Artificial intelligence (AI) has emerged as a transformative force in healthcare, particularly in the field of ophthalmology. This comprehensive review examines the current applications of AI in ophthalmology, highlighting its significant contributions to diagnostic accuracy, treatment efficacy, and patient care. AI technologies, such as deep learning algorithms, have demonstrated exceptional performance in the early detection and diagnosis of various eye conditions, including diabetic retinopathy (DR), age-related macular degeneration (AMD), and glaucoma. Additionally, AI has enhanced the analysis of ophthalmic imaging techniques like optical coherence tomography (OCT) and fundus photography, facilitating more precise disease monitoring and management. The review also explores AI's role in surgical assistance, predictive analytics, and personalized treatment plans, showcasing its potential to revolutionize clinical practice and improve patient outcomes. Despite these advancements, challenges such as data privacy, regulatory hurdles, and ethical considerations remain. The review underscores the need for continued research and collaboration among clinicians, researchers, technology developers, and policymakers to address these challenges and fully harness the potential of AI in improving eye health worldwide. By integrating AI with teleophthalmology and developing AI-driven wearable devices, the future of ophthalmic care promises enhanced accessibility, efficiency, and efficacy, ultimately reducing the global burden of visual impairment and blindness.
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http://dx.doi.org/10.7759/cureus.61826 | DOI Listing |
J Imaging Inform Med
January 2025
School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA.
Vision transformer (ViT)and convolutional neural networks (CNNs) each possess distinct strengths in medical imaging: ViT excels in capturing long-range dependencies through self-attention, while CNNs are adept at extracting local features via spatial convolution filters. While ViT may struggle with capturing detailed local spatial information, critical for tasks like anomaly detection in medical imaging, shallow CNNs often fail to effectively abstract global context. This study aims to explore and evaluate hybrid architectures that integrate ViT and CNN to leverage their complementary strengths for enhanced performance in medical vision tasks, such as segmentation, classification, reconstruction, and prediction.
View Article and Find Full Text PDFSports Med Open
January 2025
Institute of Primary Care, University of Zurich, Zurich, Switzerland.
Background: Marathon training and running have many beneficial effects on human health and physical fitness; however, they also pose risks. To date, no comprehensive review regarding both the benefits and risks of marathon running on different organ systems has been published.
Main Body: The aim of this review was to provide a comprehensive review of the benefits and risks of marathon training and racing on different organ systems.
J Youth Adolesc
January 2025
Department of Social Work, the Chinese University of Hong Kong, Hong Kong, China.
Considering the potential detrimental impact of poverty on psychological development and the resulting harmful cycles, implementing poverty alleviation interventions is necessary for children and adolescents. Although several meta-analyses have demonstrated the effectiveness of monetary poverty reduction programs, there remains a significant gap in understanding how multidimensional poverty reduction strategies boost psychological development. This meta-analysis aims to address this gap by disclosing the impact of multifaceted anti-poverty interventions on the psychological development of children and adolescents.
View Article and Find Full Text PDFArch Gynecol Obstet
January 2025
Department of Radiology, National Defense Medical College, 3-2 Namiki, Tokorozawa, Saitama, 359-8513, Japan.
Purpose: To comprehensively compare the diagnostic ability and inter-reader agreement of magnetic resonance imaging (MRI) findings for predicting massive hemorrhage after cesarean section in patients with placental malposition, aiming to identify the most reliable and objective indicators.
Methods: Totally, 148 consecutive patients with placental malposition underwent MRI and cesarean section at our hospital between January 2014 and July 2021. The patients were divided into massive and non-massive hemorrhage groups.
Arch Gynecol Obstet
January 2025
Faculty of Medicine and Health Sciences, Tel Aviv University, Tel Aviv, Israel.
Purpose: To quantify the separation between maternal blood cell-free (cf)DNA markers in preeclampsia and unaffected pregnancies and compare with existing markers. This approach has not been used in previous studies.
Methods: Comprehensive systematic literature search of PubMed to identify studies measuring total cfDNA, fetal cf(f)DNA or the fetal fraction (FF) in pregnant women.
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