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Artificial Intelligence in Ophthalmology: Revolutionizing Age-Related Macular Degeneration and Other Ophthalmologic Conditions

Artificial Intelligence in Ophthalmology: Revolutionizing Age-Related Macular Degeneration and Other Ophthalmologic Conditions

Med-IQ Express

Developed in collaboration
Med-IQ      Duke Medicine

Online Course | Specialties: Ophthalmology
Released: 5/27/2021
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Expires: 5/26/2022
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Max Credits: 0.5
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Faculty
Eleonora Lad, MD, PhD
Associate Professor of Ophthalmology, Vitreoretinal Diseases
Duke University Medical Center
Durham, NC
 
Activity Planners
Susan Kuhn, MHSc
Manager, Educational Strategy and Content
Med-IQ
Baltimore, MD

Jane Frutchey, MS
Managing Editor
Med-IQ
Baltimore, MD

Samantha Gordon, MS
Accreditation Manager
Med-IQ
Baltimore, MD

Amy Sison
Director of CME
Med-IQ
Baltimore, MD

Writer
Jenny Cai, MSc
Montreal, Quebec, Canada
 
Learning Objective
Upon completion, participants should be able to:

  • Identify the current and emerging role of artificial intelligence in ophthalmology and AMD in particular 

Target Audience
This activity is intended for ophthalmologists.
 
Statement of Need
Improvements are needed in the detection, assessment, treatment, and prevention of ophthalmologic conditions that can cause irreversible blindness such as glaucoma, diabetic retinopathy, retinopathy of prematurity, and age-related macular degeneration (AMD). Advances in computational technology are now enabling the rapid application of artificial intelligence (AI) and machine learning in ophthalmologic conditions for which AI can aid in image interpretation, diagnosis, referral management, risk stratification, and prognostication. Some systems are achieving accuracy similar to or greater than human clinicians. Deep learning (DL) algorithms can recognize pigmentation, exudate, hemorrhage, and drusen and determine their correlation with AMD stages. Furthermore, DL-AMD grading has been shown to be comparable to human grading for the 4-step classification and is showing promise for detailed severity grading and estimating risk of progression to advanced AMD. As the use of AI in eye disease is rapidly evolving, ophthalmologists require an understanding of this technology and its potential role within clinical practice.

Collaborator Statement
This activity was developed by Med-IQ in collaboration with Duke Health.
Med-IQ      Duke Medicine

Accreditation/Designation Statements
Med-IQ is accredited by the Accreditation Council for Continuing Medical Education (ACCME) to provide continuing medical education for physicians.
 
Med-IQ designates this enduring material for a maximum of 0.5 AMA PRA Category 1 Credit™. Physicians should claim only the credit commensurate with the extent of their participation in the activity.
 
Medium/Method of Participation
This CME activity consists of a 0.5-credit online publication. To receive credit, read the introductory CE material, read the publication, and complete the evaluation, attestation, and post-test, answering at least 70% of the post-test questions correctly.  
 
Initial Release Date: May 27, 2021
Expiration Date: May 26, 2022
Estimated Time to Complete This Activity: 30 minutes

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Disclosure Statement
The content of this activity has been peer reviewed and has been approved for compliance. The faculty and contributors have indicated the following financial relationships, which have been resolved through an established COI resolution process, and have stated that these reported relationships will not have any impact on their ability to give an unbiased presentation. 

Eleonora Lad, MD, PhD
Consulting fees/advisory boards: Alexion, Annexon Biosciences, Apellis, 4D Molecular Therapeutics, F. Hoffmann-La Roche Ltd., Iveric Bio, Retrotope
Contracted research: Apellis, F. Hoffmann-La Roche Ltd., Genentech, LumiThera, Novartis Pharmaceuticals Corporation
Ownership interest (stocks/stock options – excluding mutual funds): Retrotope 

The writer, peer reviewers, and activity planners have no financial relationships to disclose. 
 
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For CME questions or comments about this activity, please contact Med-IQ. Call (toll-free) 866 858 7434 or email info@med-iq.com.

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The information provided through this activity is for continuing education purposes only and is not meant to substitute for the independent medical judgment of a physician relative to diagnostic and treatment options of a specific patient’s medical condition.

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Copyright
© 2021 Duke University Health System

Abstract

Here are the key takeaways from this activity. Deeper insights and evidence, plus an opportunity to receive credit, are available at the "Continue" button below.

  • Recent advances in computational technology are enabling the rapid application of artificial intelligence (AI), deep learning (DL), and machine learning (subtypes of AI) in ophthalmology; currently, AI can aid in image interpretation, diagnosis, referral management, risk stratification, and prognostication
  • AI applied to fundus photographs, optical coherence tomography, anterior segment photographs, and corneal topography is being evaluated for use in aiding the management of a variety of ophthalmologic conditions, including diabetic retinopathy, glaucoma, retinopathy of prematurity, and cataracts
  • In age-related macular degeneration (AMD), AI can play a critical role in improving earlier diagnosis, screening, prognosis, and prediction, with some systems achieving accuracy similar to or greater than human clinicians and retinal specialists
    • DL algorithms can recognize pigmentation, exudates, hemorrhage, or drusen and determine their correlation with AMD stages
    • DL tools are being evaluated for their use in predicting progression from early AMD to late exudative AMD using automatic tissue segmentation to identify anatomic changes; some computational DL models have been designed to classify AMD based on the presence of exudative changes, without the use of image segmentation
    • DL-AMD grading has been shown to be comparable to human grading for the 4-step classification and is promising for determining AMD-detailed severity grading and for estimating 5-year risk of progression to advanced AMD
  • DL technology has the potential to improve diagnosis and longitudinal care, individualize risk assessment, or be used as an ongoing screening or monitoring tool

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