrate three wavelet-based downsampling modules, OrthLattUwU, BiorthLattUwU, and LS-BiorthLattUwU, into a motion-corrected MGU-Net architecture. These modules use learnable lattice filter banks to preserve both low- and high-frequency features, enhancing spatial detail and structural consistency. Evaluated on the Jacobs Retina Center (JRC) OCT dataset, our framework shows significant improvement in accuracy and Dice score, particularly with LS-BiorthLattUwU, highlighting the benefits of tunable wavelet filters in volumetric medical image segmentation.

  PublisherOA PDFDOI

• Evaluating the clinical utility of multimodal large language models in rare maculopathy

  Scientific Reports · 2025-12-03

  articleOpen accessSenior author

  This study aimed to assess how multimodal large language models (MLLM) diagnose and differentiate Pentosan Polysulfate (PPS) Maculopathy from other phenotypic mimics. A retrospective review of clinical records and multimodal retinal imaging was conducted with patients from the Shiley Eye Institute and Casey Eye Institute. Four MLLMs (ChatGPT-4o, Claude 3.5 Sonnet, Google Gemini 1.5 Pro, Perplexity Llama 3.1 Sonar/Default) along with human retinal specialists answered prompts based on retinal imaging and demographic data. Performance was evaluated using accuracy, sensitivity and specificity estimates. The study included 126 eyes from 63 patients, with 36 eyes with PPS maculopathy, 50 eyes with Stargardt disease, and 40 eyes with PRPH2-associated multifocal pattern dystrophy. MLLMs showed improved accuracy and sensitivity when answer choices were restricted, with ChatGPT consistently performing best when all imaging modalities were prompted together. The inclusion of demographic data further enhanced performance in prompts with limited answer choices. Human retinal specialist evaluations aligned with MLLM performance trends and also improved with demographic data. While MLLMs show diagnostic potential, further refinement is needed before clinical implementation. These findings highlight the importance of prompt design and demographic data to optimize MLLM performance with retinal imaging modalities.

  PublisherOA PDFDOI

Frequent coauthors

• Baljean Dhillon

  University College London

  118 shared

• Fritz Gerald P. Kalaw

  University of California, San Diego

  73 shared

• Radha Ayyagari

  University of California, San Diego

  72 shared

• William R. Freeman

  Jacobs (United States)

  66 shared

• Leonardo Landó

  Hospital de Câncer de Barretos

  46 shared

• Peng Yong Sim

  Moorfields Eye Hospital

  43 shared

• Pooja Biswas

  University of San Diego

  42 shared

• Vasileios T. Papastavrou

  Royal Victoria Infirmary

  37 shared