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. 2021 May 19;36(4):250-257.
doi: 10.1080/08820538.2021.1893351. Epub 2021 Mar 18.

Biomarkers for Progression in Diabetic Retinopathy: Expanding Personalized Medicine through Integration of AI with Electronic Health Records

Cris Martin P Jacoba  1   2 , Leo Anthony Celi  3   4   5 , Paolo S Silva  1   2
Affiliations

Biomarkers for Progression in Diabetic Retinopathy: Expanding Personalized Medicine through Integration of AI with Electronic Health Records

Cris Martin P Jacoba et al. Semin Ophthalmol. .

Abstract

The goal of personalized diabetes eye care is to accurately predict in real-time the risk of diabetic retinopathy (DR) progression and visual loss. The use of electronic health records (EHR) provides a platform for artificial intelligence (AI) algorithms that predict DR progression to be incorporated into clinical decision-making. By implementing an algorithm on data points from each patient, their risk for retinopathy progression and visual loss can be modeled, allowing them to receive timely treatment. Data can guide algorithms to create models for disease and treatment that may pave the way for more personalized care. Currently, there exist numerous challenges that need to be addressed before reliably building and deploying AI algorithms, including issues with data quality, privacy, intellectual property, and informed consent.

Keywords: Artificial intelligence; biomarkers; diabetic retinopathy; electronic health records; personalized medicine.

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Conflict of interest statement

DISCLOSURE STATEMENT

The authors declare that they have no financial interests related to this research

Figures

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Figure 1.
Summary of central concepts.
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References

    1. Bellemo V, Lim G, Rim TH, et al. Artificial intelligence screening for diabetic retinopathy: the real-world emerging application. Curr Diab Rep. 2019;19(9):72. doi:10.1007/s11892-019-1189-3. - DOI - PubMed
    1. Saeedi P, Petersohn I, Salpea P, et al. Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: results from the international diabetes federation diabetes atlas. Diabetes Res Clin Pract. 2019;157:107843. doi:10.1016/j.diabres.2019.107843. - DOI - PubMed
    1. Chakrabarti R, Harper CA, Keeffe JE. Diabetic retinopathy management guidelines. Expert Rev Ophthalmol. 2012;7(5):417–439. doi:10.1586/eop.12.52. - DOI
    1. Shin P, Finnegan B. Assessing the Need for On-site Eye Care Professionals in Community Health Centers. 2009. - PubMed
    1. Hazin R, Colyer M, Lum F, et al. Revisiting diabetes 2000: challenges in establishing nationwide diabetic retinopathy prevention programs. Am J Ophthalmol. 2011;152(5):723–729. doi:10.1016/j.ajo.2011.06.022. - DOI - PubMed

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