Machine and Deep Learning Dominate Recent Innovations in Sensors, Signals and Imaging Informatics

Christian Baumgartner; Leticia Rittner; Thomas M. Deserno; Section Editors for the IMIA Yearbook Section on Sensors, Signals and Imaging Informatics (SSII)

doi:10.1055/s-0043-1768743

Yearbook of Medical Informatics, Table of Contents

CC BY-NC-ND 4.0 · Yearb Med Inform 2023; 32(01): 282-285
DOI: 10.1055/s-0043-1768743

Section 12: Sensor, Signal and Imaging Informatics

Synopsis

Machine and Deep Learning Dominate Recent Innovations in Sensors, Signals and Imaging Informatics

Christian Baumgartner

¹Institute of Health Care Engineering with European Testing Center of Medical Devices, Graz University of Technology, Austria

,

Leticia Rittner

²Medical Imaging Computing Lab (MICLab), School of Electrical and Computer Engineering University of Campinas, Brazil

,

Thomas M. Deserno

³Peter 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 (SSII)

› Author Affiliations

Abstract

Summary

Objectives: This review presents research papers highlighting notable developments and trends in sensors, signals, and imaging informatics (SSII) in 2022.

Method: We performed a bibliographic search in PubMed combining Medical Subject Heading (MeSH) terms and keywords to create particular queries for sensors, signals, and imaging informatics. Only papers published in journals containing greater than three articles in the search query were considered. Using a three-point Likert scale (1 = not include, 2 = perhaps include, 3 = include), we reviewed the titles and abstracts of all database results. Only articles that scored three times Likert scale 3, or two times Likert scale 3, and one time Likert scale 2 were considered for full paper review. On this pre-selection, only papers with a total of at least eight points of the three section co-editors were considered for external review. Based on the external reviewers, we selected the top two papers representing significant research in SSII.

Results: Among the 469 returned papers published in 2022 in the various areas of SSII, 90, 31, and 348 papers for sensors, signals, and imaging informatics, and then, the full review process selected the two best papers. From the 469 papers, the section co-editors identified 29 candidate papers with at least 8 Likert points in total, of which 9 were nominated as the best contributions after a full paper assessment. Five external reviewers evaluated the nominated papers, and the two highest-scoring papers were selected based on the overall scores of all external reviewers. A consensus of the International Medical Informatics Association (IMIA) Yearbook editorial board finally approved the nominated papers. Machine and deep learning-based techniques continue to be the dominant theme in this field.

Conclusions: Sensors, signals, and imaging informatics is a dynamic field of intensive research with increasing practical applications to support medical decision-making on a personalized basis.

Keywords

Biomedical informatics - machine learning - deep learning - personalized medicine

Full Text

References

References
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