Longevity Metrics AI/ML Development Study
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.
연구 개요
상태
상태
정황
정황
개입 / 치료
개입 / 치료
상세 설명
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.
연구 유형
연구 유형
등록 (추정된)
등록
연락처 및 위치
연구 연락처
연구 연락처
- 이름: William E Brandenburg, MD
- 전화번호: 3035010016
- 이메일: info@longevitymetrics.org
연구 장소
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Colorado
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Boulder, Colorado, 미국, 80301
- 모병
- Longevity Metrics
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연락하다:
- William Brandenburg, MD
- 전화번호: 3035010016
- 이메일: info@longevitymetrics.org
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-
참여기준
자격 기준
자격 기준
공부할 수 있는 나이
- 성인
- 고령자
건강한 자원 봉사자를 받아들입니다
샘플링 방법
연구 인구
설명
Inclusion Criteria:
- Age >= 18
- willing to participate in the study
Exclusion Criteria:
- Age < 18 years
공부 계획
연구는 어떻게 설계됩니까?
디자인 세부사항
연구는 무엇을 측정합니까?
주요 결과 측정
주요 결과 측정
결과 측정 |
측정값 설명 |
기간 |
|---|---|---|
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Measurement Accuracy, Non-Inferiority to Human Scoring.
기간: At each model freeze, through study completion, up to 100 years.
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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.
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At each model freeze, through study completion, up to 100 years.
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Cross-Prediction of Non-Performed Results and Derived Scores
기간: At each model freeze, through study completion, up to 100 years.
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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.
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At each model freeze, through study completion, up to 100 years.
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Prediction of Remaining Life Expectancy
기간: Through study completion, up to 100 years
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Accuracy of predicting remaining life expectancy from video and library data, against observed mortality through linkage to the 100-Year Human Aging Study.
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Through study completion, up to 100 years
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Prediction of Physician Clinical Judgment
기간: At each model freeze, through study completion, up to 100 years
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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.
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At each model freeze, through study completion, up to 100 years
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Screening-Related Injuries and Adverse Events
기간: Continuously from first screening through study completion, up to 100 years
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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.
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Continuously from first screening through study completion, up to 100 years
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2차 결과 측정
2차 결과 측정
결과 측정 |
측정값 설명 |
기간 |
|---|---|---|
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Measurement Accuracy, Superiority to Human Scoring
기간: At each model freeze, through study completion, up to 100 years
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Conditional on non-inferiority, whether the model scores more reliably or precisely than a qualified human scorer.
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At each model freeze, through study completion, up to 100 years
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Uncertainty Calibration
기간: At each model freeze, through study completion, up to 100 years
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Agreement between the model's stated confidence and its observed accuracy.
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At each model freeze, through study completion, up to 100 years
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Within-Session Repeatability
기간: At each model freeze, through study completion, up to 100 years.
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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.
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At each model freeze, through study completion, up to 100 years.
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Missing-Data and Completeness Robustness
기간: At each model freeze, through study completion, up to 100 years
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Model performance on partial or incomplete input relative to complete input.
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At each model freeze, through study completion, up to 100 years
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Physician Assessment of Model Output
기간: Continuously from first model read through study completion, up to 100 years
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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.
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Continuously from first model read through study completion, up to 100 years
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Model Enrichment
기간: Through study completion, up to 100 years.
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Whether added parameters improve prediction of 100-Year outcomes over the base measure.
Reported once Primary Outcome 3 is estimable.
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Through study completion, up to 100 years.
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Improvement Over Standard Screening
기간: Through study completion, up to 100 years
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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.
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Through study completion, up to 100 years
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Incident Chronic Disease Prediction
기간: Through study completion, up to 100 years
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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.
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Through study completion, up to 100 years
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Cause-of-Death Prediction
기간: Through study completion, up to 100 years
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Concordance between predicted and actual cause of death, through the 100-Year linkage.
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Through study completion, up to 100 years
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Time to Functional Disability
기간: Through study completion, up to 100 years
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Accuracy of predicting the timing of functional disability onset, through the 100-Year linkage.
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Through study completion, up to 100 years
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Geographic Predictive Transportability
기간: At each model freeze, through study completion, up to 100 years
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Change in predictive performance with environmental distance from the validated envelope.
Residuals are returned to the Human Observatory Study.
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At each model freeze, through study completion, up to 100 years
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Temporal Stability and Drift
기간: Continuously from first model freeze through study completion, up to 100 years
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Stability of performance across calendar time and across a participant's repeat screenings.
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Continuously from first model freeze through study completion, up to 100 years
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Rate of Change
기간: Through study completion, up to 100 years
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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.
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Through study completion, up to 100 years
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공동 작업자 및 조사자
수사관
수사관
- 수석 연구원: William E Brandenburg, MD, Longevity Metrics, Inc.
간행물 및 유용한 링크
유용한 링크
연구 기록 날짜
연구 주요 날짜
연구 시작 (실제)
연구 시작
기본 완료 (추정된)
기본 완료
연구 완료 (추정된)
연구 완료
연구 등록 날짜
최초 제출
최초 제출
QC 기준을 충족하는 최초 제출
QC 기준을 충족하는 최초 제출
처음 게시됨 (실제)
처음 게시됨
연구 기록 업데이트
마지막 업데이트 게시됨 (실제)
마지막 업데이트 게시됨
QC 기준을 충족하는 마지막 업데이트 제출
QC 기준을 충족하는 마지막 업데이트 제출
마지막으로 확인됨
마지막으로 확인됨
추가 정보
이 연구와 관련된 용어
키워드
추가 관련 MeSH 약관
기타 연구 ID 번호
기타 연구 ID 번호
- AIML
개별 참가자 데이터(IPD) 계획
개별 참가자 데이터(IPD)를 공유할 계획입니까?
IPD 계획 설명
약물 및 장치 정보, 연구 문서
미국 FDA 규제 의약품 연구
미국 FDA 규제 기기 제품 연구
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