Singapore Eye Research Institute, Singapore, Singapore National Eye Centre, Singapore; Duke-National University of Singapore Medical School, Singapore.
Institute of High Performance Computing, Agency for Science, Technology and Research (A*STAR), Singapore.
Lancet Digit Health. 2021 May;3(5):e317-e329. doi: 10.1016/S2589-7500(21)00055-8.
By 2050, almost 5 billion people globally are projected to have myopia, of whom 20% are likely to have high myopia with clinically significant risk of sight-threatening complications such as myopic macular degeneration. These are diagnoses that typically require specialist assessment or measurement with multiple unconnected pieces of equipment. Artificial intelligence (AI) approaches might be effective for risk stratification and to identify individuals at highest risk of visual loss. However, unresolved challenges for AI medical studies remain, including paucity of transparency, auditability, and traceability.
In this retrospective multicohort study, we developed and tested retinal photograph-based deep learning algorithms for detection of myopic macular degeneration and high myopia, using a total of 226 686 retinal images. First we trained and internally validated the algorithms on datasets from Singapore, and then externally tested them on datasets from China, Taiwan, India, Russia, and the UK. We also compared the performance of the deep learning algorithms against six human experts in the grading of a randomly selected dataset of 400 images from the external datasets. As proof of concept, we used a blockchain-based AI platform to demonstrate the real-world application of secure data transfer, model transfer, and model testing across three sites in Singapore and China.
The deep learning algorithms showed robust diagnostic performance with areas under the receiver operating characteristic curves [AUC] of 0·969 (95% CI 0·959-0·977) or higher for myopic macular degeneration and 0·913 (0·906-0·920) or higher for high myopia across the external testing datasets with available data. In the randomly selected dataset, the deep learning algorithms outperformed all six expert graders in detection of each condition (AUC of 0·978 [0·957-0·994] for myopic macular degeneration and 0·973 [0·941-0·995] for high myopia). We also successfully used blockchain technology for data transfer, model transfer, and model testing between sites and across two countries.
Deep learning algorithms can be effective tools for risk stratification and screening of myopic macular degeneration and high myopia among the large global population with myopia. The blockchain platform developed here could potentially serve as a trusted platform for performance testing of future AI models in medicine.
None.
到 2050 年，预计全球将有近 50 亿人近视，其中 20%可能患有高度近视，并有发生威胁视力的并发症（如近视性黄斑病变）的临床显著风险。这些诊断通常需要专科评估或使用多件互不相连的设备进行测量。人工智能（AI）方法可能有助于进行风险分层，并识别出视力丧失风险最高的个体。然而，AI 医学研究仍存在未解决的挑战，包括缺乏透明度、可审核性和可追溯性。
在这项回顾性多队列研究中，我们使用总共 226686 张视网膜图像，开发并测试了基于视网膜照片的深度学习算法，以检测近视性黄斑病变和高度近视。首先，我们在来自新加坡的数据集上训练和内部验证算法，然后在来自中国、中国台湾、印度、俄罗斯和英国的数据集上进行外部测试。我们还比较了深度学习算法与六位人类专家在对来自外部数据集的 400 张随机选择图像数据集的分级中的表现。作为概念验证，我们使用基于区块链的 AI 平台来展示安全数据传输、模型传输和模型测试在新加坡和中国的三个地点的实际应用。
深度学习算法在外部测试数据集上具有稳健的诊断性能，其用于检测近视性黄斑病变的受试者工作特征曲线下面积（AUC）为 0.969（95%CI 0.959-0.977）或更高，用于检测高度近视的 AUC 为 0.913（0.906-0.920）或更高。在随机选择的数据集上，深度学习算法在检测每种疾病方面均优于所有六位专家分级器（用于检测近视性黄斑病变的 AUC 为 0.978（0.957-0.994），用于检测高度近视的 AUC 为 0.973（0.941-0.995））。我们还成功地使用区块链技术在站点之间以及两个国家之间进行数据传输、模型传输和模型测试。
深度学习算法可以成为一种有效的工具，用于对全球近视人群进行近视性黄斑病变和高度近视的风险分层和筛查。此处开发的区块链平台有可能成为医学中未来 AI 模型性能测试的可信平台。
无。
