The Wilmer Institute Mentored Clinician Scientist Scholar Program-Supplement

项目摘要/摘要 人工智能(AI)，以机器学习(ML)和深度学习(DL)的形式， 使医学领域发生了革命性的变化，特别是在子专科领域，可以获得 大量的图像，如眼科。虽然前景看好，但它的应用 眼科的ML/AI受到缺乏关于如何提取的明确指南的限制 眼科数据，将这些数据转换为可用于ML/AI模型训练的形式 并确保这些数据遵守公平(可查找性、可访问性、 互操作性和重用)。我们的建议旨在通过以下方式解决这些限制 通过在线系列讲座描述数据管理的最佳实践，该系列讲座将是 一旦课程经过评估和验证，即可免费向公众开放 另一家学术机构(哈佛/马萨诸塞州眼耳医院)。 如果成功，这项提议将产生影响，因为它将： 1.加速ML/AI中的可重复研究，方法是创建 标准化的ML/AI就绪和公平的数据集。 2.将数据管理的最佳实践传播到约翰·霍普金斯大学以外 (JHU)，因为在线讲座将向公众免费提供。 3.促进创建标准化的多机构数据集，以提高 眼科ML/AI算法的性能和泛化能力 支持联合学习方法和模型的外部验证。

PROJECT SUMMARY/ ABSTRACT Artificial intelligence (AI), in the form of machine learning (ML) and deep learning (DL), has revolutionized the field of medicine, especially in subspecialties with access to a large number of images, such as ophthalmology. While promising, the application of ML/AI in ophthalmology is limited by the lack of clear guidelines on how to extract ophthalmic data, convert these data into a form that is usable for ML/AI model training and ensure these data are adhering to the principles of FAIR (Findability, Accessibility, Interoperability, and Reuse). Our proposal aims to address these limitations by delineating the best practices for data curation via an online lecture series that will be available free of charge to the public, once the curriculum is assessed and validated by another academic institution (Harvard/Massachusetts Eye and Ear Infirmary). If successful, the proposal will be impactful, as it will: 1. Accelerate reproducible research in ML/AI by enabling the creation of standardized ML/AI-ready and FAIR datasets. 2. Disseminate the best practices in data curation beyond Johns Hopkins University (JHU), as the online lectures will be made free of charge to the public. 3. Foster the creation of standardized multi-institutional datasets that will improve both performance and generalizability of ML/AI algorithms in ophthalmology, by enabling federated learning approaches and external validation of models.