Zobrazeno 1 - 10
of 63
pro vyhledávání: '"Akram Belghith"'
Autor:
Mark Christopher, Ruben Gonzalez, Justin Huynh, Evan Walker, Bharanidharan Radha Saseendrakumar, Christopher Bowd, Akram Belghith, Michael H. Goldbaum, Massimo A. Fazio, Christopher A. Girkin, Carlos Gustavo De Moraes, Jeffrey M. Liebmann, Robert N. Weinreb, Sally L. Baxter, Linda M. Zangwill
Publikováno v:
Bioengineering, Vol 11, Iss 2, p 140 (2024)
A longitudinal ophthalmic dataset was used to investigate multi-modal machine learning (ML) models incorporating patient demographics and history, clinical measurements, optical coherence tomography (OCT), and visual field (VF) testing in predicting
Externí odkaz:
https://doaj.org/article/ef9cf9348a93430385e5e546f924fb7d
Autor:
Rui Fan, PhD, Kamran Alipour, PhD, Christopher Bowd, PhD, Mark Christopher, PhD, Nicole Brye, James A. Proudfoot, MS, Michael H. Goldbaum, MD, Akram Belghith, PhD, Christopher A. Girkin, MD, Massimo A. Fazio, PhD, Jeffrey M. Liebmann, MD, Robert N. Weinreb, MD, Michael Pazzani, PhD, David Kriegman, PhD, Linda M. Zangwill, PhD
Publikováno v:
Ophthalmology Science, Vol 3, Iss 1, Pp 100233- (2023)
Purpose: To compare the diagnostic accuracy and explainability of a Vision Transformer deep learning technique, Data-efficient image Transformer (DeiT), and ResNet-50, trained on fundus photographs from the Ocular Hypertension Treatment Study (OHTS)
Externí odkaz:
https://doaj.org/article/136c451302b347619841acec91a433a5
Autor:
Jasmin Rezapour, Andrew Q. Tran, Christopher Bowd, Nevin W. El-Nimri, Akram Belghith, Mark Christopher, Nicole Brye, James A. Proudfoot, Jade Dohleman, Massimo A. Fazio, Jost B. Jonas, Robert N. Weinreb, Linda M. Zangwill
Publikováno v:
Frontiers in Medicine, Vol 9 (2022)
PurposeTo compare optic nerve head (ONH) ovality index and rotation angle measurements based on semi-automated delineation of the clinical ONH margin derived from photographs and automated BMO configuration derived from optical coherence tomography (
Externí odkaz:
https://doaj.org/article/022ea64b208441918d0da9a58bfe1bef
Autor:
Jasmin Rezapour, Christopher Bowd, Jade Dohleman, Akram Belghith, James A. Proudfoot, Mark Christopher, Leslie Hyman, Jost B. Jonas, Massimo A. Fazio, Robert N. Weinreb, Linda M. Zangwill
Publikováno v:
Scientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
Abstract This study characterizes differences in glaucomatous eyes with and without high axial myopia using custom automated analysis of OCT images. 452 eyes of 277 glaucoma patients were stratified into non (n = 145 eyes), mild (n = 214 eyes), and h
Externí odkaz:
https://doaj.org/article/29ffdc95bc5e4fe3b6a87062477c4de6
Autor:
Chunwei Zhang, Andrew J Tatham, Ricardo Y Abe, Na'ama Hammel, Akram Belghith, Robert N Weinreb, Felipe A Medeiros, Jeffrey M Liebmann, Christopher A Girkin, Linda M Zangwill
Publikováno v:
PLoS ONE, Vol 11, Iss 8, p e0160549 (2016)
To investigate macular ganglion cell-inner plexiform layer (mGCIPL) thickness in glaucomatous eyes with visible localized retinal nerve fiber layer (RNFL) defects on stereophotographs.112 healthy and 149 glaucomatous eyes from the Diagnostic Innovati
Externí odkaz:
https://doaj.org/article/1a06115ed05e4d80aa402e85e1c12a1a
Autor:
Christopher Bowd, Akram Belghith, Jasmin Rezapour, Mark Christopher, Leslie Hyman, Jost B. Jonas, Robert N. Weinreb, Linda M. Zangwill
Publikováno v:
American Journal of Ophthalmology. 242:26-35
To evaluate the diagnostic accuracy of a novel optical coherence tomography texture-based en face image analysis (SALSA-Texture) that requires segmentation of only 1 retinal layer for glaucoma detection in eyes with axial high myopia, and to compare
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::dc73d0847f90d1e9de63b0e492942395
https://doi.org/10.1016/j.ajo.2022.04.019
https://doi.org/10.1016/j.ajo.2022.04.019
Autor:
Jasmin Rezapour, James A. Proudfoot, Christopher Bowd, Jade Dohleman, Mark Christopher, Akram Belghith, Suzanne M. Vega, Keri Dirkes, Min Hee Suh, Jost B. Jonas, Leslie Hyman, Massimo A. Fazio, Ruti Sella, Natalie A. Afshari, Robert N. Weinreb, Linda M. Zangwill
Publikováno v:
American Journal of Ophthalmology. 237:221-234
PurposeTo determine the predictors of Bruch membrane opening (BMO) location accuracy and the visibility of the BMO location in glaucoma and healthy individuals with and without axial high myopia.DesignCross-sectional study.MethodsHealthy eyes and eye
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4aebeb00b5286d88ca98be97733ce140
https://doi.org/10.1016/j.ajo.2021.11.030
https://doi.org/10.1016/j.ajo.2021.11.030
Autor:
Linda M. Zangwill, Akram Belghith, Robert N. Weinreb, Jasmin Rezapour, Christopher Bowd, Rui Fan, Mark Christopher, Alireza Kamalipour, Michael H. Goldbaum, Huiyuan Hou, Sasan Moghimi
Publikováno v:
Am J Ophthalmol
PurposeTo compare convolutional neural network (CNN) analysis of en face vessel density images to gradient boosting classifier (GBC) analysis of instrument-provided, feature-based optical coherence tomography angiography (OCTA) vessel density measure
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7e3ef56b7b747a021a21be45b6e6066c
https://doi.org/10.1016/j.ajo.2021.11.008
https://doi.org/10.1016/j.ajo.2021.11.008
Autor:
Rui, Fan, Kamran, Alipour, Christopher, Bowd, Mark, Christopher, Nicole, Brye, James A, Proudfoot, Michael H, Goldbaum, Akram, Belghith, Christopher A, Girkin, Massimo A, Fazio, Jeffrey M, Liebmann, Robert N, Weinreb, Michael, Pazzani, David, Kriegman, Linda M, Zangwill
Publikováno v:
Ophthalmology science. 3(1)
To compare the diagnostic accuracy and explainability of a Vision Transformer deep learning technique, Data-efficient image Transformer (DeiT), and ResNet-50, trained on fundus photographs from the Ocular Hypertension Treatment Study (OHTS) to detect
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=pmid________::fd1b89951d8d3485ec0e5d3fb72d0c95
https://pubmed.ncbi.nlm.nih.gov/36545260
https://pubmed.ncbi.nlm.nih.gov/36545260
Autor:
Linda M. Zangwill, David Kriegman, Michael Pazzani, Robert N. Weinreb, Jeffrey M. Liebmann, Massimo Fazio, Christopher A. Girkin, Akram Belghith, Michael H. Goldbaum, James A. Proudfoot, Nicole Brye, Mark Christopher, Christopher Bowd, Kamran Alipour, Rui Fan
Purpose: To compare the diagnostic accuracy and explainability of a new Vision Transformer deep learning technique, Data-efficient image Transformer (DeiT), and Resnet-50, trained on fundus photographs from the Ocular Hypertension Treatment Study (OH
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::908aedb56a02578ece9da37a1110471f
https://doi.org/10.36227/techrxiv.19727314
https://doi.org/10.36227/techrxiv.19727314
