Evidence map›Paper›PMID 40505849›Full record

ArticleAmerican journal of ophthalmology2025

Artificial Intelligence Deep Learning Models to Predict Spaceflight Associated Neuro-Ocular Syndrome.

Alex S Huang, Jalil Jalili, Evan Walker, Robert N Weinreb, Steven S Laurie, Brandon R Macias, Mark Christopher

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in American journal of ophthalmology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Trial
  2. Review
  3. Review
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.

Alex S HuangFrom the Hamilton Glaucoma Center (A.S.H., J.J., E.W., R.N.W., and M.C.), The Viterbi Family Department of Ophthalmology, Shiley Eye Institute, University of California, San Diego, California, USA. Electronic address: aahuang@health.uscd.edu.
Jalil JaliliFrom the Hamilton Glaucoma Center (A.S.H., J.J., E.W., R.N.W., and M.C.), The Viterbi Family Department of Ophthalmology, Shiley Eye Institute, University of California, San Diego, California, USA.
Evan WalkerFrom the Hamilton Glaucoma Center (A.S.H., J.J., E.W., R.N.W., and M.C.), The Viterbi Family Department of Ophthalmology, Shiley Eye Institute, University of California, San Diego, California, USA.
Robert N WeinrebFrom the Hamilton Glaucoma Center (A.S.H., J.J., E.W., R.N.W., and M.C.), The Viterbi Family Department of Ophthalmology, Shiley Eye Institute, University of California, San Diego, California, USA.
Steven S LaurieKBR (S.S.L.), Houston, Texas, USA.
Brandon R MaciasNASA (B.R.M.), Johnson Space Center, Houston, Texas, USA.
Mark ChristopherFrom the Hamilton Glaucoma Center (A.S.H., J.J., E.W., R.N.W., and M.C.), The Viterbi Family Department of Ophthalmology, Shiley Eye Institute, University of California, San Diego, California, USA.

Funding

Vision BiostatisticsP30EY022589 · NEI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI Radha Ayyagari · 2012 to 2026
$10.3M
Deep Learning Approaches to Detect Glaucoma and Predict Progression from Spectral Domain Optical Coherence TomographyR00EY030942 · NEI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI CHRISTOPHER, MARK · 2023 to 2025
$747k
NEI NIH HHS P30 EY022589NEI NIH HHS R00 EY030942
6 · The paper itself

Abstract

purposeTo create deep learning artificial intelligence (AI) models for predicting the development of Spaceflight Associated Neuro-Ocular Syndrome (SANS) using optical coherence tomography (OCT) imaging of the optic nerve head.

designRetrospective analysis.

methodsAI deep learning models were trained to predict SANS onset by using two OCT datasets: pre- and inflight OCT images acquired from astronauts (flight data) and pre- and in-bedrest images from research participants undergoing head-down tilt bedrest as an Earth-bound model of SANS (ground data). Both datasets were partitioned by participant into training and testing data. Resnet50-based models were trained using exclusively flight data, exclusively ground data, and a combination of both. All models were evaluated based on their ability to predict SANS using only pre-flight or pre-bedrest imaging in both datasets. Performance was assessed using receiver operating characteristic areas under the curve (AUC). Class activation maps (CAMs) were generated to identify impactful image regions.

resultsThe model trained on flight data achieved an AUC (95% CI) of 0.82 (0.54-1.0) on flight data and 0.67 (0.51-0.83) on ground data. The ground-trained model achieved an AUC of 0.71 (0.50-0.91) on ground data and 0.76 (0.51-0.91) on flight data. The combined model achieved an AUC of 0.81 (0.53-0.95) and 0.72 (0.52-0.92) on flight and ground data, respectively. CAMs identified peripapillary regions of the nerve fiber layer, retinal pigmented epithelium, and anterior lamina surface as most important for predictions.

conclusionsAI models can predict SANS based on pre-flight OCT imaging with moderate-to-high performance even in this data-limited setting. The performance of cross-trained models and similarity in CAMs suggests similarity between SANS-related changes in flight and ground datasets, proving further support that head-down tilt bedrest is a reasonable Earth-bound model for SANS. NOTE: Publication of this article is sponsored by the American Ophthalmological Society.

Indexed as

Deep LearningOptic DiskSpace FlightAdultArtificial IntelligenceAstronautsFemaleHumansMaleMiddle AgedRetrospective StudiesROC CurveSyndromeTomography, Optical Coherence

Identifiers

PMID40505849
PMCPMC12481839

What OpenQuestion holds

Textmetadata
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.