CC BY-NC-ND 4.0 · Yearb Med Inform 2020; 29(01): 139-144
DOI: 10.1055/s-0040-1702004
Section 4: Sensor, Signal and Imaging Informatics
Georg Thieme Verlag KG Stuttgart

Notable Papers and Trends from 2019 in Sensors, Signals, and Imaging Informatics

William Hsu
1  Medical & Imaging Informatics, Department of Radiological Sciences, David Geffen School of Medicine at UCLA, United States of America
Christian Baumgartner
2  Institute of Health Care Engineering with European Testing Center of Medical Devices, Graz University of Technology, Austria
Thomas M. Deserno
3  Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Braunschweig, Germany
Section Editors for the IMIA Yearbook Section on Sensors, Signals, and Imaging Informatics› Author Affiliations
Further Information

Publication History

Publication Date:
21 August 2020 (online)


Objective: To highlight noteworthy papers that are representative of 2019 developments in the fields of sensors, signals, and imaging informatics.

Method: A broad literature search was conducted in January 2020 using PubMed. Separate predefined queries were created for sensors/signals and imaging informatics using a combination of Medical Subject Heading (MeSH) terms and keywords. Section editors reviewed the titles and abstracts of both sets of results. Papers were assessed on a three-point Likert scale by two co-editors, rated from 3 (do not include) to 1 (should be included). Papers with an average score of 2 or less were then read by all three section editors, and the group nominated top papers based on consensus. These candidate best papers were then rated by at least six external reviewers.

Results: The query related to signals and sensors returned a set of 255 papers from 140 unique journals. The imaging informatics query returned a set of 3,262 papers from 870 unique journals. Based on titles and abstracts, the section co-editors jointly filtered the list down to 50 papers from which 15 candidate best papers were nominated after discussion. A composite rating after review determined four papers which were then approved by consensus of the International Medical Informatics Association (IMIA) Yearbook editorial board. These best papers represent different international groups and journals.

Conclusions: The four best papers represent state-of-the-art approaches for processing, combining, and analyzing heterogeneous sensor and imaging data. These papers demonstrate the use of advanced machine learning techniques to improve comparisons between images acquired at different time points, fuse information from multiple sensors, and translate images from one modality to another.