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.

项目概要 用于医疗保健应用的机器学习 (ML) 模型的开发已成为高度关注的领域。 活跃且快速发展的研究领域，特别是在眼科领域，该领域在很大程度上依赖于模式 认出。经过训练来解释医疗数据的 ML 模型已得到显着改善 过去十年的表现很大程度上是由深度学习的出现推动的。现已有多个型号 获得 FDA 批准并正在临床中实施，使人工智能 (AI) 成为 美国眼科学会和整个领域的优先考虑。 与传统的机器学习算法相比，深度学习利用大规模训练 数据集来生成能够在模式识别方面实现前所未有的性能的预测模型 在结构化或非结构化数据中。组装正确标记的数据集，这些数据集代表 目标患者群体并且足够大以训练深度学习模型，具有挑战性，并且仍然是 阻碍该领域持续进步的主要障碍。由于这些数据是稀缺的，因此最大化数据至关重要 通过以可用的格式广泛提供它们的实用性。研究人员花费了大量的时间和精力 管理用于成功训练 ML 模型的数据库，但随后很少会使用这些数据集 以公平（可查找、可访问、可互操作和可重用）的方式共享。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将增强 跨学科合作，促进该领域的进步。 在本补充中，我们提出了一个基于网络的、公开可用的数据科学模块，旨在 为来自不同背景的视觉科学研究人员提供概念和实践知识 生成公平的 ML 就绪数据所必需的。 AI模块将通过组合来实现这一目标 录制视频讲座、阅读材料、知识评估和实践作业 立即反馈。该模块将被开发并整合到现有的博士前课程中 由俄勒冈健康与科学大学主办，但将向全球观众免费在线提供。 讲师将包括一支跨学科团队，他们具有在人工智能和人工智能界面上操作的经验 眼科，包括数据科学、医学信息学、机器学习方法、图像方面的专家 加工和公共卫生。