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  1. Article ; Online: Entering the Exciting Era of Artificial Intelligence and Big Data in Ophthalmology.

    Lee, Cecilia S / Brandt, James D / Lee, Aaron Y

    Ophthalmology science

    2024  Volume 4, Issue 2, Page(s) 100469

    Language English
    Publishing date 2024-01-19
    Publishing country Netherlands
    Document type Editorial
    ISSN 2666-9145
    ISSN (online) 2666-9145
    DOI 10.1016/j.xops.2024.100469
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Retinal Biomarkers for Alzheimer Disease: The Facts and the Future.

    Yuan, Amy / Lee, Cecilia S

    Asia-Pacific journal of ophthalmology (Philadelphia, Pa.)

    2022  Volume 11, Issue 2, Page(s) 140–148

    Abstract: Abstract: Alzheimer disease (AD) is a significant cause of morbidity and mortality worldwide, with limited treatment options and considerable diagnostic challenges. Identification and validation of retinal changes that correlate with clinicopathologic ... ...

    Abstract Abstract: Alzheimer disease (AD) is a significant cause of morbidity and mortality worldwide, with limited treatment options and considerable diagnostic challenges. Identification and validation of retinal changes that correlate with clinicopathologic features of AD could provide a noninvasive method of screening and monitoring progression of disease, with notable implications for developing new therapies, particularly in its preclinical stages. Retinal biomarkers that have been studied to date include structural changes in neurosensory retinal layers, alterations in vascular architecture and function, and pathologic deposition of proteins within the retina, which have all demonstrated variable correlation with the presence of preclinical or clinical AD. Evolution of specialized retinal imaging modalities and advances in artificial intelligence hold great promise for future study in this burgeoning field. The current status of research in retinal biomarkers, and some of the challenges that will need to be addressed in future work, are reviewed herein.
    MeSH term(s) Alzheimer Disease/diagnostic imaging ; Alzheimer Disease/metabolism ; Artificial Intelligence ; Biomarkers ; Humans ; Mass Screening ; Retina
    Chemical Substances Biomarkers
    Language English
    Publishing date 2022-05-03
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2756329-7
    ISSN 2162-0989 ; 2162-0989
    ISSN (online) 2162-0989
    ISSN 2162-0989
    DOI 10.1097/APO.0000000000000505
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: Geographic Distribution of Visual Impairment and Access to Ophthalmologists.

    Lu, Randy Y / Lee, Cecilia S

    JAMA ophthalmology

    2022  Volume 140, Issue 6, Page(s) 585–586

    MeSH term(s) Humans ; Ophthalmologists ; Ophthalmology ; Vision, Low
    Language English
    Publishing date 2022-06-13
    Publishing country United States
    Document type Journal Article ; Comment
    ZDB-ID 2701705-9
    ISSN 2168-6173 ; 2168-6165
    ISSN (online) 2168-6173
    ISSN 2168-6165
    DOI 10.1001/jamaophthalmol.2022.1222
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    Ul I Zs.26: Show issues Location:
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  4. Article ; Online: Real-time Augmented Reality-The Next Frontier for Ophthalmic Surgery.

    Yuan, Amy / Lee, Cecilia S

    JAMA ophthalmology

    2022  Volume 140, Issue 2, Page(s) 177–178

    MeSH term(s) Augmented Reality ; Humans ; Ophthalmology ; Surgery, Computer-Assisted ; Virtual Reality
    Language English
    Publishing date 2022-01-12
    Publishing country United States
    Document type Journal Article ; Comment
    ZDB-ID 2701705-9
    ISSN 2168-6173 ; 2168-6165
    ISSN (online) 2168-6173
    ISSN 2168-6165
    DOI 10.1001/jamaophthalmol.2021.5745
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    Ul I Zs.26: Show issues Location:
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  5. Article ; Online: Imaging Amyloid and Tau in the Retina: Current Research and Future Directions.

    Tang, Mira Y / Blazes, Marian S / Lee, Cecilia S

    Journal of neuro-ophthalmology : the official journal of the North American Neuro-Ophthalmology Society

    2023  Volume 43, Issue 2, Page(s) 168–179

    Abstract: Background: The retina is a key focus in the search for biomarkers of Alzheimer's disease (AD) because of its accessibility and shared development with the brain. The pathological hallmarks of AD, amyloid beta (Aβ), and hyperphosphorylated tau (pTau) ... ...

    Abstract Background: The retina is a key focus in the search for biomarkers of Alzheimer's disease (AD) because of its accessibility and shared development with the brain. The pathological hallmarks of AD, amyloid beta (Aβ), and hyperphosphorylated tau (pTau) have been identified in the retina, although histopathologic findings have been mixed. Several imaging-based approaches have been developed to detect retinal AD pathology in vivo. Here, we review the research related to imaging AD-related pathology in the retina and implications for future biomarker research.
    Evidence acquisition: Electronic searches of published literature were conducted using PubMed and Google Scholar.
    Results: Curcumin fluorescence and hyperspectral imaging are both promising methods for detecting retinal Aβ, although both require validation in larger cohorts. Challenges remain in distinguishing curcumin-labeled Aβ from background fluorescence and standardization of dosing and quantification methods. Hyperspectral imaging is limited by confounding signals from other retinal features and variability in reflectance spectra between individuals. To date, evidence of tau aggregation in the retina is limited to histopathologic studies. New avenues of research are on the horizon, including near-infrared fluorescence imaging, novel Aβ labeling techniques, and small molecule retinal tau tracers. Artificial intelligence (AI) approaches, including machine learning models and deep learning-based image analysis, are active areas of investigation.
    Conclusions: Although the histopathological evidence seems promising, methods for imaging retinal Aβ require further validation, and in vivo imaging of retinal tau remains elusive. AI approaches may hold the greatest promise for the discovery of a characteristic retinal imaging profile of AD. Elucidating the role of Aβ and pTau in the retina will provide key insights into the complex processes involved in aging and in neurodegenerative disease.
    MeSH term(s) Humans ; Amyloid beta-Peptides ; Neurodegenerative Diseases/pathology ; Curcumin ; Artificial Intelligence ; Alzheimer Disease/diagnostic imaging ; Retina/diagnostic imaging ; Retina/pathology ; Biomarkers
    Chemical Substances Amyloid beta-Peptides ; Curcumin (IT942ZTH98) ; Biomarkers
    Language English
    Publishing date 2023-02-03
    Publishing country United States
    Document type Review ; Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, N.I.H., Extramural
    ZDB-ID 1189901-3
    ISSN 1536-5166 ; 1070-8022
    ISSN (online) 1536-5166
    ISSN 1070-8022
    DOI 10.1097/WNO.0000000000001786
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    Zs.A 1670: Show issues Location:
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  6. Article: Understanding the Brain through Aging Eyes.

    Blazes, Marian / Lee, Cecilia S

    Advances in geriatric medicine and research

    2021  Volume 3, Issue 2

    Abstract: The eye and brain share common mechanisms of aging and disease, thus the retina is an essential source of accessible information about neurodegenerative processes occurring in the brain. Advances in retinal imaging have led to the discovery of many ... ...

    Abstract The eye and brain share common mechanisms of aging and disease, thus the retina is an essential source of accessible information about neurodegenerative processes occurring in the brain. Advances in retinal imaging have led to the discovery of many potential biomarkers of Alzheimer's disease, although further research is needed to validate these associations. Understanding the mechanisms of retinal disease in the context of aging will extend our knowledge of AD and may enable advancements in diagnosis, monitoring, and treatment.
    Language English
    Publishing date 2021-03-01
    Publishing country England
    Document type Journal Article
    DOI 10.20900/agmr20210008
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  7. Article ; Online: Artificial Intelligence and Diabetic Retinopathy: AI Framework, Prospective Studies, Head-to-head Validation, and Cost-effectiveness.

    Rajesh, Anand E / Davidson, Oliver Q / Lee, Cecilia S / Lee, Aaron Y

    Diabetes care

    2023  Volume 46, Issue 10, Page(s) 1728–1739

    Abstract: Current guidelines recommend that individuals with diabetes receive yearly eye exams for detection of referable diabetic retinopathy (DR), one of the leading causes of new-onset blindness. For addressing the immense screening burden, artificial ... ...

    Abstract Current guidelines recommend that individuals with diabetes receive yearly eye exams for detection of referable diabetic retinopathy (DR), one of the leading causes of new-onset blindness. For addressing the immense screening burden, artificial intelligence (AI) algorithms have been developed to autonomously screen for DR from fundus photography without human input. Over the last 10 years, many AI algorithms have achieved good sensitivity and specificity (>85%) for detection of referable DR compared with human graders; however, many questions still remain. In this narrative review on AI in DR screening, we discuss key concepts in AI algorithm development as a background for understanding the algorithms. We present the AI algorithms that have been prospectively validated against human graders and demonstrate the variability of reference standards and cohort demographics. We review the limited head-to-head validation studies where investigators attempt to directly compare the available algorithms. Next, we discuss the literature regarding cost-effectiveness, equity and bias, and medicolegal considerations, all of which play a role in the implementation of these AI algorithms in clinical practice. Lastly, we highlight ongoing efforts to bridge gaps in AI model data sets to pursue equitable development and delivery.
    MeSH term(s) Humans ; Artificial Intelligence ; Diabetic Retinopathy/diagnosis ; Prospective Studies ; Cost-Benefit Analysis ; Algorithms ; Diabetes Mellitus
    Language English
    Publishing date 2023-09-18
    Publishing country United States
    Document type Review ; Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
    ZDB-ID 441231-x
    ISSN 1935-5548 ; 0149-5992
    ISSN (online) 1935-5548
    ISSN 0149-5992
    DOI 10.2337/dci23-0032
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    Zs.A 1434: Show issues Location:
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    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (1.OG)
    ab Jg. 2022: Lesesaal (EG)
    Zs.MO 365: Show issues

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  8. Article: How Artificial Intelligence Can Transform Randomized Controlled Trials.

    Lee, Cecilia S / Lee, Aaron Y

    Translational vision science & technology

    2020  Volume 9, Issue 2, Page(s) 9

    MeSH term(s) Artificial Intelligence ; Randomized Controlled Trials as Topic ; Research Design
    Language English
    Publishing date 2020-02-12
    Publishing country United States
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
    ZDB-ID 2674602-5
    ISSN 2164-2591
    ISSN 2164-2591
    DOI 10.1167/tvst.9.2.9
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  9. Article ; Online: Clinical applications of continual learning machine learning.

    Lee, Cecilia S / Lee, Aaron Y

    The Lancet. Digital health

    2020  Volume 2, Issue 6, Page(s) e279–e281

    MeSH term(s) Data Management/methods ; Delivery of Health Care/statistics & numerical data ; Humans ; Machine Learning ; Pattern Recognition, Automated/methods
    Language English
    Publishing date 2020-12-17
    Publishing country England
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't ; Review
    ISSN 2589-7500
    ISSN (online) 2589-7500
    DOI 10.1016/S2589-7500(20)30102-3
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  10. Article ; Online: Defining Geographic Atrophy in the Asian Population Using the Asian Eye Epidemiology Consortium.

    Hojjatie, Sara L / Lee, Cecilia S

    Ophthalmology

    2020  Volume 127, Issue 10, Page(s) 1382–1383

    MeSH term(s) Asia/epidemiology ; Asians ; Geographic Atrophy/diagnosis ; Geographic Atrophy/epidemiology ; Humans ; Macular Degeneration ; Prevalence
    Language English
    Publishing date 2020-09-21
    Publishing country United States
    Document type Editorial ; Comment
    ZDB-ID 392083-5
    ISSN 1549-4713 ; 0161-6420
    ISSN (online) 1549-4713
    ISSN 0161-6420
    DOI 10.1016/j.ophtha.2020.05.009
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    Zs.MO 533: Show issues

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