Symptomatic Vitreous Opacity Quantification By A Novel Ai-Guided Scanning Laser Ophthalmoscopy (Slo) And Optical Coherence Tomography (Oct) Device
Published 2026 - 44th Congress of the ESCRS
Reference: PPE1.04 | Type: ESONT Abstract | DOI: 10.82333/2qs9-fp22
Authors: Mr Eric Martin* 1 , Dr Thas Yuwaraj 2 , Mrs Anastasiia Mereshchuk 3 , Dr Thomas McElroy 3 , Dr Mark Greve 4 , Dr Riz Somani 4 , Dr Dave Ehmann 4 , Dr Ernesto Calvo 5 , Dr Mark Seamone 4 , Dr Carl Reithmeier 3 , Dr Nir Katchinskiy 3
1null,edmonton,Canada, 2null,Toronto,Canada, 3null,Edmonton,Canada, 4Ophthalmology,University of Alberta,Edmonton,Canada, 5Ophthalmology,Universidad de Panama,Panama City,Panama
Purpose
Symptomatic vitreous opacities (SVOs) are common, yet diagnosis and management remain non-standardized and thus inconsistent across practices. This study aimed to quantitatively evaluate SVO morphology and location in adults using an investigational non-contact AI-guided SLO/OCT imaging platform.
Setting
Two-centre independent prospective imaging study in Panama City, Panama and Edmonton, Alberta, Canada; data collected at private ophthalmology clinics
Methods
Adults presenting with symptomatic vitreous opacities (floaters) were prospectively enrolled and imaged using an investigational, non-contact AI-guided SLO/OCT imaging platform. The system simultaneously acquired SLO sequences and volumetric OCT scans. Combined manual and automated analysis localized SVOs within the vitreous and quantified size, opacity, morphology, and position relative to the retina. Participants completed a questionnaire assessing floater-related symptoms.
Results
Across both centres, 238 eyes from 135 subjects were analyzed. Both male (39.7%) and female (60.3%) participants were enrolled, with most aged 65 years and older. A total of 1045 SLO and 966 OCT images were acquired. SVOs were visible in 943 SLO (90.2%) and 779 OCT (80.6%) scans. System imaging was used to (1) quantify SVO size, morphology, motility, and location relative to the retina; (2) explore associations between floater features and participant-reported symptoms; and (3) establish an in vivo database of floater morphology
Conclusions
This study demonstrates feasibility of this novel non-contact AI-guided SLO/OCT platform for objective, quantitative assessment of SVOs. Image-derived metrics may advance understanding of floater morphology, support standardized grading, reduce management variability, and enable measurable follow-up.