- Browse by Subject
Browsing by Subject "OCTA"
Now showing 1 - 2 of 2
Results Per Page
Sort Options
Item Using Multi-Layer Perceptron Driven Diagnosis to Compare Biomarkers for Primary Open Angle Glaucoma(Association for Research in Vision and Ophthalmology, 2024) Riina, Nicholas; Harris, Alon; Siesky, Brent A.; Ritzer, Lukas; Pasquale, Louis R.; Tsai, James C.; Keller, James; Wirostko, Barbara; Arciero, Julia; Fry, Brendan; Eckert, George; Verticchio Vercellin, Alice; Antman, Gal; Sidoti, Paul A.; Guidoboni, Giovanna; Mathematical Sciences, School of SciencePurpose: To use neural network machine learning (ML) models to identify the most relevant ocular biomarkers for the diagnosis of primary open-angle glaucoma (POAG). Methods: Neural network models, also known as multi-layer perceptrons (MLPs), were trained on a prospectively collected observational dataset comprised of 93 glaucoma patients confirmed by a glaucoma specialist and 113 control subjects. The base model used only intraocular pressure, blood pressure, heart rate, and visual field (VF) parameters to diagnose glaucoma. The following models were given the base parameters in addition to one of the following biomarkers: structural features (optic nerve parameters, retinal nerve fiber layer [RNFL], ganglion cell complex [GCC] and macular thickness), choroidal thickness, and RNFL and GCC thickness only, by optical coherence tomography (OCT); and vascular features by OCT angiography (OCTA). Results: MLPs of three different structures were evaluated with tenfold cross validation. The testing area under the receiver operating characteristic curve (AUC) of the models were compared with independent samples t-tests. The vascular and structural models both had significantly higher accuracies than the base model, with the hemodynamic AUC (0.819) insignificantly outperforming the structural set AUC (0.816). The GCC + RNFL model and the model containing all structural and vascular features were also significantly more accurate than the base model. Conclusions: Neural network models indicate that OCTA optic nerve head vascular biomarkers are equally useful for ML diagnosis of POAG when compared to OCT structural biomarker features alone.Item Vascular Imaging Findings in Retinopathy of Prematurity(Wiley, 2024) Rowe, Lucas W.; Belamkar, Aditya; Antman, Gal; Hajrasouliha, Amir R.; Harris, Alon; Ophthalmology, School of MedicineRetinopathy of prematurity (ROP) is a vascular disease among preterm infants involving incomplete or abnormal retinal vascularization and is a leading cause of preventable blindness globally. Measurements of ocular blood flow originating from a variety of imaging modalities, including colour Doppler imaging (CDI), fluorescein angiography (FA) and ocular coherence tomography angiography (OCTA), have been associated with changes in ROP patients. Herein, we discuss and summarize the relevant current literature on vascular imaging and ROP reviewed through December 2022. Differences in vascular imaging parameters between ROP patients and healthy controls are reviewed and summarized. The available data identify significantly increased peak systolic velocity (PSV) in the central retinal artery and ophthalmic artery as measured by CDI, increased vascular tortuosity as measured by FA, smaller foveal avascular zone (FAZ) as measured by FA and OCTA, and increased foveal vessel density (VD) and reduced parafoveal VD as measured by OCTA in ROP patients compared with controls. None of the above findings appear to reliably correlate with visual acuity. The studies currently available, however, are inconclusive and lack robust longitudinal data. Vascular imaging demonstrates the potential to aid in the diagnosis, management and monitoring of ROP, alongside retinal examination via indirect ophthalmoscopy and fundus photography.