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
1   Institute of Health Care Engineering with European Testing Center of Medical Devices, Graz University of Technology, Austria
,
Leticia Rittner
2   Medical Imaging Computing Lab (MICLab), School of Electrical and Computer Engineering University of Campinas, Brazil
,
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 (SSII) › Author Affiliations

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.



Publication History

Article published online:
26 December 2023

© 2023. IMIA and Thieme. This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/)

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  • References

  • 1 Hsu W, Baumgartner C, Deserno TM. Notable papers and new directions in sensors, signals, and imaging informatics. IMIA Yearb Med Inform 2021Aug;30(1):150-8. doi: 10.1055/s-0041-1726526.
  • 2 Baumgartner C, Deserno TM. Best Research Papers in the Field of Sensors, Signals, and Imaging Informatics 2021. Yearb Med Inform 2022 Aug;31(1):296-302. doi: 10.1055/s-0042-1742545.
  • 3 Hsu W, Baumgartner C, Deserno TM. Notable papers and trends from 2019 in sensors, signals, and imaging informatics. IMIA Yearbook of Med Inform 2020 Aug;29(1):139-44. doi: 10.1055/s-0040-1702004.
  • 4 Hsu W, Baumgartner C, Deserno TM. Advancing artificial intelligence in sensors, signals, and imaging informatics. IMIA Yearb Med Inform 2019 Aug;28(1):115-7. doi: 10.1055/s-0039-1677943.
  • 5 Malik J, Devecioglu OC, Kiranyaz S, Ince T, Gabbouj M. Real-Time Patient-Specific ECG Classification by 1D Self-Operational Neural Networks. IEEE Trans Biomed Eng 2022 May;69(5):1788-801. doi: 10.1109/TBME.2021.3135622.
  • 6 Xia Y, Chen X, Ravikumar N, Kelly C, Attar R, Aung N, et al. Automatic 3D+t four-chamber CMR quantification of the UK biobank: integrating imaging and non-imaging data priors at scale. Med Image Anal 2022 Aug;80:102498. doi: 10.1016/j.media.2022.102498.
  • 7 Srichan C, Srichan W, Danvirutai P, Ritsongmuang C, Sharma A, Anutrakulchai S. Non-invasively accuracy enhanced blood glucose sensor using shallow dense neural networks with NIR monitoring and medical features. Sci Rep 2022 Feb 2;12(1):1769. doi: 10.1038/s41598-022-05570-8.
  • 8 De Giovanni E, Teijeiro T, Millet GP, Atienza D. Adaptive R-Peak Detection on Wearable ECG Sensors for High-Intensity Exercise. IEEE Trans Biomed Eng 2023 Mar;70(3):941-53. doi: 10.1109/TBME.2022.3205304.
  • 9 Jahmunah V, Ng EYK, Tan RS, Oh SL, Acharya UR. Explainable detection of myocardial infarction using deep learning models with Grad-CAM technique on ECG signals. Comput Biol Med 2022 Jul;146:105550. doi: 10.1016/j.compbiomed.2022.105550.
  • 10 Wen H, Kang J. A novel deep learning package for electrocardiography research. Physiol Meas 2022 Nov 4;43(11). doi: 10.1088/1361-6579/ac9451.
  • 11 Qayyum A, Sultani W, Shamshad F, Tufail R, Qadir J. Single-shot retinal image enhancement using untrained and pretrained neural networks priors integrated with analytical image priors. Comput Biol Med 2022 Sep;148:105879. doi: 10.1016/j.compbiomed.2022.105879.
  • 12 Bridge CP, Gorman C, Pieper S, Doyle SW, Lennerz JK, Kalpathy-Cramer J, et al. Highdicom: a Python Library for Standardized Encoding of Image Annotations and Machine Learning Model Outputs in Pathology and Radiology. J Digit Imaging 2022 Dec;35(6):1719-37. doi: 10.1007/s10278-022-00683-y.
  • 13 Berntsen J, Rimestad J, Lassen JT, Tran D, Kragh MF. Robust and generalizable embryo selection based on artificial intelligence and time-lapse image sequences. PLoS One 2022 Feb 2;17(2):e0262661. doi: 10.1371/journal.pone.0262661.
  • 14 de Aguiar EJ, Traina C, Traina AJM. Security and Privacy in Machine Learning for Health Systems: Strategies and Challenges. IMIA Yearbook of Med Inform 2023:269-81.
  • 15 Baumgartner C, Harer J, Schröttner J, editors. Medical Devices and In Vitro Diagnostics: Requirements in Europe. Cham: Springer Nature; 2022. ISBN: 2731-0493, eISBN: 2731-0507. doi: 0.1007/978-3-030-98743-5.