|Title||AI-Human Hybrid Workflow Enhances Teleophthalmology for the Detection of Diabetic Retinopathy.|
|Publication Type||Journal Article|
|Year of Publication||2023|
|Authors||Dow, Eliot R., Khan Nergis C., Chen Karen M., Mishra Kapil, Perera Chandrashan, Narala Ramsudha, Basina Marina, Dang Jimmy, Kim Michael, Levine Marcie, Phadke Anuradha, Tan Marilyn, Weng Kirsti, Do Diana V., Moshfeghi Darius M., Mahajan Vinit B., Mruthyunjaya Prithvi, Leng Theodore, and Myung David|
|Date Published||2023 Dec|
OBJECTIVE: Detection of diabetic retinopathy (DR) outside of specialized eye care settings is an important means of access to vision-preserving health maintenance. Remote interpretation of fundus photographs acquired in a primary care or other nonophthalmic setting in a store-and-forward manner is a predominant paradigm of teleophthalmology screening programs. Artificial intelligence (AI)-based image interpretation offers an alternative means of DR detection. IDx-DR (Digital Diagnostics Inc) is a Food and Drug Administration-authorized autonomous testing device for DR. We evaluated the diagnostic performance of IDx-DR compared with human-based teleophthalmology over 2 and a half years. Additionally, we evaluated an AI-human hybrid workflow that combines AI-system evaluation with human expert-based assessment for referable cases.
DESIGN: Prospective cohort study and retrospective analysis.
PARTICIPANTS: Diabetic patients ≥ 18 years old without a prior DR diagnosis or DR examination in the past year presenting for routine DR screening in a primary care clinic.
METHODS: Macula-centered and optic nerve-centered fundus photographs were evaluated by an AI algorithm followed by consensus-based overreading by retina specialists at the Stanford Ophthalmic Reading Center. Detection of more-than-mild diabetic retinopathy (MTMDR) was compared with in-person examination by a retina specialist.
MAIN OUTCOME MEASURES: Sensitivity, specificity, accuracy, positive predictive value, and gradability achieved by the AI algorithm and retina specialists.
RESULTS: The AI algorithm had higher sensitivity (95.5% sensitivity; 95% confidence interval [CI], 86.7%-100%) but lower specificity (60.3% specificity; 95% CI, 47.7%-72.9%) for detection of MTMDR compared with remote image interpretation by retina specialists (69.5% sensitivity; 95% CI, 50.7%-88.3%; 96.9% specificity; 95% CI, 93.5%-100%). Gradability of encounters was also lower for the AI algorithm (62.5%) compared with retina specialists (93.1%). A 2-step AI-human hybrid workflow in which the AI algorithm initially rendered an assessment followed by overread by a retina specialist of MTMDR-positive encounters resulted in a sensitivity of 95.5% (95% CI, 86.7%-100%) and a specificity of 98.2% (95% CI, 94.6%-100%). Similarly, a 2-step overread by retina specialists of AI-ungradable encounters improved gradability from 63.5% to 95.6% of encounters.
CONCLUSIONS: Implementation of an AI-human hybrid teleophthalmology workflow may both decrease reliance on human specialist effort and improve diagnostic accuracy.
FINANCIAL DISCLOSURES: Proprietary or commercial disclosure may be found after the references.
|Alternate Journal||Ophthalmol Sci|
|PubMed Central ID||PMC10336195|