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Randy Kardon

Showing results (51-60 of 64) with videos related to

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BMJ Neurology Open|July 2, 2024
Classifying and quantifying changes in papilloedema using machine learningJoseph Branco, Jui-Kai Wang, Tobias Elze, et al.
Biomedical Optics Express|January 11, 2014
A combined machine-learning and graph-based framework for the segmentation of retinal surfaces in SD-OCT volumesBhavna J Antony, Michael D Abràmoff, Matthew M Harper, et al.
Investigative Ophthalmology & Visual Science|January 14, 2026
Multimodal Deep Learning Differentiates Papilledema and Non-Arteritic Anterior Ischemic Optic Neuropathy From Healthy EyesDavid Szanto, Asala Erekat, Brian Woods, et al.
Neuroimage. Clinical|May 23, 2021
Network topology changes in chronic mild traumatic brain injury (mTBI)Elias Boroda, Michael Armstrong, Casey S Gilmore, et al.
Investigative Ophthalmology & Visual Science|November 6, 2014
Baseline OCT measurements in the idiopathic intracranial hypertension treatment trial, part I: quality control, comparisons, and variability, Peggy Auinger, Mary Durbin, et al.
Investigative Ophthalmology & Visual Science|November 6, 2014
Baseline OCT measurements in the idiopathic intracranial hypertension treatment trial, part II: correlations and relationship to clinical features, Peggy Auinger, Mary Durbin, et al.
Ophthalmology|August 4, 2006
Prevalence of a normal C-reactive protein with an elevated erythrocyte sedimentation rate in biopsy-proven giant cell arteritisMona Parikh, Neil R Miller, Andrew G Lee, et al.
Psychiatry Research|November 9, 2016
Pupillary response abnormalities in depressive disordersScott A Laurenzo, Randy Kardon, Johannes Ledolter, et al.
Frontiers in Neurology|March 12, 2019
Standards in PupillographyCarina Kelbsch, Torsten Strasser, Yanjun Chen, et al.
Annals of Clinical and Translational Neurology|November 18, 2021
Optical coherence tomography in multiple sclerosis: A 3-year prospective multicenter studyFriedemann Paul, Peter A Calabresi, Frederik Barkhof, et al.
Pageof 7

Showing results (51-60 of 64) with videos related to

Sort By:
Pageof 7
BMJ Neurology Open|July 2, 2024
Classifying and quantifying changes in papilloedema using machine learningJoseph Branco, Jui-Kai Wang, Tobias Elze, et al.
Biomedical Optics Express|January 11, 2014
A combined machine-learning and graph-based framework for the segmentation of retinal surfaces in SD-OCT volumesBhavna J Antony, Michael D Abràmoff, Matthew M Harper, et al.
Investigative Ophthalmology & Visual Science|January 14, 2026
Multimodal Deep Learning Differentiates Papilledema and Non-Arteritic Anterior Ischemic Optic Neuropathy From Healthy EyesDavid Szanto, Asala Erekat, Brian Woods, et al.
Neuroimage. Clinical|May 23, 2021
Network topology changes in chronic mild traumatic brain injury (mTBI)Elias Boroda, Michael Armstrong, Casey S Gilmore, et al.
Investigative Ophthalmology & Visual Science|November 6, 2014
Baseline OCT measurements in the idiopathic intracranial hypertension treatment trial, part I: quality control, comparisons, and variability, Peggy Auinger, Mary Durbin, et al.
Investigative Ophthalmology & Visual Science|November 6, 2014
Baseline OCT measurements in the idiopathic intracranial hypertension treatment trial, part II: correlations and relationship to clinical features, Peggy Auinger, Mary Durbin, et al.
Ophthalmology|August 4, 2006
Prevalence of a normal C-reactive protein with an elevated erythrocyte sedimentation rate in biopsy-proven giant cell arteritisMona Parikh, Neil R Miller, Andrew G Lee, et al.
Psychiatry Research|November 9, 2016
Pupillary response abnormalities in depressive disordersScott A Laurenzo, Randy Kardon, Johannes Ledolter, et al.
Frontiers in Neurology|March 12, 2019
Standards in PupillographyCarina Kelbsch, Torsten Strasser, Yanjun Chen, et al.
Annals of Clinical and Translational Neurology|November 18, 2021
Optical coherence tomography in multiple sclerosis: A 3-year prospective multicenter studyFriedemann Paul, Peter A Calabresi, Frederik Barkhof, et al.
Pageof 7