Deep learning

4 papers4 findings

C, W, M, H: cells, worms or flies, mice, humans. Dark: independent labs found the same change there. Light: one lab or one study. Outline: nothing yet.

Labs are independent when their papers share no last author and no institution.

Against: findings that point the other way from most findings on the same outcome.

On aging: the drug’s trials ARN judged to be about aging, of every trial that tests it for any use.

No results posted: trials that ended more than a year ago with no results on ClinicalTrials.gov and no paper in ARN’s record.

Company ties: findings with an author at a company, or from a paper whose competing-interests statement declares ties to one.

1 lab in 1 country

Funders: National Institute on Aging and 7 more

In silico1

Outcomes

Early disease diagnosis

upin reviews1

1 study

Deep learning improves early disease diagnosis.
“Recent advances in deep learning have significantly improved early disease diagnosis”
Review
A Systematic Review of Deep Learning Algorithms for the Early Detection of Chronic Disease
Research Square9 Sep 2026Preprint

Biological aging prediction

upin humans1

1 study

Deep learning improves biological aging prediction in humans.
“Recent advances in artificial intelligence (AI), utilizing deep learning techniques, have significantly enhanced the prediction of biological aging”
Review
Deep aging clocks: AI-powered strategies for biological age estimation
Ageing research reviews2 Sep 2025

Associations

Early-onset Alzheimer's disease classification

upin silico1

1 study

Deep learning predicts early-onset Alzheimer's disease classification in silico (accuracy 94%).
“The CNN was able to differentiate early onset patients from normal aging with an accuracy of 94%.”
In silicoT1 MRI imaging
Beyond Images : Seeing the unseen for early detection of Alzheimer's Disease
AAIC 2025 abstract23 Dec 2025

More on Early-onset Alzheimer's disease classification

Systemic disease

upin humans1

1 study

Deep learning predicts systemic disease in humans.
“retinal imaging modalities, such as color fundus photography, optical coherence tomography (OCT), OCT angiography (OCTA), and wide-field imaging, are increasingly integrated with deep learning algorithms to detect, predict, and manage a broad spectrum of systemic diseases”
Review
Artificial intelligence oculomics for systemic health and longevity medicine: 2025 and beyond
Current opinion in ophthalmology4 Sep 2025