Discussion
diabetic, vision loss
MICROANEURYSMS
In this prospective study, we demonstrate the capability of our DL model to prioritize diabetic patients for a subsequent screening visit, based on their personalized risk of DR progression. Our evaluation assessed the model's clinical usefulness during COVID-19 recovery efforts in Thailand, with the objective of mitigating delays in scheduling follow-up screening appointments for those at higher risk. The DL model was implemented at sites with high patient volumes, where prioritization of patients based on DR risk could potentially lead to a more efficient allocation of resources and ensure that patients with a higher risk of DR progression receive timely eye clinic visits for appropriate management. Furthermore, our approach directly tackles the problem of optimizing screening intervals by risk stratifying the two largest groups of patients: those without any DR and those with mild DR.The proposed screening order determined by the DL model (approach D) showed a significantly higher sensitivity (defined as fraction of patients who were MOD+ at second screen who were screened in the first 50% of subsequent screens) compared with baseline methods (approach A and B). The observed screening order (approach E) was highly consistent with the model proposed order at three of the four screening sites, with the exception of the rural Phrao site. At the three consistent-ordering sites (i.e., excluding Phrao), our model successfully provided a more accurate personalized risk assessment to optimize screening intervals. As a result, a greater proportion of patients at higher risk were scheduled for their subsequent annual visit prior to those with lower risk, compared with the baseline methods, ensuring overall earlier detection of progression to MOD+. However, owing to few MOD+ cases at these three sites, there was a lack of power to measure a significant improvement in sensitivity compared with the baseline approaches. Including Phrao in the aggregate analysis, our results showed that simply ordering patients by their baseline grade (mild DR followed by no DR) for the subsequent visit identified 44.3% of those that progressed to MOD+ at 10.2% screened. Although the model proposed order and rankings by grade and HbA1c also demonstrated the same sensitivity at 10.2% screened, both quickly outperformed the baseline approaches as the fraction screened increased, suggesting that both the DL model and HbA1c were good signals for prioritizing patients for subsequent screenings. A notable advantage of the DL model compared with rankings by baseline grade and HbA1c is that it solely requires CFPs (which are already taken for DR screening purposes) as an input, making it a more practical solution for implementation if HbA1c values are not already available. However, in settings where the DL system cannot be deployed, or where HbA1c monitoring is routine, it may be practical and feasible to prioritize subsequent screening visits based on baseline grade and decreasing HbA1c levels.This study provides data not just on the DL model's performance, but also real-world insights on implementation of the solution. Three of the four evaluated sites successfully implemented the DL model into real-world practice by following the model proposed order for the majority of patients. However, several real-world deployment challenges were encountered during the study. For instance, the attendance order was disrupted by patients who missed their appointment and required rescheduling during the follow-up period. Additionally, patients in certain rural areas traveled to the screening site in groups using prearranged transport, making it challenging to adhere to the model proposed order. This was particularly the case at the Phrao site, which had the greatest number of cases overall and also most of the cases that progressed to MOD+ at the subsequent screen. Because Phrao comprised the majority of patients and MOD+, this factor substantially impacted the sensitivity results using the observed order. Nevertheless, our experience demonstrates the complexities that impact the real-world use of artificial intelligence in health care and highlights the importance of careful evaluation, design, and implementation of systems for improved artificial intelligence delivery.This work aims to personalize follow-ups according to the patient's risk of progression, which may result in some patients being seen sooner or later than currently recommended guidelines. International guidelines recommend annual DR screening for those without DR or with mild DRLimitations of this study include DR grading variability, particularly for subtle retinal changes such as microaneurysms.In conclusion, our study demonstrates the ability of a DL model to help schedule follow-up visits for the two largest subgroups of DR patients based on their risk of progression. To our knowledge, this is the first real-world deployment of a DR progression model in a health care setting. Our findings suggest that such a model could enable earlier identification and treatment of patients at greatest risk of progression before irreversible vision loss occurs. At the same time, it could decrease the burden of screening attendance for lower risk patients by extending their follow-up intervals. However, additional studies are needed to further validate the DL model with a robust dataset for analysis and assess its scalability in screening programs. Further research is needed to evaluate the long-term impact of adapting screening intervals to reflect personalized risk profiles on cost-effectiveness, clinical management, and patient outcomes.