AI training module for Vision Science

视觉科学人工智能训练模块

• 批准号: 10405897
• 负责人: Kate E Keller
• 金额: $ 8.64万
• 依托单位: OREGON HEALTH & SCIENCE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-07-01 至 2022-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10405897
• 关键词: Training Activity; vision science

Project Summary The development of machine learning (ML) models for health care applications has become a highly active and rapidly evolving area of research, particularly in ophthalmology, which relies heavily upon pattern recognition. ML models trained to interpret medical data have demonstrated dramatically improved performance in the past decade, driven largely by the advent of deep learning. Multiple models have now received FDA approval and are being implemented in the clinical setting, making artificial intelligence (AI) a priority for the American Academy of Ophthalmology and the field in general. Compared to traditional ML learning algorithms, deep learning leverages massively large training datasets to generate prediction models capable of achieving unprecedented performance in pattern recognition within structured or unstructured data. Assembling correctly labeled datasets, which are representative of the target patient population and are large enough to train a deep learning model, is challenging and remains the primary barrier to continued advancement in this field. Because these data are scarce, it is crucial to maximize their utility by making them broadly available in a useable format. Researchers spend significant time and effort curating the databases used to successfully train their ML models, but rarely are these datasets subsequently shared in a manner that is FAIR (findable, accessible, interoperable, and reusable). Emphasis on structuring these data in such a manner while protecting subjects' private health information would enhance interdisciplinary collaboration and promote advancement of the field. In this supplement, we propose a web-based, publicly available data science module designed to provide vision science researchers from a variety of backgrounds with the conceptual and practical knowledge necessary to produce FAIR, ML-ready data. The AI module will accomplish this goal through a combination of recorded video lectures, reading materials, knowledge assessments, and hands-on assignments with immediate feedback. The module will be developed and integrated into an existing predoctoral curriculum and hosted by Oregon Health & Science University, but will be freely available online to a global audience. Instructors will include an interdisciplinary team with experience operating at the interface of AI and ophthalmology, including experts in data science, medical informatics, machine learning methodology, image processing, and public health.

项目摘要 用于医疗保健应用的机器学习（ML）模型的开发已经成为一个高度复杂的问题。 一个活跃和快速发展的研究领域，特别是在眼科，它严重依赖于模式 识别.经过训练以解释医疗数据的ML模型已经证明了显着的改进 过去十年的表现，主要是由深度学习的出现推动的。目前，多个型号 获得FDA批准，并正在临床环境中实施，使人工智能（AI）成为 优先为美国眼科学会和该领域的一般。 与传统的ML学习算法相比，深度学习利用了大量的训练， 数据集生成预测模型，能够在模式识别中实现前所未有的性能 结构化或非结构化数据。组装正确标记的数据集，这些数据集代表 目标患者人群，并且足够大，可以训练深度学习模型，这是一个挑战， 这是该领域继续发展的主要障碍。因为这些数据是稀缺的，所以最大化地 通过使它们以可用的格式广泛可用来提高它们的实用性。研究人员花费大量的时间和精力 管理用于成功训练其ML模型的数据库，但这些数据集很少随后 以公平的方式共享（可查找、可访问、可互操作和可重用）。强调结构化 以这种方式收集这些数据，同时保护受试者的私人健康信息， 跨学科合作，促进该领域的发展。 在本增刊中，我们提出了一个基于Web的公开数据科学模块，旨在 为来自各种背景的视觉科学研究人员提供概念和实践知识 需要产生公平的，ML就绪的数据。AI模块将通过以下组合来实现这一目标： 录制的视频讲座、阅读材料、知识评估和动手作业， 即时反馈。该模块将被开发并纳入现有的博士前课程， 由俄勒冈州健康与科学大学主办，但将免费提供给全球观众在线。 讲师将包括一个跨学科的团队，他们在人工智能的界面上有经验， 眼科，包括数据科学，医学信息学，机器学习方法学，图像 加工和公共卫生。