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Ophthalmology and Artificial Intelligence: Present or Future? A Diabetic Retinopathy Screening Perspective of the Pursuit for Fairness.

作者信息

Nakayama Luis Filipe, Ribeiro Lucas Zago, Malerbi Fernando Korn, Regatieri Caio Vinicius Saito

机构信息

Retina and Vitreous Department, São Paulo Federal University (UNIFESP), Sao Paulo, Brazil.

出版信息

Front Ophthalmol (Lausanne). 2022 May 10;2:898181. doi: 10.3389/fopht.2022.898181. eCollection 2022.

Abstract
摘要

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本文引用的文献

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Review of Machine Learning Applications Using Retinal Fundus Images.
Diagnostics (Basel). 2022 Jan 6;12(1):134. doi: 10.3390/diagnostics12010134.
2
A Radiology-focused Review of Predictive Uncertainty for AI Interpretability in Computer-assisted Segmentation.
Radiol Artif Intell. 2021 Sep 15;3(6):e210031. doi: 10.1148/ryai.2021210031. eCollection 2021 Nov.
6
Considerations for Artificial Intelligence Real-World Implementation in Ophthalmology: Providers' and Patients' Perspectives.
Asia Pac J Ophthalmol (Phila). 2021;10(3):299-306. doi: 10.1097/APO.0000000000000400.
7
Economic Evaluations of Artificial Intelligence in Ophthalmology.
Asia Pac J Ophthalmol (Phila). 2021 Jul 13;10(3):307-316. doi: 10.1097/APO.0000000000000403.
8
Deep learning for diabetic retinopathy detection and classification based on fundus images: A review.
Comput Biol Med. 2021 Aug;135:104599. doi: 10.1016/j.compbiomed.2021.104599. Epub 2021 Jun 25.
9
A global review of publicly available datasets for ophthalmological imaging: barriers to access, usability, and generalisability.
Lancet Digit Health. 2021 Jan;3(1):e51-e66. doi: 10.1016/S2589-7500(20)30240-5. Epub 2020 Oct 1.
10
Socioeconomic risk factors and age-related macular degeneration in the UK Biobank study.
BMJ Open Ophthalmol. 2021 Feb 23;6(1):e000585. doi: 10.1136/bmjophth-2020-000585. eCollection 2021.

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