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.
調査の概要
状態
詳細な説明
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.
研究の種類
入学 (推定)
連絡先と場所
研究連絡先
- 名前：William E Brandenburg, MD
- 電話番号：3035010016
- メール：info@longevitymetrics.org
研究場所
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Colorado
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Boulder、Colorado、アメリカ、80301
- 募集
- Longevity Metrics
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コンタクト：
- William Brandenburg, MD
- 電話番号：3035010016
- メール：info@longevitymetrics.org
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参加基準
適格基準
就学可能な年齢
- 大人
- 高齢者
健康ボランティアの受け入れ
サンプリング方法
調査対象母集団
説明
Inclusion Criteria:
- Age >= 18
- willing to participate in the study
Exclusion Criteria:
- Age < 18 years
研究計画
研究はどのように設計されていますか？
デザインの詳細
この研究は何を測定していますか？
主要な結果の測定
結果測定 |
メジャーの説明 |
時間枠 |
|---|---|---|
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Measurement Accuracy, Non-Inferiority to Human Scoring.
時間枠：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
時間枠：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
時間枠：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
時間枠：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
時間枠：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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二次結果の測定
結果測定 |
メジャーの説明 |
時間枠 |
|---|---|---|
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Measurement Accuracy, Superiority to Human Scoring
時間枠：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
時間枠：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
時間枠：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
時間枠：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
時間枠：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
時間枠：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
時間枠：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
時間枠：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
時間枠：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
時間枠：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
時間枠：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
時間枠：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
時間枠：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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協力者と研究者
スポンサー
捜査官
- 主任研究者：William E Brandenburg, MD、Longevity Metrics, Inc.
出版物と役立つリンク
便利なリンク
研究記録日
主要日程の研究
研究開始 (実際)
一次修了 (推定)
研究の完了 (推定)
試験登録日
最初に提出
QC基準を満たした最初の提出物
最初の投稿 (実際)
学習記録の更新
投稿された最後の更新 (実際)
QC基準を満たした最後の更新が送信されました
最終確認日
詳しくは
本研究に関する用語
キーワード
追加の関連 MeSH 用語
その他の研究ID番号
- AIML
個々の参加者データ (IPD) の計画
個々の参加者データ (IPD) を共有する予定はありますか?
IPD プランの説明
医薬品およびデバイス情報、研究文書
米国FDA規制医薬品の研究
米国FDA規制機器製品の研究
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