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EyeArt artificial intelligence analysis of diabetic retinopathy in retinal screening events

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Abstract

Purpose

Early detection and treatment of diabetic retinopathy (DR) are critical for decreasing the risk of vision loss and preventing blindness. Community vision screenings may play an important role, especially in communities at higher risk for diabetes. To address the need for increased DR detection and referrals, we evaluated the use of artificial intelligence (AI) for screening DR.

Methods

Patient images of 124 eyes were obtained using a 45° Canon Non-Mydriatic CR-2 Plus AF retinal camera in the Department of Endocrinology Clinic (Newark, NJ) and in a community screening event (Newark, NJ). Images were initially classified by an onsite grader and uploaded for analysis by EyeArt, a cloud-based AI software developed by Eyenuk (California, USA). The images were also graded by an off-site retina specialist. Using Fleiss kappa analysis, a correlation was investigated between the three grading systems, the AI, onsite grader, and a US board-certified retina specialist, for a diagnosis of DR and referral pattern.

Results

The EyeArt results, onsite grader, and the retina specialist had a 79% overall agreement on the diagnosis of DR: 86 eyes with full agreement, 37 eyes with agreement between two graders, 1 eye with full disagreement. The kappa value for concordance on a diagnosis was 0.69 (95% CI 0.61–0.77), indicating substantial agreement. Referral patterns by EyeArt, the onsite grader, and the ophthalmologist had an 85% overall agreement: 96 eyes with full agreement, 28 eyes with disagreement. The kappa value for concordance on “whether to refer” was 0.70 (95% CI 0.60–0.80), indicating substantial agreement. Using the board-certified retina specialist as the gold standard, EyeArt had an 81% accuracy (101/124 eyes) for diagnosis and 83% accuracy (103/124 eyes) in referrals. For referrals, the sensitivity of EyeArt was 74%, specificity was 87%, positive predictive value was 72%, and negative predictive value was 88%.

Conclusions

This retrospective cross-sectional analysis offers insights into use of AI in diabetic screenings and the significant role it will play in automated detection of DR. The EyeArt readings were beneficial with some limitations in a community screening environment. These limitations included a decreased accuracy in the presence of cataracts and the functional cost of EyeArt uploads in a community setting.

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Abbreviations

AI:

Artificial intelligence

BMI:

Body mass index

DLAI:

Deep learning artificial intelligence

DR:

Diabetic retinopathy

T2DM:

Type II diabetes mellitus

VTDs:

Vision-threatening diseases

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Acknowledgements

This work was supported by the New Jersey Health Foundation.

Funding

Author Dr. N Bhagat receives grant funding from New Jersey Health Foundation. The New Jersey Health Foundation was not involved in the study design, data collection, analysis, interpretation, and in the decision to submit this article for publication. All other authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest, or non-financial interest in the subject matter or materials discussed in this manuscript.

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Authors and Affiliations

  1. The Institute of Ophthalmology and Visual Science (IOVS), Rutgers-New Jersey Medical School (Rutgers NJMS), 90 Bergen St., Suite 6100, Newark, NJ, 07103, USA

    Rita Vought, Victoria Vought, Megh Shah, Bernard Szirth & Neelakshi Bhagat

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  1. Rita Vought
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  2. Victoria Vought
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  3. Megh Shah
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  4. Bernard Szirth
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  5. Neelakshi Bhagat
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Contributions

Study conceptualization was completed by Ms. RV, Dr. NB, and Dr. BS. Dr. NB was responsible for funding acquisition. Data collection was performed by Ms. RV, Ms. VV, and Dr. BS. Images were read and analyzed by Dr NB. Data analysis was performed by Ms. VV and Mr. MS. All authors contributed to the writing of the manuscript. Dr. BS and Dr. NB were responsible for the supervision of the study. All authors approved the final manuscript.

Corresponding author

Correspondence to Neelakshi Bhagat.

Ethics declarations

Conflict of interest

Author Dr. B Szirth receives grant funding from New Jersey Health Foundation. The New Jersey Health Foundation was not involved in the study design, data collection, analysis, interpretation, and in the decision to submit this article for publication. All other authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest, or non-financial interest in the subject matter or materials discussed in this manuscript. The authors declare that they have no conflict of interest.

Ethics approval

This study was approved by the Rutgers University Institutional Review Board (Pro20140001070) and is in compliance with the World Medical Association’s Declaration of Helsinki.

Consent to participate

Informed consent was obtained from all individual participants included in the study.

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Not applicable.

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Vought, R., Vought, V., Shah, M. et al. EyeArt artificial intelligence analysis of diabetic retinopathy in retinal screening events. Int Ophthalmol 43, 4851–4859 (2023). https://doi.org/10.1007/s10792-023-02887-9

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