AI training module for Vision Science

项目总结

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.