PDF(Pubmed)
Abstract：
BACKGROUND: Diabetic retinopathy (DR) and diabetic macular edema (DME) are major causes of visual impairment that challenge global vision health. New strategies are needed to tackle these growing global health problems, and the integration of artificial intelligence (AI) into ophthalmology has the potential to revolutionize DR and DME management to meet these challenges.
METHODS: This review discusses the latest AI-driven methodologies in the context of DR and DME in terms of disease identification, patient-specific disease profiling, and short-term and long-term management. This includes current screening and diagnostic systems and their real-world implementation, lesion detection and analysis, disease progression prediction, and treatment response models. It also highlights the technical advancements that have been made in these areas. Despite these advancements, there are obstacles to the widespread adoption of these technologies in clinical settings, including regulatory and privacy concerns, the need for extensive validation, and integration with existing healthcare systems. We also explore the disparity between the potential of AI models and their actual effectiveness in real-world applications.
CONCLUSIONS: AI has the potential to revolutionize the management of DR and DME, offering more efficient and precise tools for healthcare professionals. However, overcoming challenges in deployment, regulatory compliance, and patient privacy is essential for these technologies to realize their full potential. Future research should aim to bridge the gap between technological innovation and clinical application, ensuring AI tools integrate seamlessly into healthcare workflows to enhance patient outcomes.
METHODS: This review discusses the latest AI-driven methodologies in the context of DR and DME in terms of disease identification, patient-specific disease profiling, and short-term and long-term management. This includes current screening and diagnostic systems and their real-world implementation, lesion detection and analysis, disease progression prediction, and treatment response models. It also highlights the technical advancements that have been made in these areas. Despite these advancements, there are obstacles to the widespread adoption of these technologies in clinical settings, including regulatory and privacy concerns, the need for extensive validation, and integration with existing healthcare systems. We also explore the disparity between the potential of AI models and their actual effectiveness in real-world applications.
CONCLUSIONS: AI has the potential to revolutionize the management of DR and DME, offering more efficient and precise tools for healthcare professionals. However, overcoming challenges in deployment, regulatory compliance, and patient privacy is essential for these technologies to realize their full potential. Future research should aim to bridge the gap between technological innovation and clinical application, ensuring AI tools integrate seamlessly into healthcare workflows to enhance patient outcomes.
摘要：
背景：糖尿病性视网膜病变（DR）和糖尿病性黄斑水肿（DME）是视力障碍的主要原因，对全球视力健康构成挑战。需要新的战略来解决这些日益严重的全球健康问题，将人工智能（AI）集成到眼科中有可能彻底改变DR和DME管理以应对这些挑战。
方法：这篇综述讨论了DR和DME在疾病识别方面的最新AI驱动方法，患者特异性疾病分析，以及短期和长期管理。这包括当前的筛查和诊断系统及其实际实施，病变检测和分析，疾病进展预测，和治疗反应模型。它还强调了在这些领域取得的技术进步。尽管取得了这些进步,在临床环境中广泛采用这些技术存在障碍，包括监管和隐私问题，需要广泛的验证，以及与现有医疗保健系统的整合。我们还探讨了AI模型的潜力与它们在现实世界应用中的实际效果之间的差距。
结论：AI有可能彻底改变DR和DME的管理，为医疗保健专业人员提供更高效和精确的工具。然而,克服部署中的挑战，法规遵从性，患者隐私对于这些技术实现其全部潜力至关重要。未来的研究应旨在弥合技术创新与临床应用之间的差距，确保AI工具无缝集成到医疗保健工作流程中，以提高患者的治疗效果。
方法：这篇综述讨论了DR和DME在疾病识别方面的最新AI驱动方法，患者特异性疾病分析，以及短期和长期管理。这包括当前的筛查和诊断系统及其实际实施，病变检测和分析，疾病进展预测，和治疗反应模型。它还强调了在这些领域取得的技术进步。尽管取得了这些进步,在临床环境中广泛采用这些技术存在障碍，包括监管和隐私问题，需要广泛的验证，以及与现有医疗保健系统的整合。我们还探讨了AI模型的潜力与它们在现实世界应用中的实际效果之间的差距。
结论：AI有可能彻底改变DR和DME的管理，为医疗保健专业人员提供更高效和精确的工具。然而,克服部署中的挑战，法规遵从性，患者隐私对于这些技术实现其全部潜力至关重要。未来的研究应旨在弥合技术创新与临床应用之间的差距，确保AI工具无缝集成到医疗保健工作流程中，以提高患者的治疗效果。
