Design Experiments From Reported Results

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Validation Cohort Design: A 1,000-Subject Synchronized Pulse/Tongue/Multi-Omics Study

Objective. Build and validate an AI "crosswalk" mapping synchronized non-invasive signals (radial pulse waveforms, standardized tongue images) and blood multi-omics onto established biomedical diagnoses and Traditional Chinese Medicine (TCM) syndrome patterns, reporting reproducibility, sensitivity, and clinical utility. This is a prospective, blinded, multi-center, observational validation cohort — no intervention, no treatment recommendation.

Why the modality mix is defensible. Arterial pulse waveform analysis has strong hemodynamic grounding: lithium-dilution-calibrated beat-by-beat waveform analysis (PulseCO) tracked reference cardiac output with r² = 0.94–0.96 in ICU patients and r² = 0.96 in children, supporting pulse-derived waveforms as physiologically informative signals rather than arbitrary surrogates Pulse waveform cardiac output validation study. Blood multi-omics are also proven classifiers: circulating miRNA panels achieved AUC = 0.930 for low-risk tumor stratification Serum miRNA diagnostic modeling study, and XGBoost-style multi-modal risk models reached F1 ≈ 0.892 (cross-validation sensitivity 0.976, specificity 0.907) on a 400k+ cohort when integrating genomic and non-genomic features Multiethnic XGBoost multi-modal prediction model.

Cohort and Data Acquisition Protocol

• N = 1,000, stratified ~50% cases (prevalent biomedical diagnoses: e.g., metabolic, cardiovascular, inflammatory), ~30% TCM syndrome-defined subjects, ~20% healthy controls; block-stratified by age, sex, and ancestry, since ancestry strongly conditions omics signal Ancestry-stratified model performance evidence.
• Synchronized acquisition: same visit, same 30-min window — radial pulse tonometry (200+ Hz, 5-min capture ×3 repeated for reproducibility), standardized tongue imaging (color-calibrated, D65 lighting, 3 angles), fasting blood draw for transcriptomics (incl. miRNA RT-qPCR panel), targeted metabolomics, and standard clinical chemistry.
• Reference standard: two blinded physicians adjudicate biomedical diagnoses (EHR-verified); two independent licensed TCM practitioners score syndrome patterns; TCM inter-rater agreement (Cohen's kappa) reported as a gate — syndromes below κ = 0.6 are excluded from supervised endpoints to avoid training on noise.
• Splits: 60/20/20 train/internal-validation/held-out test, plus a temporal replication set (collected ≥3 months later) and site-external test to catch overfitting — the dominant failure mode in high-dimensional clinical ML Overfitting and validation standards for clinical ML.

Model and Metrics

Architecture: late-fusion ensemble (2D-CNN on tongue images + 1D-CNN/LSTM on pulse waveforms + gradient boosting on omics). CNN-LSTM hybrids are appropriate for time-series force/waveform prediction tasks CNN-LSTM waveform prediction architecture evidence. Reported endpoints: (1) Reproducibility — intra-visit ICC for pulse features, test-retest AUC stability across the 3 repeated captures; (2) Discrimination — sensitivity/specificity, F1, AUC per diagnosis per syndrome, with subgroup (sex/ancestry) breakdowns as the XGBoost evidence shows subgroup F1 can fall from 0.903 to 0.81 in non-majority groups Subgroup performance disparity evidence; (3) Clinical utility — decision-curve net benefit, calibration slope, and pre/post crosswalk change in diagnostic concordance (not treatment recommendations).

Red bars are ablations (reported values showing how sensitive such models are to single feature families — the study must plan comparable ablation reporting).

Power and What Would Falsify the Crosswalk

With n=1,000 and a 50/50 balanced test set (n=200), a two-sided binomial test has >99% power to detect AUC deviation from 0.5 when true AUC ≥ 0.75; 80% power to detect AUC ≥ 0.65. The crosswalk is falsified if: (a) held-out test AUC drops >0.10 below internal validation AUC (overfitting); (b) test-retest pulse ICC < 0.70 (signal not reproducible); (c) TCM syndrome prediction does not exceed chance after κ-gating; (d) subgroup AUC differences exceed 0.15 without adjustment. Blindspots: the 1,000-subject sample is small relative to biobank-scale modeling, TCM syndrome labels are observer-dependent, miRNA/omics effects may not replicate externally External validation limitation evidence, and none of this evidence establishes any causal or treatment claim — the design tests predictive correspondence only.