Preparing Medical Imaging Data for Machine Learning

Artificial intelligence (AI) continues to garter substantial interest in medical imaging. The potential applications are vast and include the entirety of the. medical imaging life cycle from image exertion to diagnosis to outcome prediction. The chief obstacles to development and clinical implementation of AI algorithms include availability of sufficiently large, curated, and representative training data that includes expert labeling (eg, annotations). Current supervised AI methods require a curation proem for data to optimally train, validate, and test algorithms. Currently, most and research group industry have limited data access based on small sample sizes from small geographic areas. In addition, die preparation of data is a costly and time-intensive process, the results of which ate algorithms with limited utility and poor generalization. In this article, the authors describe fundamental steps for preparing medical imaging data in AI algorithm development, explain current limitations to data curation, and explore new approaches to address the problem of data availability. (C) RSNA, 2020