Developing Artificial Intelligence Approaches to Predicting Progressive Myopia and Risk of Myopic Complications Based on Optometry Data

开发人工智能方法根据验光数据预测进行性近视和近视并发症的风险

• 批准号: 2605159
• 金额: --
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2605159
• 关键词: Developing; Artificial; Intelligence; Approaches; Predicting

Worldwide, uncorrected refractive error is the leading cause of visual impairment, affecting 116.3 million people. Myopia (short-sightedness) is the most common disorder, and its prevalence is increasing as we witness a 'myopia epidemic'. In 2010, 1.9 billion people, 27% of the world's population, were myopic, with 70 million (2.8%) highly myopic. It is estimated that these percentages will increase to 52% and 10% respectively by 2050 (Fricke et al., 2018). Visual impairment from myopia has a significant economic impact and adverse effect on quality of life, with pathologic myopia particularly harmful as it leads to degenerative changes at the back of the eye causing blindness.Myopia is a risk factor for cataract, glaucoma, retinal detachment and myopic macular degeneration. It is estimated that up to 11% of people with pathological myopia develop choroidal neovascularization and axial elongation can cause distortion of the peripapillary region leading to glaucoma and loss of visual field (Wong et al., 2014). Randomized control trials have shown interventions (e.g. bifocal contact lenses, low-dose atropine) can slow progression of childhood myopia (Chamberlain et al., 2019) and earlier interventions are likely to be more effective. However, at present there is no satisfactory way to identify individuals at risk of progressive myopia or to identify those most likely to benefit from treatment intervention. The ability to better determine risk would enable treatments to be targeted for those most likely to benefit, increasing success rates, and giving optometrists confidence to increase their use of innovative treatments. If it is possible to better identify individuals at risk of myopic changes, there is greater opportunity for successful preventative treatment.The project will utilise the Scottish Clinical Optometry and Ophthalmology Network e-research (SCONe) collaboration, a research repository developed by academic partners at Glasgow Caledonian University and University of Edinburgh. In Scotland, over 1 million retinal images are captured by optometrists each year, providing a rich population-based resource for research. SCONe is utilizing images from optometry practices to build a curated dataset, incorporating retinal photographs and optical coherence tomography (OCT) images linked to clinical information. This is a growing, longitudinal resource, ideal for developing Artificial Intelligence (AI) tools to improve clinical decision making.The main aim of this project is to use SCONe to develop AI algorithms that can be used in optometric practices to identify individuals at high risk of myopic progression based on information from retinal photographs, OCT, refraction, and other clinical features. Within a busy practice, optometrists may be missing opportunities to treat those likely to benefit from preventative treatments, and an AI tool could facilitate identification of suitable individuals. The objective of developing such a tool would be to categorize eyes as high, medium, or low risk, or attribute an individualized score indicating the likelihood of progressive changes, i.e., a Myopia Progression Index (MPI). The longitudinal nature of the SCONe dataset, and its focus on data acquired from primary care optometry (i.e. high street opticians' practices) from across Scotland, provides a unique opportunity for this project. A secondary aim is to develop and validate an additional AI algorithm to determine axial length and refraction from features of retinal photographs alone and to test the algorithm in its ability to differentiate patients with myopia, high myopia and pathological myopia from healthy individuals. This second algorithm, again utilising the SCONe dataset, will aim to quantify retinal features already known to be associated with myopia; for example, signs of peripapillary atrophy; and facilitate identification of new retinal biomarkers of myopia, for example, changes in patterns of retin

在世界范围内，未矫正的屈光不正是视力障碍的主要原因，影响到1.163亿人。近视（近视）是最常见的疾病，其患病率正在增加，因为我们目睹了“近视眼”。2010年，全球有19亿人（占世界人口的27%）患有近视，其中7000万人（2.8%）高度近视。据估计，到2050年，这些百分比将分别增加到52%和10%（Replike等人，2018年）。近视引致的视力受损对经济及生活质素有重大影响，病理性近视尤其有害，因为它会导致眼后部的退化性改变，引致失明。近视是白内障、青光眼、视网膜脱离及近视性黄斑变性的危险因素。据估计，高达11%的患有病理性近视的人发展脉络膜新血管形成，并且轴向伸长可引起视乳头周围区域的变形，从而导致青光眼和视野丧失（Wong等人，2014年）。随机对照试验已经显示干预（例如双焦点隐形眼镜、低剂量阿托品）可以减缓儿童近视的进展（Chamberlain等人，2019年）和早期干预可能会更有效。然而，目前还没有令人满意的方法来识别处于进行性近视风险中的个体或识别最有可能从治疗干预中受益的个体。更好地确定风险的能力将使治疗能够针对那些最有可能受益的人，提高成功率，并使验光师有信心增加他们对创新治疗的使用。如果能够更好地识别近视变化风险的个体，成功预防治疗的机会就更大。该项目将利用苏格兰临床验光和眼科网络电子研究（SCONE）合作，这是由格拉斯哥喀里多尼亚大学和爱丁堡大学的学术合作伙伴开发的研究库。在苏格兰，验光师每年拍摄超过100万张视网膜图像，为研究提供了丰富的人口资源。SCONe正在利用验光实践中的图像来构建一个精心策划的数据集，将视网膜照片和光学相干断层扫描（OCT）图像与临床信息相关联。这是一个不断增长的纵向资源，是开发人工智能（AI）工具以改善临床决策的理想选择。该项目的主要目的是使用SCONe开发可用于验光实践的AI算法，以根据视网膜照片，OCT，屈光和其他临床特征的信息识别近视进展的高风险个体。在忙碌的实践中，验光师可能会错过治疗那些可能从预防性治疗中受益的人的机会，而人工智能工具可以帮助识别合适的个人。开发这种工具的目的是将眼睛分类为高、中或低风险，或者赋予指示进行性变化的可能性的个体化评分，即，a近视进展指数（MPI）。SCONe数据集的纵向性质及其对苏格兰各地初级保健验光（即高街配镜师的做法）获得的数据的关注，为该项目提供了独特的机会。第二个目的是开发和验证一种额外的AI算法，以单独根据视网膜照片的特征确定眼轴长度和屈光度，并测试该算法区分近视、高度近视和病理性近视患者与健康个体的能力。第二种算法再次利用SCONe数据集，旨在量化已知与近视相关的视网膜特征;例如，视乳头周围萎缩的迹象;并促进近视的新视网膜生物标志物的鉴定，例如，视网膜结构模式的变化。