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CC BY 4.0 · Yearb Med Inform 2024; 33(01): 277-279
DOI: 10.1055/s-0044-1800757
Section 12: Sensor, Signal and Imaging Informatics
Synopsis

Sensors, Signals, and Imaging Informatics: Best contributions from 2023

Leticia Rittner
1   Medical Imaging Computing Lab (MICLab), School of Electrical and Computer Engineering, University of Campinas, Brazil
,
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 Knowledge Representation and Management › Author Affiliations
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Summary

Objectives: To identify and highlight research papers that represent the advances and trends in the field of sensors, signals, and imaging informatics in 2023.

Method: We performed a bibliographic search on Scopus and PubMed databases using Medical Sub-ject Heading (MeSH) terms combined with keywords. Our aim was to build specific queries for sen-sors, signals, and imaging informatics. We disregarded journals that returned less than three papers on the query and then evaluated titles and abstracts of the papers using a 3-point Likert scale, ranging from 1 (do not include) to 3 (should be included). Only the papers with a total score of 8 or more were re-evaluated again, this time considering the full text, and the top 14 papers with the highest scores were then reviewed by external reviewers and editors of the International Medical Informatics Association (IMIA) Yearbook.

Results: Among the 643 returned papers published in 2023 in the various areas of sensors, signals, and imaging informatics (SSII), we selected 58 papers with at least 8 Likert points (in total). After a comprehensive evaluation, we identified 14 papers as the best contributions and sent them to eight external reviewers. The full review process resulted in a selection of the four best papers, which were then approved by consensus by the IMIA Yearbook Editorial Board. Although the imaging informatics sub-search returned all of these four papers, one is about sensorless freehand 3D ultrasound recon-struction (representing sensors), and another deals with video-based pulse rate estimation (representing signals).

Conclusions: Sensors, signals, and imaging informatics is a dynamic field of intensive research. The four best papers in 2023 represent advanced approaches focusing on DL-based image processing, analysis, and indicate a shift in the research field from sensor technology development to biosignal and image analysis.

Keywords

International Medical Informatics Association Yearbook - Sensor informatics - Signal in-formatics - Imaging informatics - Biomedical informatics - Machine learning - Deep learning - Personalized medicine


Publication History

Article published online:
08 April 2025

© 2024. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://creativecommons.org/licenses/by/4.0/)

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  • References

  • 1 Dehghani F, Derafshi R, Lin J, Bayat, Bento M. Alzheimer Disease Detection Studies: Perspective on Multi-Modal Data. IMIA Yearbook of Med Inform. 2024;266-276. doi: 10.1055/s-0044-1800756
    PubMed
  • 2 Baumgartner C, Rittner L, Deserno TM. Machine and deep learning dominate recent innovations in sensors, signals, and imaging informatics. Yearb Med Inform. 2023 Aug;32(1):282-285. doi: 10.1055/s-0043-1768743.
    PubMed
  • 3 Baumgartner C, Deserno TM. Best research papers in the field of sensors, signals, and imaging infor-matics 2021. Yearb Med Inform. 2022 Aug;31(1):296-302. doi: 10.1055/s-0042-1742545.
    PubMed
  • 4 Hsu W, Baumgartner C, Deserno TM. Notable papers and new directions in sensors, signals, and im-aging informatics. Yearb Med Inform. 2021 Aug;30(1):150-158. doi: 10.1055/s-0041-1726526.
    PubMed
  • 5 Wang CF, Wang TY, Kuo PH, Wang HL, Li SZ, Lin CM, et al. Upper-Arm Photoplethysmographic Sensor with One-Time Calibration for Long-Term Blood Pressure Monitoring. Biosensors (Basel). 2023 Feb 25;13(3):321. doi: 10.3390/bios13030321.
    PubMed
  • 6 Kumaki D, Motoshima Y, Higuchi F, Sato K, Sekine T, Tokito S. Unobstructive Heartbeat Monitoring of Sleeping Infants and Young Children Using Sheet-Type PVDF Sensors. Sensors (Basel). 2023 Nov 17;23(22):9252. doi: 10.3390/s23229252.
    PubMed
  • 7 Ortega-Rodríguez J, Gómez-González JF, Pereda E. Selection of the Minimum Number of EEG Sen-sors to Guarantee Biometric Identification of Individuals. Sensors (Basel). 2023 Apr 24;23(9):4239. doi: 10.3390/s23094239.
    PubMed
  • 8 Celik G. CovidCoughNet: A new method based on convolutional neural networks and deep feature extraction using pitch-shifting data augmentation for covid-19 detection from cough, breath, and voice signals. Comput Biol Med. 2023 Sep;163:107153. doi: 10.1016/j.compbiomed.2023.107153.
    PubMed
  • 9 Qiu X, Wang S, Wang R, Zhang Y, Huang L. A multi-head residual connection GCN for EEG emotion recognition. Comput Biol Med. 2023 Sep;163:107126. doi: 10.1016/j.compbiomed.2023.107126.
    PubMed
  • 10 Cao Z, Wang G, Xu L, Li C, Hao Y, Chen Q, et al. Intelligent antepartum fetal monitoring via deep learning and fusion of cardiotocographic signals and clinical data. Health Inf Sci Syst. 2023 Mar 19;11(1):16. doi: 10.1007/s13755-023-00219-w.
    PubMed
  • 11 Li Z, Fan Q, Bilgic B, Wang G, Wu W, Polimeni JR, Miller KL, et al. Diffusion MRI data analysis assisted by deep learning synthesized anatomical images (DeepAnat). Med Image Anal. 2023 May;86:102744. doi: 10.1016/j.media.2023.102744.
    PubMed
  • 12 Helland RH, Ferles A, Pedersen A, Kommers I, Ardon H, Barkhof F, et al. Segmentation of glioblas-tomas in early post-operative multi-modal MRI with deep neural networks. Sci Rep. 2023 Nov 2;13(1):18897. doi: 10.1038/s41598-023-45456-x.
    PubMed
  • 13 Chen Y, Lu X, Xie Q. Collaborative networks of transformers and convolutional neural networks are powerful and versatile learners for accurate 3D medical image segmentation. Comput Biol Med. 2023 Sep;164:107228. doi: 10.1016/j.compbiomed.2023.107228.
    PubMed
  • 14 Graham S, Vu QD, Jahanifar M, Raza SEA, Minhas F, Snead D, et al. One model is all you need: Multi-task learning enables simultaneous histology image segmentation and classification. Med Image Anal. 2023 Jan;83:102685. doi: 10.1016/j.media.2022.102685.
    PubMed
  • 15 Luo M, Yang X, Wang H, Dou H, Hu X, Huang Y, et al. RecON: Online learning for sensorless free-hand 3D ultrasound reconstruction. Med Image Anal. 2023 Jul;87:102810. doi: 10.1016/j.media.2023.102810.
    PubMed
  • 16 Huang W, Hua C, Guo Y, Gao W, Li Y, Zheng Y. Super resolution imaging reconstruction reveals that gold standard methods may not correctly conclude neural/brain functional recovery. Comput Med Imaging Graph. 2023 Apr;105:102198. doi: 10.1016/j.compmedimag.2023.102198.
    PubMed
  • 17 Ouzar Y, Djeldjli D, Bousefsaf F, Maaoui C. X-iPPGNet: A novel one stage deep learning architecture based on depthwise separable convolutions for video-based pulse rate estimation. Comput Biol Med. 2023 Mar;154:106592. doi: 10.1016/j.compbiomed.2023.106592.
    PubMed
  • 18 Nie Q, Li C, Yang J, Yao Y, Sun H, Jiang T, et al. OII-DS: A benchmark Oral Implant Image Dataset for object detection and image classification evaluation. Comput Biol Med. 2023 Dec;167:107620. doi: 10.1016/j.compbiomed.2023.107620.
    PubMed