CaltechAUTHORS
Published March 2023 | Version public
Journal Article Metadata-only

Biomonitoring and precision health in deep space supported by artificial intelligence

Creators

  • 1. ROR icon Ames Research Center
  • 2. ROR icon Baylor College of Medicine
  • 3. ROR icon Cornell University
  • 4. ROR icon Stanford University
  • 5. ROR icon Bay Area Environmental Research Institute
  • 6. ROR icon University of Georgia
  • 7. ROR icon University of California, San Francisco
  • 8. ROR icon University of Colorado Anschutz Medical Campus
  • 9. ROR icon Icahn School of Medicine at Mount Sinai
  • 10. ROR icon The University of Texas Medical Branch at Galveston
  • 11. ROR icon Johnson Space Center
  • 12. ROR icon University of Pennsylvania
  • 13. ROR icon University of Wisconsin–Madison
  • 14. ROR icon University of North Florida
  • 15. ROR icon University of Minnesota
  • 16. ROR icon San Jose State University
  • 17. ROR icon Mayo Clinic
  • 18. ROR icon McGill University
  • 19. ROR icon Imperial College London
  • 20. ROR icon California Institute of Technology
  • 21. ROR icon Salk Institute for Biological Studies
  • 22. ROR icon Lawrence Berkeley National Laboratory
  • 23. ROR icon Joint BioEnergy Institute
  • 24. ROR icon Glenn Research Center
  • 25. ROR icon Michigan State University
  • 26. ROR icon The University of Texas at San Antonio
  • 27. ROR icon Johns Hopkins University
  • 28. ROR icon Jet Propulsion Lab
  • 29. ROR icon King's College London
  • 30. ROR icon Center for the Advancement of Science in Space
  • 31. ROR icon University of Alabama at Birmingham
  • 32. ROR icon Harvard University

Abstract

Human exploration of deep space will involve missions of substantial distance and duration. To effectively mitigate health hazards, paradigm shifts in astronaut health systems are necessary to enable Earth-independent healthcare, rather than Earth-reliant. Here we present a summary of decadal recommendations from a workshop organized by NASA on artificial intelligence, machine learning and modelling applications that offer key solutions toward these space health challenges. The workshop recommended various biomonitoring approaches, biomarker science, spacecraft/habitat hardware, intelligent software and streamlined data management tools in need of development and integration to enable humanity to thrive in deep space. Participants recommended that these components culminate in a maximally automated, autonomous and intelligent Precision Space Health system, to monitor, aggregate and assess biomedical statuses.

Additional Information

© 2023 Springer Nature Limited. We thank all June 2021 participants and speakers at the 'NASA Workshop on Artificial Intelligence & Modeling for Space Biology'. Thanks go to the NASA Space Biology Program, part of the NASA Biological and Physical Sciences Division within the NASA Science Mission Directorate, as well as the NASA Human Research Program (HRP). We also thank the Space Biosciences Division and Space Biology at Ames Research Center (ARC), especially D. Ly, R. Vik and P. Vaishampayan. We are grateful for the support provided by NASA GeneLab and the NASA Ames Life Sciences Data Archive. Additional thanks go to S. Bhattacharya (NASA Space Biology Program Scientist), K. Martin (ARC Lead of Exploration Medical Capability (an Element of HRP)), as well as L. Lewis (ARC NASA HRP Lead). S.V.C. is funded by NASA Human Research Program grant NNJ16HP24I. S.E.B. holds the Heidrich Family and Friends Endowed Chair in Neurology at UCSF. S.E.B. also holds the Distinguished Professorship I in Neurology at UCSF. S.E.B. is funded by an NSF Convergence Accelerator award (2033569) and NIH/NCATS Translator award (1OT2TR003450). G.I.M. was supported by the Translational Research Institute for Space Health, through NASA NNX16AO69A (project no. T0412). E.L.A. was supported by the Translational Research Institute for Space Health, through NASA NNX16AO69A. C.E.M. acknowledges NASA grants NNX14AH50G and NNX17AB26G. This work was also part of the DOE Agile BioFoundry, supported by the US Department of Energy, Energy Efficiency and Renewable Energy, Bioenergy Technologies Office, and the DOE Joint BioEnergy Institute, supported by the Office of Science, Office of Biological and Environmental Research, through contract no. DE-AC02-05CH11231 between Lawrence Berkeley National Laboratory and the US Department of Energy. S.V.K. is funded by the Canadian Space Agency (19HLSRM04) and Natural Sciences and Engineering Research Council (NSERC, RGPIN-288253). J.H.Y. is funded by NIH grant no. R00 GM118907 and the Agilent Early Career Professor Award. These authors contributed equally: Ryan T. Scott, Lauren M. Sanders. Contributions: All authors contributed ideas and discussion during the joint workshop writing session or were speakers at the 'NASA Workshop on Artificial Intelligence & Modeling for Space Biology'. R.T.S., L.M.S. and S.V.C. prepared the manuscript. All authors provided input and feedback on the manuscript. The authors declare no competing interests.

Additional details

Identifiers

Eprint ID
122469
Resolver ID
CaltechAUTHORS:20230725-857112000.41

Funding

NASA
NNJ16HP24I
University of California, San Francisco
NSF
ITE-2033569
NIH
1OT2TR003450
NASA
NNX16AO69A
NASA
NNX14AH50G
NASA
NNX17AB26G
Department of Energy (DOE)
DE-AC02-05CH11231
Canadian Space Agency (CSA)
19HLSRM04
Natural Sciences and Engineering Research Council of Canada (NSERC)
RGPIN-288253
NIH
R00 GM118907
Agilent Early Career Professor Award

Dates

Created
2023-08-14
Created from EPrint's datestamp field
Updated
2023-08-15
Created from EPrint's last_modified field

Caltech Custom Metadata

Caltech groups
Division of Biology and Biological Engineering (BBE)