Longevity Metrics AI/ML Development Study: A Standing Data Library and Model-Development Platform for Predicting and Validating Health Measurements, Longevity, and Disease
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 发起方
- 入组人数
- 1,000,000
- 试验地点
- 1
- 主要终点
- Measurement Accuracy, Non-Inferiority to Human Scoring.
研究概览
简要总结
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.
详细描述
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.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Other
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •Age >= 18
- •willing to participate in the study
排除标准
- •Age < 18 years
结局指标
主要结局
Measurement Accuracy, Non-Inferiority to Human Scoring.
时间窗: 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.
Cross-Prediction of Non-Performed Results and Derived Scores
时间窗: 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.
Prediction of Remaining Life Expectancy
时间窗: 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.
Prediction of Physician Clinical Judgment
时间窗: 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.
Screening-Related Injuries and Adverse Events
时间窗: 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.
次要结局
- Measurement Accuracy, Superiority to Human Scoring(At each model freeze, through study completion, up to 100 years)
- Uncertainty Calibration(At each model freeze, through study completion, up to 100 years)
- Within-Session Repeatability(At each model freeze, through study completion, up to 100 years.)
- Missing-Data and Completeness Robustness(At each model freeze, through study completion, up to 100 years)
- Physician Assessment of Model Output(Continuously from first model read through study completion, up to 100 years)
- Model Enrichment(Through study completion, up to 100 years.)
- Improvement Over Standard Screening(Through study completion, up to 100 years)
- Incident Chronic Disease Prediction(Through study completion, up to 100 years)
- Cause-of-Death Prediction(Through study completion, up to 100 years)
- Time to Functional Disability(Through study completion, up to 100 years)
- Geographic Predictive Transportability(At each model freeze, through study completion, up to 100 years)
- Temporal Stability and Drift(Continuously from first model freeze through study completion, up to 100 years)
- Rate of Change(Through study completion, up to 100 years)