Browsing by Author "Keller, James"

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    Artificial Intelligence to Aid Glaucoma Diagnosis and Monitoring: State of the Art and New Directions
    (MDPI, 2022) Nunez, Roberto; Harris, Alon; Ibrahim, Omar; Keller, James; Wikle, Christopher K.; Robinson, Erin; Zukerman, Ryan; Siesky, Brent; Verticchio, Alice; Rowe, Lucas; Guidoboni, Giovanna; Ophthalmology, School of Medicine
    Recent developments in the use of artificial intelligence in the diagnosis and monitoring of glaucoma are discussed. To set the context and fix terminology, a brief historic overview of artificial intelligence is provided, along with some fundamentals of statistical modeling. Next, recent applications of artificial intelligence techniques in glaucoma diagnosis and the monitoring of glaucoma progression are reviewed, including the classification of visual field images and the detection of glaucomatous change in retinal nerve fiber layer thickness. Current challenges in the direct application of artificial intelligence to further our understating of this disease are also outlined. The article also discusses how the combined use of mathematical modeling and artificial intelligence may help to address these challenges, along with stronger communication between data scientists and clinicians.
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    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 Science
    Purpose: 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.