CC BY-NC-ND 4.0 · Yearb Med Inform 2020; 29(01): 139-144
DOI: 10.1055/s-0040-1702004
Section 4: Sensor, Signal and Imaging Informatics
Synopsis
Georg Thieme Verlag KG Stuttgart

Notable Papers and Trends from 2019 in Sensors, Signals, and Imaging Informatics

William Hsu
1   Medical & Imaging Informatics, Department of Radiological Sciences, David Geffen School of Medicine at UCLA, United States of America
,
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 Sensors, Signals, and Imaging Informatics › Author Affiliations
Further Information

Publication History

Publication Date:
21 August 2020 (online)

Summary

Objective: To highlight noteworthy papers that are representative of 2019 developments in the fields of sensors, signals, and imaging informatics.

Method: A broad literature search was conducted in January 2020 using PubMed. Separate predefined queries were created for sensors/signals and imaging informatics using a combination of Medical Subject Heading (MeSH) terms and keywords. Section editors reviewed the titles and abstracts of both sets of results. Papers were assessed on a three-point Likert scale by two co-editors, rated from 3 (do not include) to 1 (should be included). Papers with an average score of 2 or less were then read by all three section editors, and the group nominated top papers based on consensus. These candidate best papers were then rated by at least six external reviewers.

Results: The query related to signals and sensors returned a set of 255 papers from 140 unique journals. The imaging informatics query returned a set of 3,262 papers from 870 unique journals. Based on titles and abstracts, the section co-editors jointly filtered the list down to 50 papers from which 15 candidate best papers were nominated after discussion. A composite rating after review determined four papers which were then approved by consensus of the International Medical Informatics Association (IMIA) Yearbook editorial board. These best papers represent different international groups and journals.

Conclusions: The four best papers represent state-of-the-art approaches for processing, combining, and analyzing heterogeneous sensor and imaging data. These papers demonstrate the use of advanced machine learning techniques to improve comparisons between images acquired at different time points, fuse information from multiple sensors, and translate images from one modality to another.

 
  • References

  • 1 Mavrogiorgou A, Kiourtis A, Perakis K, Pitsios S, Kyriazis D. IoT in healthcare: achieving interoperability of high-quality data acquired by IoT medical devices. Sensors (Basel) 2019; 19 (09) 1978
  • 2 Suresh A, Udendhran R, Balamurgan M, Varatharajan R. A novel internet of things framework integrated with real time monitoring for intelligent healthcare environment. J Med Syst 2019; 43 (06) 165
  • 3 Eby PR. Breast cancer: let imaging be our guide and improving patient outcomes be our goal. Radiology 2019; Aug; 292 (02) 309-10
  • 4 Gibofsky A, Thiele R. A better look at rheumatoid arthritis: using imaging to improve patient outcomes. Semin Arthritis Rheum 2019; 48 (04) 763
  • 5 van Ooijen PMA, Nagaraj Y, Olthof A. Medical imaging informatics, more than ‘just’ deep learning. Eur Radiol 2020
  • 6 Chandrasekaran B, Gangadhar S, Conrad JM. A survey of multisensor fusion techniques, architectures and methodologies. SoutheastCon 2017; 1-8
  • 7 Chandra BS, Sastry CS, Jana S. Robust heartbeat detection from multimodal data via CNN-based generalizable information fusion. IEEE Trans Biomed Eng 2019; 66 (03) 710-7
  • 8 Yuan Y, Jia K. FusionAtt: deep fusional attention networks for multi-channel biomedical signals. Sensors 2019; 19 (11) 2429
  • 9 Zhu T, Pimentel MAF, Clifford GD, Clifton DA. Unsupervised bayesian inference to fuse biosignal sensory estimates for personalizing care. IEEE J Biomed Health Inf 2019; 23 (01) 47-58
  • 10 Campanella G, Hanna MG, Geneslaw L, Miraflor A, Werneck Krauss Silva V, Busam KJ. et al. Clinical-grade computational pathology using weakly supervised deep learning on whole slide images. Nat Med 2019; 25 (08) 1301-9
  • 11 Armanious K, Jiang C, Fischer M, Küstner T, Hepp T, Nikolaou K. et al MedGAN: medical image translation using GANs. Comput Med Imaging Graph 2020; 79: 101684
  • 12 Yildirim O, Baloglu UB, Tan RS, Ciaccio EJ, Acharya UR. A new approach for arrhythmia classification using deep coded features and LSTM networks. Comput Methods Programs Biomed 2019; 176: 121-33
  • 13 de Vos BD, Berendsen FF, Viergever MA, Sokooti H, Staring M, Išgum I. A deep learning framework for unsupervised affine and deformable image registration. Med Image Anal 2019; 52: 128-43
  • 14 Larson DB, Magnus DC, Lungren MP, Shah NH, Langlotz CP. Ethics of using and sharing clinical imaging data for artificial intelligence: a proposed framework. Radiology 2020; 295 (03) 675-82
  • 15 Reyes M, Meier R, Pereira S, Silva CA, Dahlweid FM, von Tengg-Kobligk H. et al. On the interpretability of artificial intelligence in radiology: challenges and opportunities. Radiol Artif Intell 2020; 2 (03) e190043
  • 16 Char DS, Shah NH, Magnus D. Implementing Machine Learning in Health Care — Addressing Ethical Challenges. New Engl J Med 2018; 378 (11) 981-3
  • 17 Kohli M, Geis R. Ethics, Artificial Intelligence, and Radiology. J Am Col Radiol 2018; 15 (09) 1317-9
  • 18 Kahn CE. Do the Right Thing. Radiol Artif Intell 2019 1. (2)
  • 19 Geis JR, Brady AP, Wu CC, Spencer J, Ranschaert E, Jaremko JL. et al. Ethics of artificial intelligence in radiology: summary of the joint European and North American multisociety Statement. Radiology 2019; 293 (02) 436-40
  • 20 Safdar NM, Banja JD, Meltzer CC. Ethical considerations in artificial intelligence. Eur J Radiol 2020; 122: 108768
  • 21 Li L, Qin L, Xu Z, Yin Y, Wang X, Kong B. et al. Artificial Intelligence Distinguishes COVID-19 from Community Acquired Pneumonia on Chest CT. Radiology 2020; 200905
  • 22 Yasaka TM, Lehrich BM, Sahyouni R. Peer-to-Peer Contact Tracing: Development of a Privacy-Preserving Smartphone App. JMIR Mhealth Uhealth 2020; 8 (04) e18936
  • 23 Wynants L, Van Calster B, Bonten MMJ, Collins GS, Debray TPA, De Vos M. et al. Prediction models for diagnosis and prognosis of covid-19 infection: systematic review and critical appraisal. BMJ 2020; 369: m1328
  • 24 Viner RM, Whittaker E. Kawasaki-like disease: emerging complication during the COVID-19 pandemic. Lancet 2020; 395 10239 1741-3