Evidence map›Paper›PMID 36008494›Full record

ReviewNPJ microgravity2022

Terrestrial health applications of visual assessment technology and machine learning in spaceflight associated neuro-ocular syndrome.

Joshua Ong, Alireza Tavakkoli, Nasif Zaman, Sharif Amit Kamran, Ethan Waisberg, Nikhil Gautam, Andrew G Lee

Abstract readReview
In one paragraph

Review in NPJ microgravity, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.

0numbers the graph read from it
0cells of the map it votes in
28citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

28 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Article
  6. Review
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Review
  16. Review
  17. Review
  18. Article
  19. Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Joshua OngUniversity of Pittsburgh School of Medicine, Pittsburgh, PA, USA.ORCID http://orcid.org/0000-0003-4860-827X
Alireza TavakkoliHuman-Machine Perception Laboratory, Department of Computer Science and Engineering, University of Nevada, Reno, Reno, NV, USA.
Nasif ZamanHuman-Machine Perception Laboratory, Department of Computer Science and Engineering, University of Nevada, Reno, Reno, NV, USA.
Sharif Amit KamranHuman-Machine Perception Laboratory, Department of Computer Science and Engineering, University of Nevada, Reno, Reno, NV, USA.ORCID http://orcid.org/0000-0002-1681-9438
Ethan WaisbergUniversity College Dublin School of Medicine, Belfield, Dublin, Ireland.ORCID http://orcid.org/0000-0001-8999-0212
Nikhil GautamDepartment of Computer Science, Rice University, Houston, TX, USA.ORCID http://orcid.org/0000-0002-5472-0172
Andrew G LeeCenter for Space Medicine, Baylor College of Medicine, Houston, TX, USA. aglee@houstonmethodist.org.

Funding

National Aeronautics and Space Administration (NASA) 80NSSC20K1831
6 · The paper itself

Abstract

The neuro-ocular effects of long-duration spaceflight have been termed Spaceflight Associated Neuro-Ocular Syndrome (SANS) and are a potential challenge for future, human space exploration. The underlying pathogenesis of SANS remains ill-defined, but several emerging translational applications of terrestrial head-mounted, visual assessment technology and machine learning frameworks are being studied for potential use in SANS. To develop such technology requires close consideration of the spaceflight environment which is limited in medical resources and imaging modalities. This austere environment necessitates the utilization of low mass, low footprint technology to build a visual assessment system that is comprehensive, accessible, and efficient. In this paper, we discuss the unique considerations for developing this technology for SANS and translational applications on Earth. Several key limitations observed in the austere spaceflight environment share similarities to barriers to care for underserved areas on Earth. We discuss common terrestrial ophthalmic diseases and how machine learning and visual assessment technology for SANS can help increase screening for early intervention. The foundational developments with this novel system may help protect the visual health of both astronauts and individuals on Earth.

Identifiers

PMID36008494
PMCPMC9411571

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.