Open AccessData Engineering for Machine Learning in Women's Imaging and Beyond
Author(s) -
Chen Cui,
Shinn-Huey S. Chou,
Laura J. Brattain,
Constance D. Lehman,
Anthony E. Samir
Publication year - 2019
Publication title -
american journal of roentgenology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.294
H-Index - 196
eISSN - 1546-3141
pISSN - 0361-803X
DOI - 10.2214/ajr.18.20464
Subject(s) - medicine , artificial intelligence , machine learning , perspective (graphical) , data science , medical imaging , computer science
OBJECTIVE. Data engineering is the foundation of effective machine learning model development and research. The accuracy and clinical utility of machine learning models fundamentally depend on the quality of the data used for model development. This article aims to provide radiologists and radiology researchers with an understanding of the core elements of data preparation for machine learning research. We cover key concepts from an engineering perspective, including databases, data integrity, and characteristics of data suitable for machine learning projects, and from a clinical perspective, including the HIPAA, patient consent, avoidance of bias, and ethical concerns related to the potential to magnify health disparities. The focus of this article is women's imaging; nonetheless, the principles described apply to all domains of medical imaging. CONCLUSION. Machine learning research is inherently interdisciplinary: effective collaboration is critical for success. In medical imaging, radiologists possess knowledge essential for data engineers to develop useful datasets for machine learning model development.