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Longevity Metrics AI/ML Development Study

keskiviikko 2. syyskuuta 2026 päivittänyt: Longevity Metrics, Inc.

Longevity Metrics AI/ML Development Study: A Standing Data Library and Model-Development Platform for Predicting and Validating Health Measurements, Longevity, and Disease

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.

Tutkimuksen yleiskatsaus

Tila

Rekrytointi

Ehdot

Interventio / Hoito

Yksityiskohtainen kuvaus

The models serve three aims. First, they automatically score simple physical and cognitive tests that already predict function and in some cases mortality, such as the sit-to-rise test and the timed walk. A model reads richer detail from the same recording than a human scorer can, so it improves on the human score rather than only reproducing it.

Second, they predict the parts of a screening a participant did not obtain from the parts that were performed, and increasingly from inexpensive standardized inputs such as a short video. Within a single record, every test is correlated with the others, so each test can both predict the ones that were not performed and serve as the truth against which those predictions are checked. A missing-data engine fills any missing part of a record by leave-one-out across the library.

Third, they predict the physician's clinical judgment where no determining measurement exists.

Models are developed by milestone freezing with forward validation. No model is validated on records it was trained on. Each model is validated in two stages. It is first validated against a human scorer for measurement accuracy, which gates its use as a clinical decision aid. It is then validated over decades for what it predicts about death and disease.

The platform's distinguishing asset is mortality. Every participant is followed for life, so a small library with verified death outcomes answers questions a much larger library without them cannot. The library is the durable asset, studied across geography and time by increasingly capable models.

This study is one of four that compound into one system. The 100-Year Human Aging Study (NCT07563777) supplies the clinical data and validates what it means for health, disease, disability, and death. The Human Observatory Study (NCT07646782) does the same with sociodemographic and environmental data, and receives each model's geographic residuals. The Health Ahead Comparative Effectiveness Study (NCT07669168) moves the screening toward increasing automation and mobility while maintaining quality. This study builds the models that make automation, prediction, and broad utilization possible.

A physician or licensed provider reviews and signs every result a participant receives.

Opintotyyppi

Havainnollistava

Ilmoittautuminen (Arvioitu)

1000000

Yhteystiedot ja paikat

Tässä osiossa on tutkimuksen suorittajien yhteystiedot ja tiedot siitä, missä tämä tutkimus suoritetaan.

Opiskeluyhteys

Opiskelupaikat

  • Yhdysvallat
    • Colorado
      • Boulder, Colorado, Yhdysvallat, 80301
        • Rekrytointi
        • Longevity Metrics
        • Ottaa yhteyttä:

Osallistumiskriteerit

Tutkijat etsivät ihmisiä, jotka sopivat tiettyyn kuvaukseen, jota kutsutaan kelpoisuuskriteereiksi. Joitakin esimerkkejä näistä kriteereistä ovat henkilön yleinen terveydentila tai aiemmat hoidot.

Kelpoisuusvaatimukset

Opintokelpoiset iät

  • Aikuinen
  • Vanhempi Aikuinen

Hyväksyy terveitä vapaaehtoisia

Joo

Näytteenottomenetelmä

Ei-todennäköisyysnäyte

Tutkimusväestö

Participants enrolled in the 100-Year Human Aging Study (NCT07563777) through the unified consent, receiving any Longevity Metrics health screening service at any fixed, mobile, or remote (online) site, current and future. Participants are self-selected community members of all health statuses; no exclusions based on health status, diagnosis, or population group. This study adds no separate enrollment: all records analyzed and all prospective measurements collected under this protocol arise from participants consented under the associated protocols. Participants are followed for life, with mortality, incident disease, and functional-status outcomes ascertained through linkage to the 100-Year Human Aging Study.

Kuvaus

Inclusion Criteria:

  • Age >= 18
  • willing to participate in the study

Exclusion Criteria:

  • Age < 18 years

Opintosuunnitelma

Tässä osiossa on tietoja tutkimussuunnitelmasta, mukaan lukien kuinka tutkimus on suunniteltu ja mitä tutkimuksella mitataan.

Miten tutkimus on suunniteltu?

Suunnittelun yksityiskohdat

Mitä tutkimuksessa mitataan?

Ensisijaiset tulostoimenpiteet

Tulosmittaus
Toimenpiteen kuvaus
Aikaikkuna
Measurement Accuracy, Non-Inferiority to Human Scoring.
Aikaikkuna: At each model freeze, through study completion, up to 100 years.
Agreement between the model's value and the reference standard, established as non-inferiority to a qualified human scorer where a human reference exists. Gates deployment as a clinical decision aid.
At each model freeze, through study completion, up to 100 years.
Cross-Prediction of Non-Performed Results and Derived Scores
Aikaikkuna: At each model freeze, through study completion, up to 100 years.
Accuracy of predicting, from video or from the performed part of a record, both the screening results the participant did not obtain, against the measured value; and the derived scores computed from a complete record, including the Longevity Score, biological age, and estimated age at death, against the value the scoring engine produces from the full measured record.
At each model freeze, through study completion, up to 100 years.
Prediction of Remaining Life Expectancy
Aikaikkuna: Through study completion, up to 100 years
Accuracy of predicting remaining life expectancy from video and library data, against observed mortality through linkage to the 100-Year Human Aging Study.
Through study completion, up to 100 years
Prediction of Physician Clinical Judgment
Aikaikkuna: At each model freeze, through study completion, up to 100 years
Accuracy of predicting the physician's clinical judgment where no determining measurement exists, against the sealed record. Applies to models that predict a clinical determination rather than a measured value. Where a determining test exists but was not performed, the outcome falls under Primary Outcome 2.
At each model freeze, through study completion, up to 100 years
Screening-Related Injuries and Adverse Events
Aikaikkuna: Continuously from first screening through study completion, up to 100 years
Participant injuries or adverse events attributed to measurements added under this study, such as the sit-to-rise test. Ascertained from two independent sources so that an event missed by one is still captured: the tester logs any fall or injury at the time of screening, and the participant reports separately on the post-screening form.
Continuously from first screening through study completion, up to 100 years

Toissijaiset tulostoimenpiteet

Tulosmittaus
Toimenpiteen kuvaus
Aikaikkuna
Measurement Accuracy, Superiority to Human Scoring
Aikaikkuna: At each model freeze, through study completion, up to 100 years
Conditional on non-inferiority, whether the model scores more reliably or precisely than a qualified human scorer.
At each model freeze, through study completion, up to 100 years
Uncertainty Calibration
Aikaikkuna: At each model freeze, through study completion, up to 100 years
Agreement between the model's stated confidence and its observed accuracy.
At each model freeze, through study completion, up to 100 years
Within-Session Repeatability
Aikaikkuna: At each model freeze, through study completion, up to 100 years.
Where a test is captured twice back to back, the stability of the model's output across the two captures. Not applicable where repeat capture is impractical, or where a second effort measures fatigue because the test is performed to failure.
At each model freeze, through study completion, up to 100 years.
Missing-Data and Completeness Robustness
Aikaikkuna: At each model freeze, through study completion, up to 100 years
Model performance on partial or incomplete input relative to complete input.
At each model freeze, through study completion, up to 100 years
Physician Assessment of Model Output
Aikaikkuna: Continuously from first model read through study completion, up to 100 years
Three physician-recorded fields per result, identical to the structured judgment recorded under the Health Ahead Comparative Effectiveness Study, so that one instrument governs physician review of model output across the platform. Agreement with the model read, 1 to 10, 1 strongly disagree to 10 strongly agree. Safety, the potential for patient harm had the read been acted upon as written, 1 to 10, 1 high potential for serious harm to 10 no potential for patient harm. Override, binary, recording whether the released interpretation differs in any substantive respect from the read. The two rating scales ascend toward the better state. Agreement records what the physician thought of a read; override records what the physician did with it, and the two diverge routinely.
Continuously from first model read through study completion, up to 100 years
Model Enrichment
Aikaikkuna: Through study completion, up to 100 years.
Whether added parameters improve prediction of 100-Year outcomes over the base measure. Reported once Primary Outcome 3 is estimable.
Through study completion, up to 100 years.
Improvement Over Standard Screening
Aikaikkuna: Through study completion, up to 100 years
Incremental value of video-derived information over standard screening for remaining life expectancy, through the 100-Year linkage. Reported once Primary Outcome 3 is estimable.
Through study completion, up to 100 years
Incident Chronic Disease Prediction
Aikaikkuna: Through study completion, up to 100 years
Accuracy of predicting new chronic disease onset, through the 100-Year linkage. Reported by predictor set, model and physician, so the prognostic comparison is made on a common outcome.
Through study completion, up to 100 years
Cause-of-Death Prediction
Aikaikkuna: Through study completion, up to 100 years
Concordance between predicted and actual cause of death, through the 100-Year linkage.
Through study completion, up to 100 years
Time to Functional Disability
Aikaikkuna: Through study completion, up to 100 years
Accuracy of predicting the timing of functional disability onset, through the 100-Year linkage.
Through study completion, up to 100 years
Geographic Predictive Transportability
Aikaikkuna: At each model freeze, through study completion, up to 100 years
Change in predictive performance with environmental distance from the validated envelope. Residuals are returned to the Human Observatory Study.
At each model freeze, through study completion, up to 100 years
Temporal Stability and Drift
Aikaikkuna: Continuously from first model freeze through study completion, up to 100 years
Stability of performance across calendar time and across a participant's repeat screenings.
Continuously from first model freeze through study completion, up to 100 years
Rate of Change
Aikaikkuna: Through study completion, up to 100 years
Accuracy of predicting the change in a measure between visits, and of predicting outcomes from that change. Applies to every model with repeat captures. The within-participant change and its pace are tested against death, disease, and functional decline alongside the cross-sectional value.
Through study completion, up to 100 years

Yhteistyökumppanit ja tutkijat

Täältä löydät tähän tutkimukseen osallistuvat ihmiset ja organisaatiot.

Sponsori

Longevity Metrics, Inc.

Tutkijat

  • Päätutkija: William E Brandenburg, MD, Longevity Metrics, Inc.

Julkaisuja ja hyödyllisiä linkkejä

Tutkimusta koskevien tietojen syöttämisestä vastaava henkilö toimittaa nämä julkaisut vapaaehtoisesti. Nämä voivat koskea mitä tahansa tutkimukseen liittyvää.

Hyödyllisiä linkkejä

Opintojen ennätyspäivät

Nämä päivämäärät seuraavat ClinicalTrials.gov-sivustolle lähetettyjen tutkimustietueiden ja yhteenvetojen edistymistä. National Library of Medicine (NLM) tarkistaa tutkimustiedot ja raportoidut tulokset varmistaakseen, että ne täyttävät tietyt laadunvalvontastandardit, ennen kuin ne julkaistaan ​​julkisella verkkosivustolla.

Opi tärkeimmät päivämäärät

Opiskelun aloitus (Todellinen)

Lauantai 8. elokuuta 2026

Ensisijainen valmistuminen (Arvioitu)

Torstai 31. joulukuuta 2099

Opintojen valmistuminen (Arvioitu)

Torstai 31. joulukuuta 2099

Opintoihin ilmoittautumispäivät

Ensimmäinen lähetetty

Keskiviikko 2. syyskuuta 2026

Ensimmäinen toimitettu, joka täytti QC-kriteerit

Keskiviikko 2. syyskuuta 2026

Ensimmäinen Lähetetty (Todellinen)

Keskiviikko 9. syyskuuta 2026

Tutkimustietojen päivitykset

Viimeisin päivitys julkaistu (Todellinen)

Keskiviikko 9. syyskuuta 2026

Viimeisin lähetetty päivitys, joka täytti QC-kriteerit

Keskiviikko 2. syyskuuta 2026

Viimeksi vahvistettu

Tiistai 1. syyskuuta 2026

Lisää tietoa

Tähän tutkimukseen liittyvät termit

Avainsanat

Muita asiaankuuluvia MeSH-ehtoja

Muut tutkimustunnusnumerot

  • AIML

Yksittäisten osallistujien tietojen suunnitelma (IPD)

Aiotko jakaa yksittäisten osallistujien tietoja (IPD)?

EI

IPD-suunnitelman kuvaus

Individual participant data are not shared. The complete participant record includes facial and voice recordings, genomic data, and neighborhood-resolution geography, and cannot be de-identified; it is never released. Identifiable data are accessed only by approved researchers working within Longevity Metrics secure facilities under data use agreements, with no data removed. Specific derived, aggregate, and statistical outputs are de-identified to HIPAA Safe Harbor standards and may be published, shared with collaborators, or contributed to open-access research databases. Models, risk estimates, and analytic findings derived from the research data may be provided to external organizations or used commercially; no participant record and no dataset containing participant data is transferred.

Lääke- ja laitetiedot, tutkimusasiakirjat

Tutkii yhdysvaltalaista FDA sääntelemää lääkevalmistetta

Ei

Tutkii yhdysvaltalaista FDA sääntelemää laitetuotetta

Ei

Nämä tiedot haettiin suoraan verkkosivustolta clinicaltrials.gov ilman muutoksia. Jos sinulla on pyyntöjä muuttaa, poistaa tai päivittää tutkimustietojasi, ota yhteyttä register@clinicaltrials.gov. Heti kun muutos on otettu käyttöön osoitteessa clinicaltrials.gov, se päivitetään automaattisesti myös verkkosivustollemme .

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