Skip to main navigation
Skip to search
Skip to main content
Icahn School of Medicine at Mount Sinai Home
Help & FAQ
Link opens in a new tab
Search content at Icahn School of Medicine at Mount Sinai
Home
Profiles
Research units
Publications & Research Outputs
Press/Media
Deep learning in ophthalmology: The technical and clinical considerations
Daniel S.W. Ting
, Lily Peng
, Avinash V. Varadarajan
, Pearse A. Keane
, Philippe M. Burlina
, Michael F. Chiang
, Leopold Schmetterer
,
Louis R. Pasquale
, Neil M. Bressler
, Dale R. Webster
, Michael Abramoff
, Tien Y. Wong
Eye and Vision Research Institute
Friedman Brain Institute
Icahn School of Medicine at Mount Sinai
Ophthalmology
Research output
:
Contribution to journal
›
Review article
›
peer-review
468
Scopus citations
Overview
Fingerprint
Fingerprint
Dive into the research topics of 'Deep learning in ophthalmology: The technical and clinical considerations'. Together they form a unique fingerprint.
Sort by
Weight
Alphabetically
Keyphrases
Clinical Considerations
100%
Deep Learning
100%
Deep Learning System
100%
Ophthalmology
100%
Artificial Intelligence Learning
66%
Deep Learning Algorithm
33%
Eye Diseases
33%
Machine Learning Learning
33%
Deep Machine Learning
33%
Big Data
16%
Aging Population
16%
Cardiovascular Risk
16%
Fundus Image
16%
Disease Progression
16%
Treatment Response
16%
Disease Burden
16%
Age-related Macular Degeneration
16%
Clinical Data
16%
Electronic Health Records
16%
Risk Disease
16%
Unmet Needs
16%
Refractive Error
16%
Optical Coherence Tomography
16%
Deep Learning Methods
16%
Glaucoma
16%
Radiology
16%
Mathematical Model
16%
Glaucoma Progression
16%
Disease Treatment
16%
Visual Field
16%
Screening Diagnosis
16%
Vision Impairment
16%
Disease Pattern
16%
Prospective Clinical Trial
16%
Healthcare
16%
Natural Language Processing
16%
Clinical Aspects
16%
Artificial Intelligence
16%
Neovascular Age-related Macular Degeneration (nAMD)
16%
Retinopathy of Prematurity
16%
Social Media
16%
Public Health Importance
16%
Diabetic Retinopathy
16%
Technical Aspects
16%
Diabetic Macular Edema
16%
Automotive Industry
16%
Computer Graphics
16%
Retinal Diseases
16%
Internet of Things
16%
Ophthalmic Practice
16%
Artificial Intelligence Machine Learning
16%
Clinical Adoption
16%
Speech Recognition
16%
Potential Challenges
16%
Machine Learning Applications
16%
Image Recognition
16%
Clinical Ophthalmology
16%
Graphics Processing Unit
16%
Robust Performance
16%
Motion Recognition
16%
Medicine and Dentistry
Ophthalmology
100%
Artificial Intelligence
100%
Glaucoma
33%
Eye Disease
33%
Diseases
33%
Radiology
16%
Public Health
16%
Clinical Trial
16%
Electronic Health Record
16%
Optical Coherence Tomography
16%
Treatment Response
16%
Cardiovascular Risk
16%
Refractive Error
16%
Prospective Study
16%
Disease Burden
16%
Visual Field
16%
Photograph
16%
Clinical Feature
16%
Dermatology
16%
Medicine
16%
Language Processing
16%
Age Related Macular Degeneration
16%
Visual Impairment
16%
Wet Macular Degeneration
16%
Diabetic Retinopathy
16%
Diabetic Macular Edema
16%
Retinopathy of Prematurity
16%
Retina Disease
16%
Vision Impairment
16%
Technical Aspect
16%