Longevity Metrics AI/ML Development Study

Conditions

• Activities of Daily Living
• Aging
• Cardiovascular Disease Prevention
• Cognitive Dysfunction
• Diagnostic Techniques and Procedures
• Frailty
• Health Services Accessibility
• Metabolic Syndrome
• Mortality
• Musculoskeletal Diseases
• Physical Disability
• Preventive Health Services

Eligibility Criteria

Sex

ALL

Age

18 Years - N/A

Healthy Volunteers

Accepted

Interventions

• AIML scored health screening battery — DIAGNOSTIC_TEST
  Video and audio-captured physical, cognitive, and observational screening tests scored by frozen, versioned AI models; models automatically score standard tests (sit-to-rise, timed walk, chair stand), predict non-performed results from performed ones, and predict clinical judgment; all outputs are physician-reviewed clinical decision aids, and no model output reaches a participant report without provider confirmation.

Study Details

This study builds AI models that score diagnostic screening tests, and that predict screening results, clinical judgment, and life expectancy. Longevity Metrics collects a battery of clinical tests on each participant, in whole or in part, and follows every participant for life. The sit-to-rise test and the timed walk are scored by hand today, from a person's count. A model scores the same test from video instead. It also measures what no one can count by eye - speed, asymmetry, steadiness - so one capture yields both the original score and additional measurements, intended to enrich the model and strengthen what it predicts. Every test in a participant's record measures the same body, so the tests are correlated: a test that was performed carries information about one that was not. A model trained across the library learns those relationships and estimates a missing result from the results that are present. Each estimate is checked against records where that part was actually measured, and over decades against death and disease through linkage to the 100-Year Human Aging Study (NCT07563777). The hypothesis is that the full battery can eventually be predicted across modalities with high accuracy using a few short video clips, replacing most in-person screening. That would let preventive screening reach people and places a physical laboratory cannot. How far the input can be reduced is the question this study exists to answer. Every model is a physician-reviewed clinical decision aid until it is cleared by the FDA.

Key Dates

First listed

Sep 9, 2026

Start date

Aug 8, 2026

Status verified

Sep 2026

Primary completion

Dec 31, 2099

Completion

Dec 31, 2099

Study Design

Enrollment

1,000,000 participants (estimated)

Primary Outcome Measure

Measurement Accuracy, Non-Inferiority to Human Scoring. [ Time Frame: At each model freeze, through study completion, up to 100 years. ]

Central Contacts

• William E Brandenburg, MD
  3035010016
  [email protected]

Locations (1)

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