Through the eyes into the brain, using artificial intelligence
Publikation: Bidrag til tidsskrift › Review › Forskning › fagfællebedømt
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Through the eyes into the brain, using artificial intelligence. / Sathianvichitr, Kanchalika; Lamoureux, Oriana; Nakada, Sakura; Tang, Zhiqun; Schmetterer, Leopold; Chen, Christopher; Cheung, Carol Y.; Najjar, Raymond P.; Milea, Dan.
I: Annals of the Academy of Medicine, Singapore, Bind 52, Nr. 2, 2023, s. 88-95.Publikation: Bidrag til tidsskrift › Review › Forskning › fagfællebedømt
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TY - JOUR
T1 - Through the eyes into the brain, using artificial intelligence
AU - Sathianvichitr, Kanchalika
AU - Lamoureux, Oriana
AU - Nakada, Sakura
AU - Tang, Zhiqun
AU - Schmetterer, Leopold
AU - Chen, Christopher
AU - Cheung, Carol Y.
AU - Najjar, Raymond P.
AU - Milea, Dan
PY - 2023
Y1 - 2023
N2 - INTRODUCTION: Detection of neurological conditions is of high importance in the current context of increasingly ageing populations. Imaging of the retina and the optic nerve head represents a unique opportunity to detect brain diseases, but requires specific human expertise. We review the current outcomes of artificial intelligence (AI) methods applied to retinal imaging for the detection of neurological and neuro-ophthalmic conditions. METHOD: Current and emerging concepts related to the detection of neurological conditions, using AI-based investigations of the retina in patients with brain disease were examined and summarised. RESULTS: Papilloedema due to intracranial hypertension can be accurately identified with deep learning on standard retinal imaging at a human expert level. Emerging studies suggest that patients with Alzheimer's disease can be discriminated from cognitively normal individuals, using AI applied to retinal images. CONCLUSION: Recent AI-based systems dedicated to scalable retinal imaging have opened new perspectives for the detection of brain conditions directly or indirectly affecting retinal structures. However, further validation and implementation studies are required to better understand their potential value in clinical practice.
AB - INTRODUCTION: Detection of neurological conditions is of high importance in the current context of increasingly ageing populations. Imaging of the retina and the optic nerve head represents a unique opportunity to detect brain diseases, but requires specific human expertise. We review the current outcomes of artificial intelligence (AI) methods applied to retinal imaging for the detection of neurological and neuro-ophthalmic conditions. METHOD: Current and emerging concepts related to the detection of neurological conditions, using AI-based investigations of the retina in patients with brain disease were examined and summarised. RESULTS: Papilloedema due to intracranial hypertension can be accurately identified with deep learning on standard retinal imaging at a human expert level. Emerging studies suggest that patients with Alzheimer's disease can be discriminated from cognitively normal individuals, using AI applied to retinal images. CONCLUSION: Recent AI-based systems dedicated to scalable retinal imaging have opened new perspectives for the detection of brain conditions directly or indirectly affecting retinal structures. However, further validation and implementation studies are required to better understand their potential value in clinical practice.
U2 - 10.47102/annals-acadmedsg.2022369
DO - 10.47102/annals-acadmedsg.2022369
M3 - Review
C2 - 36880820
AN - SCOPUS:85149589523
VL - 52
SP - 88
EP - 95
JO - Annals of the Academy of Medicine, Singapore
JF - Annals of the Academy of Medicine, Singapore
SN - 0304-4602
IS - 2
ER -
ID: 373878662