ReviewCommunications biology2026
A review of self-supervised imaging phenotypes for genome-wide association studies.
Review in Communications biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Biobank-scale imaging-genomic cohorts enable genome-wide association studies (GWASs) of organ structure and function. Traditional approaches rely on expert-defined image-derived phenotypes (IDPs), which capture only a fraction of the biological variation in high-content images. Powered by artificial intelligence, self-supervised learning (SSL) learns representations directly from intrinsic image structures, creating scalable, data-driven traits. Here, we review the technical frameworks and challenges of SSL-based imaging genetics and their applications in brain MRI, cardiac MRI, and retinal imaging. Early studies suggest that these data-driven approaches can complement traditional IDPs and may expand the phenotypic space available for discovery of heritable imaging traits, although broader validation across cohorts, ancestries, scanners, and imaging protocols remains needed.
Indexed as
Identifiers
42919866What OpenQuestion holds
Registered trials
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.