DESIGN: A questionnaire containing 47 questions was developed which encompassed clinical scenarios such as treatment response to anti-vascular endothelial growth factor and steroid, treatment side effects, as well as cost and compliance/reimbursement in the management of DME using a Dephi questionnaire as guide.
METHODS: An expert panel of 12 retinal specialists from Singapore, Malaysia, Philippines, India and Vietnam responded to this questionnaire on two separate occasions. The first round responses were compiled, analyzed and discussed in a round table discussion where a consensus was sought through voting. Consensus was considered achieved, when 9 of the 12 panellists (75%) agreed on a recommendation.
RESULTS: The DME patients were initially profiled based on their response to treatment, and the terms target response, adequate response, nonresponse, and inadequate response were defined. The panellists arrived at a consensus on various aspects of DME treatment such as need for classification of patients before treatment, first-line treatment options, appropriate time to switch between treatment modalities, and steroid-related side effects based on which recommendations were derived, and a treatment algorithm was developed.
CONCLUSIONS: This consensus article provides comprehensive, evidence-based treatment guidelines in the management of DME in Asian population. In addition, it also provides recommendations on other aspects of DME management such as steroid treatment for stable glaucoma patients, management of intraocular pressure rise, and recommendations for cataract development.
METHODS: We conducted a qualitative systematic review on published literature using AI on retina images to detect systemic diabetes complications. We searched three main databases: PubMed, Google Scholar, and Web of Science (January 1, 2000, to October 1, 2024). Research that used AI to evaluate the associations between retinal images and diabetes-associated complications, or research involving diabetes patients with retinal imaging and AI systems were included. Our primary focus was on articles related to AI, retinal images, and diabetes-associated complications. We evaluated each study for the robustness of the studies by development of the AI algorithm, size and quality of the training dataset, internal validation and external testing, and the performance. Quality assessments were employed to ensure the inclusion of high-quality studies, and data extraction was conducted systematically to gather pertinent information for analysis. This study has been registered on PROSPERO under the registration ID CRD42023493512.
FINDINGS: From a total of 337 abstracts, 38 studies were included. These studies covered a range of topics related to prediction of diabetes from pre-diabetes or non-diabeticindividuals (n = 4), diabetes related systemic risk factors (n = 10), detection of microvascular complications (n = 8) and detection of macrovascular complications (n = 17). Most studies (n = 32) utilized color fundus photographs (CFP) as retinal image modality, while others employed optical coherence tomography (OCT) (n = 6). The performance of the AI systems varied, with an AUC ranging from 0.676 to 0.971 in prediction or identification of different complications. Study designs included cross-sectional and cohort studies with sample sizes ranging from 100 to over 100,000 participants. Risk of bias was evaluated by using the Newcastle-Ottawa Scale and AXIS, with most studies scoring as low to moderate risk.
INTERPRETATION: Our review highlights the potential for the use of AI algorithms applied to retina images, particularly CFP, to screen, predict, or diagnose the various microvascular and macrovascular complications of diabetes. However, we identified few studies with longitudinal data and a paucity of randomized control trials, reflecting a gap between the development of AI algorithms and real-world implementation and translational studies.
FUNDING: Dr. Gavin Siew Wei TAN is supported by: 1. DYNAMO: Diabetes studY on Nephropathy And other Microvascular cOmplications II supported by National Medical Research Council (MOH-001327-03): data collection, analysis, trial design 2. Prognositc significance of novel multimodal imaging markers for diabetic retinopathy: towards improving the staging for diabetic retinopathy supported by NMRC Clinician Scientist Award (CSA)-Investigator (INV) (MOH-001047-00).