Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society · 2026
AI-driven retinal image quality monitoring in diabetic retinopathy screening: A retrospective study identifying actionable insights to improve imaging protocols.
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Pinto Imanol, Olazarán Álvaro, Jurio David, de la Osa Borja, Sainz Miguel, Oscoz Aritz, Ballaz Jerónimo, Gorricho Javier, Galar Mikel, Andonegui José
Аннотация
OBJECTIVE: To verify the applicability of retinal image quality (RIQ) models in real-world diabetic retinopathy screening for uncovering actionable insights to improve imaging protocols.
MATERIALS AND METHODS: NaIA-RD, a custom AI system developed by the University Hospital of Navarre (Spain) for diabetic retinopathy screening, was employed to monitor retinal image quality (RIQ) across multiple imaging sites within the hospital. A large retrospective dataset consisting of 55,801 routine retinal images collected over a period of 3.6 years was compiled for this purpose. Additionally, two convolutional neural networks, trained on external public datasets (EyeQ and DeepDRiD), were used as independent comparators. The longitudinal RIQ outputs from NaIA-RD, EyeQ, and DeepDRiD models were then analyzed to assess their alignment with clinical decisions.
RESULTS: All three models identified similar differences in RIQ across imaging sites, camera models, and imaging technicians. Ungradable rates varied widely among sites, ranging from 2.23% to 28.23%. These differences evolved over time due to changes in data distribution, or data drifts. Among the models, the one trained with DeepDRiD demonstrated the highest agreement with clinicians, achieving an Average Precision of 0.431, compared to 0.389 for NaIA-RD and 0.392 for EyeQ.
DISCUSSION: Monitoring RIQ revealed actionable insights, such as identifying differences related to camera models and technician experience, suggesting potential benefits from targeted training and imaging protocol standardization. Comparing outputs from multiple models strengthened the reliability of observed trends.
CONCLUSION: AI tools with modular design and detailed RIQ scoring can effectively monitor clinical imaging workflows, enabling data-driven healthcare quality improvement initiatives.
MATERIALS AND METHODS: NaIA-RD, a custom AI system developed by the University Hospital of Navarre (Spain) for diabetic retinopathy screening, was employed to monitor retinal image quality (RIQ) across multiple imaging sites within the hospital. A large retrospective dataset consisting of 55,801 routine retinal images collected over a period of 3.6 years was compiled for this purpose. Additionally, two convolutional neural networks, trained on external public datasets (EyeQ and DeepDRiD), were used as independent comparators. The longitudinal RIQ outputs from NaIA-RD, EyeQ, and DeepDRiD models were then analyzed to assess their alignment with clinical decisions.
RESULTS: All three models identified similar differences in RIQ across imaging sites, camera models, and imaging technicians. Ungradable rates varied widely among sites, ranging from 2.23% to 28.23%. These differences evolved over time due to changes in data distribution, or data drifts. Among the models, the one trained with DeepDRiD demonstrated the highest agreement with clinicians, achieving an Average Precision of 0.431, compared to 0.389 for NaIA-RD and 0.392 for EyeQ.
DISCUSSION: Monitoring RIQ revealed actionable insights, such as identifying differences related to camera models and technician experience, suggesting potential benefits from targeted training and imaging protocol standardization. Comparing outputs from multiple models strengthened the reliability of observed trends.
CONCLUSION: AI tools with modular design and detailed RIQ scoring can effectively monitor clinical imaging workflows, enabling data-driven healthcare quality improvement initiatives.