This review aims to identify gaps and provide direction for future research examining the use of artificial intelligence (AI) and optical coherence tomography (OCT) in the investigation and management of diabetic macular oedema (DMO) . A comprehensive literature search was conducted using MEDLINE, EMBASE, the Cochrane Central Register of Controlled Trials (CENTRAL), the Cochrane Database, and the Web of Science. The search focused on AI applications in DMO diagnosis, grading, and outcome prediction, and adhered to a predefined protocol following the Cochrane Methodology for Scoping Reviews. Following screening 40 studies were included for review. The review highlighted significant advancements in the use of AI for DMO, particularly in diagnosis and biomarker detection. AI models demonstrated high accuracy in distinguishing DMO from other retinal conditions and in segmenting key DMO biomarkers. The review concludes that future research should focus on developing robust prognostic and treatment prediction models, improving external validation and standardising performance metrics. Addressing these challenges is essential for optimising the integration of AI into DMO management, ultimately improving patient outcomes and reducing vision impairment. This review underscores AI's potential to transform DMO management, a leading cause of vision impairment in diabetes. The identified gaps and future research directions offer valuable insights for researchers and practitioners, with the potential to significantly improve patient care and healthcare efficiency.

The aim of this study was to evaluate the blood film assessment of CellaVision DC-1 compared to conventional microscopy in stained peripheral blood (PB) films from paediatric samples. Blood films (n = 50) including clinically normal samples as well as common pathological conditions, were collected and examined by conventional microscopy and CellaVision DC-1. Manual microscopy counts vs. automated WBC differentiation and RBC grading via Cellavision, including manual re-classification, were compared to expert morphologist reporting. Using statistical analysis, the following metrics were measured including sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). The reliability of RBC grading ranged between 60-100% sensitivity and 55-74% specificity for CellaVision method compared to 78-93% sensitivity with manual microscopy, demonstrating the latter as the superior method. Additionally, DC-1 misclassified the presence of blasts for lymphocytes, with 67% compared to 100% specificity with the gold standard microscopy. Both pre- and post-classification, re-classifications, and manual microscopy showed strong correlations of WBC differential counts with expert/known readings, mainly for neutrophils and lymphocytes ( 0.60-0.85). In terms of time, CellaVision took 1 min longer to scan and assess each slide than did light microscopy, which could affect timely diagnosis and treatment decisions. The use of CellaVision DC-1 may be beneficial to diagnostic laboratories in the adult setting; however, further research should focus on enhancing automated analysis when assessing paediatric samples that demand human intellect and critical thinking. Medical Scientist training and software development are recommended. Manual microscopy is faster and more accurate. Slide signing and DC-1 classifications of unclassified WBCs need scientist intervention.