CohortForge AI – Clinical Validation & Data Co-Development Partner

1. The Challenge

Medical imaging AI models are only as reliable as the cohorts used to train and validate them. Real-world clinical research datasets frequently suffer from:

Missing & Incomplete Data: Omitted clinical variables and incomplete scan acquisitions force teams into slow manual reconstruction or reduced study sizes.

Demographic & Subgroup Imbalances: Underrepresented patient groups (by sex, age, stage, or comorbidity) weaken fairness and clinical generalizability.

Protocol & Scanner Heterogeneity: Variations in acquisition settings (e.g., peak tube voltage / kVp) create intensity shifts and radiomic instability across sites.

2. Solution & Core Capabilities

CohortForge AI https://med.prodigy-ai-solutions.com/ addresses these bottlenecks through an integrated, fail-closed workflow that couples generative modeling with failure-visible validation gates:

Mixed-Type Clinical Cohort Synthesis & Completion: Employs a conditional variational autoencoder (CVAE) to generate mixed tabular clinical records, reconstruct masked fields, and rebalance underrepresented patient subgroups while preserving observed values and clinical relationships.

CT Slice Reconstruction & Completion: Uses a 2.5D residual U-Net leveraging through-plane context to reconstruct withheld CT slices and complete missing series without requiring voxel-wise co-registration.

Protocol Harmonization & Radiomic Stability: Normalizes CT intensity distributions across different tube voltage (kVp) settings and evaluates texture randomness (GLCM Joint Entropy) to guarantee biomarker stability under perturbation.

Multi-Gate Validation & Governance: Evaluates synthetic data across independent, non-compensating quality gates—including statistical fidelity (Kolmogorov–Smirnov distance), subgroup balance, anatomical plausibility, HU consistency, radiomic preservation, and subject-level copy-risk detection.

Fail-Closed Integration: A 29-field data crosswalk blocks model training and data release if field semantics, linkage, patient splits, or approvals are unresolved.

3. Target Applications & Customers

Cancer Centers & Hospitals: Validating retrospective clinical-imaging datasets and expanding underrepresented patient cohorts for research.

Imaging CROs & Health-Data Platforms: Qualifying multimodal datasets and providing protocol-harmonized evidence for commercial AI clients.

Pharma & Biotech AI Teams: Building reproducible, audit-ready data pipelines for target discovery and trial stratification.

4. Technology Readiness & Proof of Concept (TRL 4)

Working MVP research demonstrator with interactive web interface: Live Demonstrator.

Measurable Lab Performance: Achieved 74.3% imbalance reduction in artificial clinical fixtures and a 22.45% median reduction in CT reconstruction error (MAE) over linear interpolation in a 22-subject benchmark study (7,830 slice predictions).

Scalable Infrastructure: Engineered for high-performance computing, with prior multi-GPU model profiling on CINECA Leonardo A100 node clusters.

What We Are Looking For

Clinical & Hospital Validation Partners:

Objective: Retrospective validation of CohortForge AI on real-world, governed thoracic oncology (e.g., NSCLC) clinical records and baseline CT scans.

Role: Jointly define clinical variables, missingness states, and success criteria; evaluate synthetic data plausibility and metric improvement (fidelity, missing-data completion, subgroup balancing, radiomic stability) on real patient datasets held out from development.

EU Horizon & EIT Health Consortia:

Role: Joining grant proposals (Horizon Europe, EIT Health, Digital Europe) as the dedicated deep-tech AI and data-qualification partner.

Imaging CROs & Health-Data Platforms:

Role: Design-partner pilots to test protocol harmonization (kVp) and multimodal cohort qualification in secure, real-world data environments.