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Editorial
. 2023 Feb 14:10:1145522.
doi: 10.3389/fmed.2023.1145522. eCollection 2023.

Editorial: Big data and artificial intelligence in ophthalmology

Sahil Thakur  1 , Tyler Hyungtaek Rim  1   2 , Darren S J Ting  3   4   5 , Yi-Ting Hsieh  6   7 , Tae-Im Kim  8   9
Affiliations
Editorial

Editorial: Big data and artificial intelligence in ophthalmology

Sahil Thakur et al. Front Med (Lausanne). .
No abstract available

Keywords: artificial intelligence; big data; deep learning; machine learning; ophthalmology.

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Conflict of interest statement

TR was employed by Mediwhale Inc. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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  • Editorial on the Research Topic Big data and artificial intelligence in ophthalmology

References

    1. Cheng CY, Soh ZD, Majithia S, Thakur S, Rim TH, Tham YC, et al. . Big data in ophthalmology. Asia Pac J Ophthalmol. (2020) 9:291–8. 10.1097/APO.0000000000000304 - DOI - PubMed
    1. Rim TH, Lee G, Kim Y, Tham YC, Lee CJ, Baik SJ, et al. . Prediction of systemic biomarkers from retinal photographs: development and validation of deep-learning algorithms. Lancet Digit Health. (2020) 2:e526–36. 10.1016/S2589-7500(20)30216-8 - DOI - PubMed
    1. Rim TH, Lee CJ, Tham YC, Cheung N, Yu M, Lee G, et al. . Deep-learning-based cardiovascular risk stratification using coronary artery calcium scores predicted from retinal photographs. Lancet Digit Health. (2021) 3:e306–16. 10.1016/S2589-7500(21)00043-1 - DOI - PubMed
    1. Enright JM, Karacal H, Tsai LM. Floppy iris syndrome and cataract surgery. Curr Opin Ophthalmol. (2017) 28:29–34. 10.1097/ICU.0000000000000322 - DOI - PubMed

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