The study was approved by the Erasmus Hospital HUB Ethics Committee (reference P2023/464). All participants provided informed consent, and all procedures were conducted in accordance with the Declaration of Helsinki and relevant institutional guidelines and regulations.

All adult patients (≥ 18 years) with a diagnosis of diabetes attending consultations at the Erasmus Medical Center were invited to participate between 10 January and 20 April 2024. Recruitment took place immediately following their usual diabetes consultation.

The AI-based screening model was developed by MONA.health, a Belgian healthcare AI company supported by KU Leuven and the Health Unit of VITO. The system autonomously screens for referable DR and diabetic macular edema and its development and validation details have been reported previously¹⁴. Briefly, the system is based on an ensemble of convolutional neural networks that first perform automated image quality assessment, followed by binary detection of referable DR and diabetic macular edema. Multiple convolutional neural network architectures are combined to generate a patient-level referral decision using a fixed operating threshold targeting high sensitivity, as previously described. The algorithm was trained and tested on a large multicenter private dataset from a US-based screening network including individuals of African descent, Asian, Caucasian, Latin American and unspecified ethnicity, as well as a publicly available dataset comprising mainly US and French images commonly used for benchmarking DR detection algorithms.

For each participant, a single 45-degree, non-dilated, color fundus photograph of each eye was acquired using a Crystalvue camera, centered between the optic disc and macula. The MONA.health software independently performs an automated image quality assessment, and requires at least one image of sufficient quality per eye to generate a report on the patient’s referral status for DR and diabetic macular edema. If the maximum predicted value for at least one eye exceeded the fixed thresholds (1.371 for DR and 0.38 for diabetic macular edema, corresponding to an operating point targeting 90% sensitivity), the patient was flagged for referral for one or both conditions. Referable DR was defined as moderate non-proliferative DR or worse, diabetic macular edema, or an ungradable image, based on the Early Treatment Diabetic Retinopathy Study (ETDRS) classification^14,15.

Prior to patient recruitment, nurses and a medical student (LB) received standardized one-to-one training by the same trainer, covering retinal image acquisition, troubleshooting for poor image quality, automated grading using the AI system and communication of results to patients. Training and supervision were provided until staff were comfortable performing data collection and imaging independently.

Following image acquisition, retinal images were uploaded to the AI system, which generated an automated referral status report. Results were orally communicated to patients and incorporated into their medical records at the time of screening. Patients who tested positive for referable DR and/or diabetic macular edema and were previously undiagnosed, or who had insufficient image quality, were invited to consult an ophthalmologist. The Ophthalmology Department at Erasmus Hospital contacted these patients for follow-up if needed.

To determine the accuracy of the AI screening model, all retinal images were manually graded for referable DR by a retinal specialist (EM) at Erasmus Hospital using the ETDRS classification. As the reference standard, referable DR was defined as moderate non-proliferative DR or vision-threatening stages (severe non-proliferative and proliferative DR) in at least one eye. When grading differed between eyes, the presence of referable DR in at least one eye was considered sufficient to classify the patient as having referable DR, in accordance with ETDRS criteria. The AI system applies the same clinical definition of referable DR but provides a binary referable/non-referable output without further severity stratification. Human grading was performed exclusively for reference standard evaluation and was not part of the AI screening workflow. Patients identified as false negatives (no DR reported by AI in either eye but DR detected on manual grading) were contacted by telephone and advised to attend an ophthalmologic consultation.

All analyses were performed using RStudio (version 4.3.3). Patients with missing reference standard grading due to missing images in at least one eye (small pupil, poor fixation, technical error, upload error) or ungradable images according to either the AI system or manual grading were excluded from the primary analysis.

Sensitivity, specificity, positive predictive value and negative predictive value for referable DR detection were estimated with 95% exact binomial confidence intervals. AUC and corresponding confidence intervals were estimated using a bootstrap technique with 1,000 replicates. All diagnostic accuracy analyses were performed at the patient level. The highest predicted probability across both eyes was used for classification and AUC estimation, as referral is triggered when referable DR is detected in at least one eye.

The same diagnostic accuracy analysis was performed in predefined subgroups based on age, sex, ethnicity, diabetes type, HbA1c at diagnosis, diabetes duration, BMI and image quality. Image quality was categorized as excellent, good, adequate or “dirty lens”; for subgroup analysis, “excellent” corresponded to excellent quality in both eyes, “good” to good quality in at least one eye, and “adequate/dirty lens” to adequate or dirty lens quality in both eyes (Fig. 3). Comparisons of AUCs between subgroups were performed using the DeLong test.

A sensitivity analysis including all patients was undertaken to explore the potential impact of excluding individuals with missing reference standard diagnosis. In the worst-case scenario, all missing/unreadable images were treated as false negatives for referable DR; in the best-case scenario, all missing/unreadable images with referable features were treated as true positives and the remaining as true negatives.

Two multivariate logistic regression models were constructed to assess the association between systemic risk factors and referable DR, using AI-based and human grader-based classifications, respectively. Candidate variables were selected using a bootstrap procedure (n = 500), retaining only those that were significantly associated with referable DR in at least 50% of resamples. Model adequacy followed the rule of at least 10 events per explanatory variable¹⁶. Associations were summarized using odds ratios with 95% confidence intervals and a significance level of 5%.

Parts of the text (grammar and style) were revised with the assistance of a large language model (ChatGPT, OpenAI). All scientific content, data interpretation and conclusions were determined and verified by the authors, who retain full responsibility for the work.

A total of 405 patients with diabetes were invited to participate in the study. Of these, 353 (87.2%) were included in the primary analysis, while 52 (12.8%) were excluded due to missing or ungradable images. Reasons for exclusion included insufficient image quality (n = 8; 15.4%), missing images related to small pupils (n = 14; 26.9%), poor fixation (n = 1; 1.9%), technical issues such as incorrect positioning or duplicate images (n = 8; 15.4%), and images considered ungradable by the human grader (n = 21; 40.4%).

Among the 353 included patients, 192 (54.4%) were male. One hundred sixty-eight (47.6%) had type 2 diabetes, 113 (32.0%) had type 1 diabetes, and 42 (11.9%) had diabetes of unknown etiology. In the excluded group, 29 (55.8%) were male; 34 (65.4%) had type 2 diabetes, 7 (13.5%) had type 1 diabetes, and 9 (17.3%) had diabetes of unknown etiology. The median age of included patients was 56 years (range 19–89), and the median duration of diabetes was 13 years (0–56). Excluded patients were significantly older, with a median age of 68 years (25–88), and had a longer duration of diabetes (median 15 years; 1–45).

A majority of included and excluded patients, 213 (60.4%) and 28 (53.8%) respectively, reported that more than 12 months had elapsed since their last eye examination. Patient characteristics are summarized in Table 1.

Referable DR was identified by the human grader in 54 patients (15.3%) and by the AI system in 59 patients (15.8%). A total of 31 discrepancies between AI and human grader diagnoses were observed: 6 false negatives, 4 false positives, and 21 cases that were ungradable according to human grader because of image quality issues. Among the ungradable cases, 13 were classified as non-referable and 8 as referable by the AI system (Table 2).

A flow chart of the screening procedure and patient follow-up is shown in Fig. 1. Examples of AI-generated grading reports are presented in Fig. 2, and examples of images across different quality categories are shown in Fig. 3.

In the primary analysis, the prevalence of referable DR was 15.3% based on the human grader. The AI system demonstrated excellent diagnostic performance for the detection of referable DR. The area under the curve (AUC) was 96.5% (95% CI: 91.8–99.7), sensitivity was 88.9% (77.4–95.8), and specificity was 98.7% (96.7–99.7). The positive predictive value was 92.3% (81.4–97.9), and the negative predictive value was 98.0% (95.7–99.3). These values indicate strong agreement between the AI system and human grader for classifying patients as referable or non-referable (Table 3). Among the referable cases identified by the human grader, 3 patients had vision-threatening DR, all of whom were correctly classified as referable by the AI system.

To assess the impact of excluding patients with missing or ungradable reference standard grading, a sensitivity analysis was performed on all 405 patients. In a worst-case scenario, all ungradable and non referable cases were assumed to be false negatives, leading to a substantial reduction in estimated sensitivity to 48.5%. In a best-case scenario, all ungradable and referable cases were treated as true positives and all ungradable and non-referable cases as true negatives, resulting in an estimated sensitivity of 90.2%. These analyses suggest that missing data may influence sensitivity estimates but do not fundamentally alter the high overall performance of the AI system.

Subgroup analyses were conducted according to age, sex, ethnicity, diabetes type, HbA1c at diagnosis, duration of diabetes, body mass index (BMI) and image quality (Table 4). The AI system maintained high AUC values across subgroups, with no statistically significant differences between groups. Diagnostic accuracy was particularly high in younger adults (19–52 years), women, European Caucasians, patients with type 1 diabetes and those with excellent image quality, although performance remained robust across all categories.

Multivariate logistic regression models were constructed to evaluate systemic predictors of referable DR for both AI-based and human grader-based classifications. Elevated HbA1c at diabetes diagnosis and longer duration of diabetes emerged as significant predictors of referable DR in both models. These two factors are well-established determinants of DR progression and were confirmed as strong risk markers in our cohort. Other variables, such as BMI and smoking status, were not significantly associated with referable DR, possibly due to the relatively low prevalence of referable DR and limited statistical power for these factors. Interestingly, other ethnicities (sub-Saharan African and Asian) were associated with an increased risk of referable DR in the AI model but not in the human grader model (Table 5).

Overall, 83 patients (23.5%) received a referral recommendation from the human graders for newly identified ocular findings. Among these, 36 (43.3%) were referred for retinal vascular tortuosity, 21 (25.3%) for asymmetric or atrophic optic discs, 14 (16.9%) for suspected age-related macular degeneration, 5 (6.0%) for optic disc hemorrhage or retinal lesions requiring further investigation, 5 (6.0%) for choroidal naevi and 4 (4.8%) for epiretinal membranes. Of all referred cases, 13 (15.7%) had referable DR. These results highlight the added value of fundus photography for detecting non-DR ocular pathologies that also warrant specialist assessment.

The authors declare no competing interests.

The datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request. Access is restricted due to GDPR and institutional data-protection policies.