Best practices for clinical skin image acquisition in translational artificial intelligence research Guidelines


Authors: Phung, M.; Muralidharan, V.; Rotemberg, V.; Novoa, R. A.; Chiou, A. S.; Sadée, C. Y.; Rapaport, B.; Yekrang, K.; Bitz, J.; Gevaert, O.; Ko, J. M.; Daneshjou, R.
Title: Best practices for clinical skin image acquisition in translational artificial intelligence research
Abstract: Recent advances in artificial intelligence research have led to an increase in the development of algorithms for detecting malignancies from clinical and dermoscopic images of skin diseases. These methods are dependent on the collection of training and testing data. There are important considerations when acquiring skin images and data for translational artificial intelligence research. In this paper, we discuss the best practices and challenges for light photography image data collection, covering ethics, image acquisition, labeling, curation, and storage. The purpose of this work is to improve artificial intelligence for malignancy detection by supporting intentional data collection and collaboration between subject matter experts, such as dermatologists and data scientists. © 2023 The Authors
Journal Title: Journal of Investigative Dermatology
Volume: 143
Issue: 7
ISSN: 0022-202X
Publisher: Elsevier Science, Inc.  
Date Published: 2023-07-01
Start Page: 1127
End Page: 1132
Language: English
DOI: 10.1016/j.jid.2023.02.035
PUBMED: 37353282
PROVIDER: scopus
DOI/URL:
Notes: Short survey -- Source: Scopus
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