ARVO 2026 | Booth # 11000 | Colorado Convention Center
ARVO 2026
IDHea® at ARVO 2026 | TOPCON HEALTHCARE BOOTH # 11000
ARVO represents an important moment for IDHea, with expanded research contributions and growing engagement from the vision science community. We look forward to connecting with researchers and collaborators throughout the meeting.
3 May – 7 May | Colorado Convention Center, Denver, CO
What we're bringing to ARVO 2026
IDHea will have a significant presence at the Association for Research in Vision and Ophthalmology (ARVO) Conference this year, with 26 scientific papers | posters featured at the main event and the Imaging in the Eye pre-conference session May 2nd. These presentations highlight how large-scale, real-world eye care and systemic datasets from the IDHea platform accelerate advances in oculomics, biomarker discovery, and AI-driven research. Just one year after launch, this broad representation reflects the rapid adoption of IDHea across the vision science community.
On Stage at ARVO
Experience our podium presentations for live expert insights and in-depth discussions of breakthrough research in ocular science. Engage directly with presenters on the latest advances in AI, machine learning and data science driving innovation.
| Title | First Author | Session No. | Session Title | Date | Time | Presentation No. |
|---|---|---|---|---|---|---|
| Improving community-based OCT screening through AI-based biomarker visualization | Jacob Pichelmann | 112 | AI in retina I | 5/3/2026 | 1:00 pm - 2:45 pm | 311 |
| Impact of scan misalignment in cpRNFL precision | Marco Miranda | 312 | Big data and data science | 5/5/2026 | 8:30 am - 10:15 am | 312 |
| Comparison of Foundation Models in Classification of OCT Volumes | Theodore Spaide | 525 | Machine learning for classification | 5/7/2026 | 11:45 am - 1:30 pm | 5547 |
On the Floor at ARVO
Visit our poster sessions to explore new datasets, innovative methodologies, and research applications—and connect with the team behind the work.
| Title | First Author | Session No. | Session Title | Date | Time | Presentation No. |
|---|---|---|---|---|---|---|
| Normalization of data for ocular clinical narratives to train large-language models | Kerry E. Ashby | 320 | AI in ophthalmology IV | 5/3/2026 | 8:30am - 10:15am | 0350 |
| Prevalence and Characterization of Pigment Epithelial Detachment and Hyporeflective Spaces Detected on OCT Using AI in Optometry | Reena Chopra | 150 | AI in retina I | 5/3/2026 | 3:15 pm - 5:00 pm | 922-0410 |
| Comparison of Retinal Thickness Measurements from Vertical and Horizontal OCT Volume Scans | Thai Do | 253 | OCT clinical applications | 5/4/2026 | 3:00 pm- 4:45 pm | 2145-0577 |
| An anatomy-aware OCT reference database built using deep learning with real-world data | Yi Sing Hsiao | 255 | OCT/OCTA development and technical advances | 5/4/2026 | 3:00 pm - 4:45 pm | 2206-0638 |
| Temporal Dynamics of Ophthalmology Research Themes via BERTopic on IDHea | Ramzi Nasri | 257 | AI in ophthalmology III | 5/4/2026 | 3:00pm - 4:45pm | 2274 - 0706 |
| Optimizing real-world EMR data curation with an NLP pipeline | Anya Guzman | 319 | Big Data and EHR analysis | 5/5/2026 | 8:30am -10:15am | 2718 - 0346 |
| Developing a Standardized Ontology for Reporting Ocular Imaging and Functional Testing Findings | Juan Arias | 319 | Big data and EHR analysis | 5/5/2026 | 8:30am - 10:15am | 2700 - 0328 |
| Comparing demographic signal in RETFound features across datasets | Nessa Pantfoerder | 320 | AI in ophthalmology IV | 5/5/2026 | 8:30am - 10:15am | 2719 - 0347 |
| Analyses of relationship between retinal melanin and glaucoma | Mitchell Kerr | 343 | Big Data and data science | 5/5/2026 | 1:15pm - 3:00pm | 3032 - 0282 |
| Learning Effect of a Novel Binocular Visual Function Perimeter | Derek Ho | 345 | Ophthalmic imaging, visual and retinal function | 5/5/2026 | 1:15pm - 3:00pm | 3088-0489 |
| Secure Bring-Your-Own-Model Framework for Model IP Protection in the IDHea Research Platform | Niina Mäkinen | 509 | Machine learning for classification, segmentation and others | 5/7/2026 | 8:00am - 9:45am | 5188 - 0251 |
| Federated Learning and Databricks Clean Rooms for Privacy-Preserving Multi-Institutional AI in the IDHea Research Platform | Uula Ranta | 509 | Machine learning for classification, segmentation and others | 5/7/2026 | 8:00am - 9:45am | 5184 - 0247 |
| Evaluating the Robustness of an OCT Biomarker Detection Model in a Predominantly Healthy Cohort | Jamie Campbell-Burke | 535 | AI in Retina III | 5/7/2026 | 11:45am - 1:30pm | 0313 |
| Precision and Cross-Device Agreement of a Pattern-Based OCT Metric for Glaucoma Detection | Amiee Ho | 507 | Posterior segment and optic nerve imaging | 4/7/2026 | 8:00am – 9:45am | 5126-0189 |
| Explainability vs. Data Efficiency: Comparing Specialized and Foundation Models for Retinal Feature Classification | Markus Unterdechler | 535 | AI in Retina III | 5/7/2026 | 11:45 am – 1:30pm | 5661 - 0314 |
| IDHea Ophthalmic Metrics Dashboard (RNFL Thickness and Disc Are | Rahul Kendale | RE On-demand Presentations | OD257 |
On the Floor at ARVO Imaging in the Eye
(Ancillary Session, 05/02/2026)
| Title | First Author | Session No. | Session Title | Date | Time | Presentation No. |
|---|---|---|---|---|---|---|
| Comparing multi-device reproducibility for choroidal analysis between TABS and Choroidalyzer | Jamie Campbell-Burke | Retinal Vasculature & Choroid | 5/2/2026 | 9:30-10am;1-1:30pm;3:30-4pm | PB00127 | |
| Longitudinal retinal nerve fiber layer and ganglion cell layer thickness trends in a large real-world OCT data | Anya Guzman | Glaucoma | 5/2/2026 | 9:30-10am;1-1:30pm;3:30-4pm | PB0059 | |
| Comparison of Performance Response to Dataset Size for Different Foundation Models | Theodore Spaide | Artificial Intelligence & Deep Learning | 5/2/2026 | 9:30-10am;1-1:30pm;3:30-4pm | PB0031 | |
| Semi-automated approach for improving patient metadata accuracy in retinal imaging using vascular pattern matching | Yi Sing Hsiao | OCT/OCTA Methodology & Analysis | 5/2/2026 | 9:30-10am;1-1:30pm;3:30-4pm | PB0093 | |
| A comparison of OCT measures obtained from a real-world reference database of healthy subjects when matched with subjects with retinal disease | Kristen Knight | OCT/OCTA Methodology & Analysis | 5/2/2026 | 9:30-10am;1-1:30pm;3:30-4pm | PB0095 | |
| A Multi-Stage Filtering Framework for Large-Scale Cohorting of Patients with Diabetic Macular Edema | Jennifer Luu | Diabetic Eye Disease | 5/2/2026 | 9:30-10am;1-1:30pm;3:30-4pm | PB0050 | |
| Comparative evaluation of two deep learning solutions based on open-source foundation models for diabetic retinopathy detection using a gold standard fundus dataset | Mitchell Kerr | Diabetic Eye Disease | 5/2/2026 | 9:30-10am;1-1:30pm;3:30-4pm | PB0051 |
Our IDHea Authors at ARVO
Conference & ARVO Imaging in the Eye Session
