Davis Tyler

Mohammadzadeh Vahid, Wu Sean, Davis Tyler, Vepa Arvind, Morales Esteban, Besharati Sajad et al.

A deep learning model accurately predicted glaucoma visual field progression from serial optic disc photos, offering a promising tool for early clinical intervention.

Mohammadzadeh Vahid, Wu Sean, Besharati Sajad, Davis Tyler, Vepa Arvind, Morales Esteban et al.

A deep learning model using baseline RNFL thickness and serial optic disc photos accurately predicted visual field progression in glaucoma, showing promise as a clinical tool for identifying high-risk patients.

Mohammadzadeh Vahid, Li Leyan, Fei Zhe, Davis Tyler, Morales Esteban, Wu Kara et al.

Smoothing visual field data with NN and VAE algorithms improves signal-to-noise, enabling earlier and more effective detection of glaucoma progression in patients.

Rasheed Haroon Adam, Davis Tyler, Morales Esteban, Fei Zhe, Grassi Lourdes, De Gainza Agustina et al.

DDLSNet, a deep learning system, moderately agreed with clinicians in grading glaucoma severity from fundus photos, demonstrating the feasibility of automated optic disc assessment for clinical use.

Rasheed Haroon Adam, Davis Tyler, Morales Esteban, Fei Zhe, Grassi Lourdes, De Gainza Agustina et al.

RimNet, a deep learning system, accurately identified and measured optic disc rim parameters in glaucoma, offering a valuable tool for automated glaucoma severity grading.