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

Abstract

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.