人工智能模型在糖尿病性黄斑水肿治疗结果自动OCT分析和预测中的应用综述

Q1 Medicine

Informatics in Medicine Unlocked Pub Date : 2025-01-01 DOI:10.1016/j.imu.2025.101676

Mohaimen Al-Zubaidy , Agnieszka Stankiewicz , Matthew Anderson , Jordan Reed , Veronica Corona , Rebecca Pope , Boguslaw Obara , Maged S. Habib , David H. Steel

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引用次数: 0

摘要

目的探讨人工智能（AI）和光学相干断层扫描（OCT）在糖尿病性黄斑水肿（DMO）调查和治疗中的应用，为今后的研究提供方向。方法采用MEDLINE、EMBASE、Cochrane Central Register of Controlled Trials （Central）、Cochrane Database和Web of Science进行综合文献检索。检索重点关注人工智能在DMO诊断、分级和结果预测中的应用，并遵循Cochrane范围评价方法学的预定义协议。结果筛选后纳入40项研究。该综述强调了人工智能用于DMO的重大进展，特别是在诊断和生物标志物检测方面。人工智能模型在区分DMO和其他视网膜疾病以及分割关键DMO生物标志物方面表现出很高的准确性。结论本综述认为，未来的研究应侧重于建立可靠的预后和治疗预测模型，改进外部验证和标准化绩效指标。解决这些挑战对于优化人工智能与DMO管理的整合，最终改善患者的治疗效果和减少视力损害至关重要。这篇综述强调了人工智能在改变糖尿病视力损害的主要原因DMO管理方面的潜力。确定的差距和未来的研究方向为研究人员和从业人员提供了有价值的见解，有可能显著改善患者护理和医疗保健效率。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

A scoping review of the use of artificial intelligence models in automated OCT analysis and prediction of treatment outcomes in diabetic macular oedema

Objective

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).

Methods

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.

Results

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.

Conclusion

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.

Significance

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.

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来源期刊

Informatics in Medicine Unlocked Medicine-Health Informatics

CiteScore

9.50

自引率

0.00%

发文量

282

审稿时长

39 days

期刊介绍： Informatics in Medicine Unlocked (IMU) is an international gold open access journal covering a broad spectrum of topics within medical informatics, including (but not limited to) papers focusing on imaging, pathology, teledermatology, public health, ophthalmological, nursing and translational medicine informatics. The full papers that are published in the journal are accessible to all who visit the website.