Global Search

Search articles, concepts, and chapters

Kamalipour Alireza

๐Ÿ‡ด๐Ÿ‡ฒ University of San Diego
ORCIDOpenAlex36 articles in GJC

36 articles in GJC

1.

Retinal nerve fibre layer optical texture analysis: retinal nerve fibre bundle defect patterns and the extent of macular involvement across different stages of glaucoma.

Kamalipour Alireza, Moghimi Sasan, Khosravi Pooya, Tansuebchueasai Natchada, Camp Andrew Steven, Vasile Cristiana et al.

Br J OphthalmolNov 20250 citationsObservational Study

ROTA revealed widespread RNFL bundle defects in glaucoma, especially papillomacular and papillofoveal bundles. Their presence significantly predicts central visual field loss, even in early disease.

3.

Detection of glaucoma progression on longitudinal series of en-face macular optical coherence tomography angiography images with a deep learning model.

Mohammadzadeh Vahid, Liang Youwei, Moghimi Sasan, Xie Pengtao, Nishida Takashi, Mahmoudinezhad Golnoush et al.

Br J OphthalmolNov 20243 citationsCohort Study

A deep learning model effectively detected glaucoma progression using longitudinal macular OCTA images, outperforming traditional methods. This could enhance early clinical detection and management.

9.

Association Between Longitudinal 10-2 Central Visual Field Change and the Risk of Visual Acuity Loss in Mild-to-Moderate Glaucoma.

Wu Jo-Hsuan, Moghimi Sasan, Nishida Takashi, Kamalipour Alireza, Liebmann Jeffrey M, Fazio Massimo et al.

J GlaucomaMay 20234 citationsCohort Study

This study found that faster worsening of central 10-2 visual fields predicts future visual acuity loss in mild-to-moderate glaucoma, highlighting its importance for monitoring impending vision impairment.

14.

Deep Learning Estimation of 10-2 Visual Field Map Based on Circumpapillary Retinal Nerve Fiber Layer Thickness Measurements.

Kamalipour Alireza, Moghimi Sasan, Khosravi Pooya, Jazayeri Mohammad Sadegh, Nishida Takashi, Mahmoudinezhad Golnoush et al.

Am J OphthalmolNov 202219 citationsObservational Study

Deep learning accurately estimated central visual fields from OCT RNFL data, outperforming traditional methods. This could help personalize glaucoma monitoring and resource allocation for central vision loss.

15.

Combining Optical Coherence Tomography and Optical Coherence Tomography Angiography Longitudinal Data for the Detection of Visual Field Progression in Glaucoma.

Kamalipour Alireza, Moghimi Sasan, Khosravi Pooya, Mohammadzadeh Vahid, Nishida Takashi, Micheletti Eleonora et al.

Am J OphthalmolNov 202218 citationsCohort Study

Machine learning combining longitudinal OCT and OCTA data best detected glaucoma visual field progression, showing OCTA complements OCT for evaluating functional loss.

22.

Association of Initial Optical Coherence Tomography Angiography Vessel Density Loss With Faster Visual Field Loss in Glaucoma.

Nishida Takashi, Moghimi Sasan, Wu Jo-Hsuan, Chang Aimee C, Diniz-Filho Alberto, Kamalipour Alireza et al.

JAMA OphthalmolApr 20220 citationsCohort Study

This study found that faster initial retinal vessel density loss, measured by OCTA, predicts faster subsequent visual field loss in glaucoma, highlighting OCTA's potential to identify rapid progressors.

24.

A Prospective Longitudinal Study to Investigate Corneal Hysteresis as a Risk Factor of Central Visual Field Progression in Glaucoma.

Kamalipour Alireza, Moghimi Sasan, Eslani Medi, Nishida Takashi, Mohammadzadeh Vahid, Micheletti Eleonora et al.

Am J OphthalmolMar 20220 citationsCohort Study

This study found lower corneal hysteresis predicts faster central visual field decline in glaucoma, suggesting CH can help clinicians assess progression risk, especially for central vision.