A New Approach to Staging Diabetic Eye Disease

Channa, Roomasa; Wolf, Risa M.; Simo, Rafael; Brigell, Mitchell; Fort, Patrice; Curcio, Christine; Lynch, Stephanie; Verbraak, Frank; Abramoff, Michael D.; Abramoff, Michael D.; Channa, Roomasa; Wolf, Risa M.; Simo, Rafael; Brigell, Mitch; Fort, Patrice; Curcio, Christine; Lynch, Stephanie; Verbraak, Frank; Gardner, Thomas W.

doi:10.1016/j.xops.2023.100420

Ophthalmology Science

2024

DOI: 10.1016/j.xops.2023.100420

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A New Approach to Staging Diabetic Eye Disease

Roomasa Channa,

Risa M. Wolf,

Rafael Simo

et al.

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2024

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Cited by 5 publications

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Mitigation of AI adoption bias through an improved autonomous AI system for diabetic retinal disease

Abràmoff,

Lavin,

Jakubowski

et al. 2024

npj Digit. Med.

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Where adopted, Autonomous artificial Intelligence (AI) for Diabetic Retinal Disease (DRD) resolves longstanding racial, ethnic, and socioeconomic disparities, but AI adoption bias persists. This preregistered trial determined sensitivity and specificity of a previously FDA authorized AI, improved to compensate for lower contrast and smaller imaged area of a widely adopted, lower cost, handheld fundus camera (RetinaVue700, Baxter Healthcare, Deerfield, IL) to identify DRD in participants with diabetes without known DRD, in primary care. In 626 participants (1252 eyes) 50.8% male, 45.7% Hispanic, 17.3% Black, DRD prevalence was 29.0%, all prespecified non-inferiority endpoints were met and no racial, ethnic or sex bias was identified, against a Wisconsin Reading Center level I prognostic standard using widefield stereoscopic photography and macular Optical Coherence Tomography. Results suggest this improved autonomous AI system can mitigate AI adoption bias, while preserving safety and efficacy, potentially contributing to rapid scaling of health access equity. ClinicalTrials.gov NCT05808699 (3/29/2023).

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Mitigation of AI adoption bias through an improved autonomous AI system for diabetic retinal disease

Abràmoff,

Lavin,

Jakubowski

et al. 2024

npj Digit. Med.

View full text Add to dashboard Cite

show abstract

Diabetes Renders Photoreceptors Susceptible to Retinal Ischemia-Reperfusion Injury

Antonetti,

Lin,

Shanmugam

et al. 2024

Invest. Ophthalmol. Vis. Sci.

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Data Harmonization, Standardization, and Collaboration for Diabetic Retinal Disease (DRD) Research: Report From the 2024 Mary Tyler Moore Vision Initiative Workshop on Data

Domalpally,

Fickweiler,

Levine

et al. 2024

Trans. Vis. Sci. Tech.

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A New Approach to Staging Diabetic Eye Disease

Cited by 5 publications

References 79 publications

Mitigation of AI adoption bias through an improved autonomous AI system for diabetic retinal disease

Mitigation of AI adoption bias through an improved autonomous AI system for diabetic retinal disease

Diabetes Renders Photoreceptors Susceptible to Retinal Ischemia-Reperfusion Injury

Data Harmonization, Standardization, and Collaboration for Diabetic Retinal Disease (DRD) Research: Report From the 2024 Mary Tyler Moore Vision Initiative Workshop on Data

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