Publications
ICL sizing & vault research
My research is about fitting the right EVO / EVO+ ICL to an individual eye. The lens comes in four sizes and the same eye behaves very differently across them, so the question is which one fits that anatomy, not which one hits a number. Conventional sizing infers the answer from white-to-white corneal diameter and a single formula (OCOS, Reinstein, KS, NK, Parkhurst). My collaborators and I train image-based models on anterior-segment imaging and real surgical outcomes instead, which is what powers ICL Fit.
ICL Fit
ICL Fit is the AI agent this research is built into, and how I size an ICL. Published first as VAULT-OCT in the Journal of Cataract & Refractive Surgery, it predicts what each of the four available lens sizes would do in a specific eye from that eye's own anterior-segment imaging, trained on a dataset of 756 real EVO ICL surgical cases, and identifies the best fit.
Vault is an output, not the target. The 250 to 750 micron band usually quoted is a population average, and the same 400 microns can be comfortable in one anterior segment and too tight in another. The goal is the lens that fits that eye, not the lens that lands nearest a number taken from everybody else.
Peer-reviewed articles
- ★ JCRSVAULT-OCT: Predicting ICL Postoperative Vault from Anterior-Segment OCT Using Deep LearningJournal of Cataract & Refractive Surgery · 2025
A deep-learning model that predicts postoperative EVO ICL vault directly from a preoperative anterior-segment OCT (AS-OCT) image. Trained on real Implantable Collamer Lens outcomes, VAULT-OCT learns sulcus and anterior-segment anatomy that conventional white-to-white and anterior-chamber-depth nomograms miss, aiming to improve ICL sizing and cut vault-related exchanges.
- ★ JCRSVAULT: A Novel Image-Based AI Model for Predicting Implantable Collamer Lens Postoperative VaultJournal of Cataract & Refractive Surgery · 2024
The original image-based artificial-intelligence model for ICL vault prediction. It demonstrated that a neural network reading anterior-segment images can estimate postoperative vault more accurately than traditional sizing formulas, establishing AI ICL sizing as a viable path to safer phakic IOL surgery.
- Clinical OphthalmologyICL Exchanges or Explants Due to Sizing in a US High-Volume CenterClinical Ophthalmology · 2025
A retrospective review quantifying how often EVO ICL lenses are exchanged or explanted for sizing-related vault problems at a high-volume US refractive practice, and what drives sizing error; the clinical case for better vault prediction.
- Clinical OphthalmologyClinical Outcomes of ICL for Myopia in Eyes with Anterior Chamber Depth < 3.0 mmClinical Ophthalmology · 2025
Refractive and safety outcomes of EVO ICL for myopia in eyes with shallow anterior chamber depth (< 3.0 mm), a group often considered borderline ICL candidates, showing the implant can be safe and effective when sizing is done carefully.
- CureusAccuracy of Reported Sizes of the EVO/EVO+ Visian Implantable Collamer LensCureus · 2025
A measurement study showing that labeled EVO / EVO+ Visian ICL sizes can differ from the lens's true dimensions, a source of sizing error that directly affects vault prediction and the choice of ICL size.
- Clinical OphthalmologyDynamic Changes in ICL Vault and Anterior Chamber Angle Under Varying LightingClinical Ophthalmology · 2026
Anterior-segment OCT imaging of 100 post-ICL eyes under scotopic, mesopic, and photopic lighting shows vault and anterior chamber angle shift by roughly 100 µm with pupil size: evidence that ICL vault is a physiologic range, not a single fixed micron target, and the rationale for best-fit-per-eye sizing.
Featured data: vault is dynamic
From Dynamic Changes in ICL Vault and Anterior Chamber Angle Under Varying Lighting Conditions (Clinical Ophthalmology, 2026), where we imaged 100 post-ICL eyes under scotopic, mesopic, and photopic lighting. The finding underpins how I approach sizing: vault is a physiologic range, not a fixed micron target, so the goal is best fit for the individual eye.




Conference presentations
- VAULT-OCT: Deep Learning to Predict ICL Vault from OCTAmerican Society of Cataract & Refractive Surgery (ASCRS) 2024 · Young MD Connect Live 2025 · Ophthalmology Outliers 2025
- The Future of AI ICL Sizing is OCT, Not UBMSTAAR Surgical Surgeon Summit · 2024
Educational articles
- Enhancing ICL Sizing Accuracy through Image-Based Machine-Learning AlgorithmsThe Ophthalmology Business Minute · 2023
Common questions
What is ICL Fit?
ICL Fit is an AI agent that identifies the best-fitting EVO ICL size for an individual eye from a preoperative anterior-segment OCT image. It was published as VAULT-OCT in the Journal of Cataract & Refractive Surgery, developed with collaborators at Parkhurst NuVision, and trained on real surgical outcomes rather than a single corneal-diameter formula.
Why does ICL vault matter?
Vault is the gap between the ICL and the eye's natural lens. Too little risks cataract, too much can crowd the angle and raise pressure, so it is worth measuring. But it is a number that follows from the lens you chose, not a target to aim at: the quoted 250 to 750 micron range is a population average, and the right clearance depends on that eye's chamber depth, angle and sulcus. ICL Fit works the other way round, predicting which size actually fits the anatomy.
How does AI improve ICL sizing?
Traditional sizing uses white-to-white and anterior-chamber-depth formulas. AI models such as VAULT and VAULT-OCT learn directly from anterior-segment images and real surgical outcomes, capturing sulcus and anatomy the formulas miss; the approach behind the ICL Fit sizing tool.
Can people with a shallow anterior chamber get an EVO ICL?
Often, yes. My research on eyes with anterior chamber depth under 3.0 mm found EVO ICL can be safe and effective for myopia in carefully sized, appropriately selected eyes, though candidacy always requires a full exam.