Mohammad Reza Abdol Homayuni

To conduct a systematic evaluation of reconstructive techniques for the periocular area and eyelids, highlighting their functional and cosmetic outcomes, complications, and future advancements. A systematic review was conducted following PRISMA guidelines. Searches were performed in PubMed, Scopus, Embase, and Web of Science using terminology related to oculofacial flaps. Studies were included if they utilized flap techniques for periocular repair, represented original peer-reviewed research, and met defined eligibility criteria. Data concerning patient demographics, types of defects, reconstructive techniques, results, and complications were extracted and analyzed. From an initial 1975 publications, 55 studies met the inclusion requirements. Techniques such as Mustardé cheek rotation, modified rhomboid, and paramedian forehead flaps were primarily utilized, with basal cell carcinoma as the principal cause of abnormalities. Complications were primarily mild, including tip necrosis (13.5%), wound dehiscence, and ectropion. Innovative methods like propeller flaps and the LOOP flap demonstrated improved cosmetic and functional outcomes for substantial defects. Challenges remain in achieving oncological safety and patient-centered aesthetics in complex reconstructions. The diversity of therapies underscores the imperative of tailoring reconstructive techniques to the particular attributes of the defect and the patient’s goals. Advancements in flap design have expanded surgical options; yet, issues like result unpredictability and the necessity for long-term monitoring persist. Continuous research is vital to enhance techniques and develop innovative solutions, hence improving the quality of care for patients undergoing periocular reconstruction.

Background/Objectives: Diabetic retinopathy (DR) remains a leading cause of preventable blindness, with its global prevalence projected to rise sharply as diabetes incidence increases. Early detection and timely management are critical to reducing DR-related vision loss. Optical Coherence Tomography Angiography (OCTA) now enables non-invasive, layer-specific visualization of the retinal vasculature, facilitating more precise identification of early microvascular changes. Concurrently, advancements in artificial intelligence (AI), particularly deep learning (DL) architectures such as convolutional neural networks (CNNs), attention-based models, and Vision Transformers (ViTs), have revolutionized image analysis. These AI-driven tools substantially enhance the sensitivity, specificity, and interpretability of DR screening. Methods: A systematic review of PubMed, Scopus, WOS, and Embase databases, including quality assessment of published studies, investigating the result of different AI algorithms with OCTA parameters in DR patients was conducted. The variables of interest comprised training databases, type of image, imaging modality, number of images, outcomes, algorithm/model used, and performance metrics. Results: A total of 32 studies were included in this systematic review. In comparison to conventional ML techniques, our results indicated that DL algorithms significantly improve the accuracy, sensitivity, and specificity of DR screening. Multi-branch CNNs, ensemble architectures, and ViTs were among the sophisticated models with remarkable performance metrics. Several studies reported that accuracy and area under the curve (AUC) values were higher than 99%. Conclusions: This systematic review underscores the transformative potential of integrating advanced DL and machine learning (ML) algorithms with OCTA imaging for DR screening. By synthesizing evidence from 32 studies, we highlight the unique capabilities of AI-OCTA systems in improving diagnostic accuracy, enabling early detection, and streamlining clinical workflows. These advancements promise to enhance patient management by facilitating timely interventions and reducing the burden of DR-related vision loss. Furthermore, this review provides critical recommendations for clinical practice, emphasizing the need for robust validation, ethical considerations, and equitable implementation to ensure the widespread adoption of AI-OCTA technologies. Future research should focus on multicenter studies, multimodal integration, and real-world validation to maximize the clinical impact of these innovative tools.