Best Research Papers in the Field of Sensors, Signals, and Imaging Informatics 2021

Abstract

Objectives: In this synopsis, we identify and highlight research papers representing noteworthy developments in signals, sensors, and imaging informatics in 2021. Methods: A broad literature search was conducted on PubMed and Scopus databases. We combined Medical Subject Heading (MeSH) terms and keywords to construct particular queries for sensors, signals, and imaging informatics. Except for the sensor section, we only consider papers that have been published in journals providing at least three articles in the query response. Using a three-point Likert scale (1=not include, 2=maybe include, and 3=include), we reviewed the titles and abstracts of all database returns. Only those papers which reached two times three points were further considered for full paper review using the same Likert scale. Again, we only considered works with two times three points and provided these for external reviews. Based on the external reviews, we selected three best papers, as it happens that the three highest ranked papers represent works from all three parts of this section: sensors, signals, and imaging informatics. Results: The search for papers was executed in January 2022. After removing duplicates and conference proceedings, the query returned a set of 88, 376, and 871 papers for sensors, signals, and imaging informatics, respectively. For signals and images, we filtered out journals that had less than three papers in the query results, reducing the number of papers to 215 and 512, respectively. From this total of 815 papers, the section co-editors identified 35 candidate papers with two times three Likert points, from which nine candidate best papers were nominated after full paper assessment. At least three external reviewers then rated the remaining papers and the three best-ranked papers were selected using the composite rating of all external reviewers. By accident, these three papers represent each of the three fields of sensor, signal, and imaging informatics. They were approved by consensus of the International Medical Informatics Association (IMIA) Yearbook editorial board. Deep and machine learning techniques are still a dominant topic as well as concepts beyond the state-of-the-art. Conclusions: Sensors, signals, and imaging informatics is a dynamic field of intense research. Current research focuses on creating and processing heterogeneous sensor data towards meaningful decision support in clinical settings.

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

“…Searching the literature for the best papers for the SSII section was challenging given the broad scope of this field. This year, we used the same search terms and acronyms for the queries as last year [2], which have been continuously expanded and harmonized in previous years [1,3,4]. Nevertheless, we focused on English-language research articles and excluded review articles.…”

“…In mid-January 2023, we performed the final query, which yielded a set of 90, 31, and 348 articles on sensors, signals, and imaging informatics, respectively. As last year [2], we set the threshold at a minimum of three articles for a relevant journal and excluded articles from irrelevant journals. We then reviewed all 469 titles and abstracts and scored them independently on a three-point Likert scale (1 = not included, 2 = maybe included, 3 = included).…”

Informatics for One Health

“…The top nine papers in the Sensors, Signals and Imaging Informatics section were focused on machine learning and deep learning approaches to support decisions, prognosis, and novel therapies. Section editors Christian Baumgartner, Leticia Rittner, Thomas M. Deserno noted that even with the many selections of papers in imaging and sensors, it remains very difficult to identify and select truly novel and groundbreaking approaches [28]. For the survey paper by de Aguiar et al [29], security and privacy aspects of machine learning are very complex and important to consider as more machine and deep learning methods are used.…”

Inclusive Digital Health

“…The Special Section provides a review of significant papers for 2021 that focus on health equity and informatics tools. Section editors Brian Dixon and John Holmes found studies that characterized significant challenges facing biomedical informatics with respect to practices that support equity and literacy in the design of health information systems [5]. Challenges are further addressed in the survey paper where Koehle et al stress that "it may be easy to look at the current health equity landscape as irreparable, having been built on hundreds of years of oppression, marginalization, and discrimination.…”

“…Twenty public datasets used in these studies are also listed. SSII co-editors Christian Baumgartner and Thomas Deserno wrote a synopsis featuring their selection of the best papers of 2021 in which an emerging trend was notably observed, namely the combination of signal processing and sensor technologies, and where the development of deep learning approaches for medical image processing remains substantial [34].…”