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Primary diabetes care and diabetic retinopathy (DR) screening persist as major public health challenges due to a shortage of trained primary care physicians (PCPs), particularly in low-resource settings. Here, to bridge the gaps, we developed an integrated image-language system (DeepDR-LLM), combining a large language model (LLM module) and image-based deep learning (DeepDR-Transformer), to provide individualized diabetes management recommendations to PCPs. In a retrospective evaluation, the LLM module demonstrated comparable performance to PCPs and endocrinology residents when tested in English and outperformed PCPs and had comparable performance to endocrinology residents in Chinese. For identifying referable DR, the average PCP's accuracy was 81.0% unassisted and 92.3% assisted by DeepDR-Transformer. Furthermore, we performed a single-center real-world prospective study, deploying DeepDR-LLM. We compared diabetes management adherence of patients under the unassisted PCP arm (n = 397) with those under the PCP+DeepDR-LLM arm (n = 372). Patients with newly diagnosed diabetes in the PCP+DeepDR-LLM arm showed better self-management behaviors throughout follow-up (P < 0.05). For patients with referral DR, those in the PCP+DeepDR-LLM arm were more likely to adhere to DR referrals (P < 0.01). Additionally, DeepDR-LLM deployment improved the quality and empathy level of management recommendations. Given its multifaceted performance, DeepDR-LLM holds promise as a digital solution for enhancing primary diabetes care and DR screening., (© 2024. The Author(s).)

Background: Diabetic macular edema (DME) is the leading cause of vision loss in people with diabetes. Application of artificial intelligence (AI) in interpreting fundus photography (FP) and optical coherence tomography (OCT) images allows prompt detection and intervention., Purpose: To evaluate the performance of AI in detecting DME from FP or OCT images and identify potential factors affecting model performances., Data Sources: We searched seven electronic libraries up to 12 February 2023., Study Selection: We included studies using AI to detect DME from FP or OCT images., Data Extraction: We extracted study characteristics and performance parameters., Data Synthesis: Fifty-three studies were included in the meta-analysis. FP-based algorithms of 25 studies yielded pooled area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity of 0.964, 92.6%, and 91.1%, respectively. OCT-based algorithms of 28 studies yielded pooled AUROC, sensitivity, and specificity of 0.985, 95.9%, and 97.9%, respectively. Potential factors improving model performance included deep learning techniques, larger size, and more diversity in training data sets. Models demonstrated better performance when validated internally than externally, and those trained with multiple data sets showed better results upon external validation., Limitations: Analyses were limited by unstandardized algorithm outcomes and insufficient data in patient demographics, OCT volumetric scans, and external validation., Conclusions: This meta-analysis demonstrates satisfactory performance of AI in detecting DME from FP or OCT images. External validation is warranted for future studies to evaluate model generalizability. Further investigations may estimate optimal sample size, effect of class balance, patient demographics, and additional benefits of OCT volumetric scans., (© 2024 by the American Diabetes Association.)

Importance: The presence of diabetic macular ischemia (DMI) on optical coherence tomography angiography (OCTA) images predicts diabetic retinal disease progression and visual acuity (VA) deterioration, suggesting an OCTA-based DMI evaluation can further enhance diabetic retinopathy (DR) management., Objective: To investigate whether an automated binary DMI algorithm using OCTA images provides prognostic value on DR progression, diabetic macular edema (DME) development, and VA deterioration in a cohort of patients with diabetes., Design, Setting, and Participants: In this cohort study, DMI assessment of superficial capillary plexus and deep capillary plexus OCTA images was performed by a previously developed deep learning algorithm. The presence of DMI was defined as images exhibiting disruption of fovea avascular zone with or without additional areas of capillary loss, while absence of DMI was defined as images presented with intact fovea avascular zone outline and normal distribution of vasculature. Patients with diabetes were recruited starting in July 2015 and were followed up for at least 4 years. Cox proportional hazards models were used to evaluate the association of the presence of DMI with DR progression, DME development, and VA deterioration. Analysis took place between June and December 2022., Main Outcomes and Measures: DR progression, DME development, and VA deterioration., Results: A total of 321 eyes from 178 patients were included for analysis (85 [47.75%] female; mean [SD] age, 63.39 [11.04] years). Over a median (IQR) follow-up of 50.41 (48.16-56.48) months, 105 eyes (32.71%) had DR progression, 33 eyes (10.28%) developed DME, and 68 eyes (21.18%) had VA deterioration. Presence of superficial capillary plexus-DMI (hazard ratio [HR], 2.69; 95% CI, 1.64-4.43; P < .001) and deep capillary plexus-DMI (HR, 3.21; 95% CI, 1.94-5.30; P < .001) at baseline were significantly associated with DR progression, whereas presence of deep capillary plexus-DMI was also associated with DME development (HR, 4.60; 95% CI, 1.15-8.20; P = .003) and VA deterioration (HR, 2.12; 95% CI, 1.01-5.22; P = .04) after adjusting for age, duration of diabetes, fasting glucose, glycated hemoglobin, mean arterial blood pressure, DR severity, ganglion cell-inner plexiform layer thickness, axial length, and smoking at baseline., Conclusions and Relevance: In this study, the presence of DMI on OCTA images demonstrates prognostic value for DR progression, DME development, and VA deterioration.

Optical coherence tomography angiography (OCT-A) provides depth-resolved visualization of the retinal microvasculature without intravenous dye injection. It facilitates investigations of various retinal vascular diseases and glaucoma by assessment of qualitative and quantitative microvascular changes in the different retinal layers and radial peripapillary layer non-invasively, individually, and efficiently. Deep learning (DL), a subset of artificial intelligence (AI) based on deep neural networks, has been applied in OCT-A image analysis in recent years and achieved good performance for different tasks, such as image quality control, segmentation, and classification. DL technologies have further facilitated the potential implementation of OCT-A in eye clinics in an automated and efficient manner and enhanced its clinical values for detecting and evaluating various vascular retinopathies. Nevertheless, the deployment of this combination in real-world clinics is still in the "proof-of-concept" stage due to several limitations, such as small training sample size, lack of standardized data preprocessing, insufficient testing in external datasets, and absence of standardized results interpretation. In this review, we introduce the existing applications of DL in OCT-A, summarize the potential challenges of the clinical deployment, and discuss future research directions. [ABSTRACT FROM AUTHOR]