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Core Technology

PMS Imaging Standard™

The Gold Standard for Medical Imaging Datasets

A comprehensive framework that ensures every medical imaging dataset is clinically validated, fully traceable, bias-aware, and compliance-ready.

Medical Imaging AI Has a Data Problem

Most medical imaging datasets used for AI training lack standardized validation, traceability, and regulatory alignment.

This creates compounding risk: models trained on unverified data face regulatory rejection, bias issues, and clinical safety concerns. The cost of poor data isn't just technical — it's measured in delayed treatments, failed submissions, and eroded trust.

No standardized validation for most public datasets
Incomplete provenance chains break regulatory audits
Hidden biases in demographic and acquisition diversity

Four Pillars of Certification

Every dataset certified under the PMS Imaging Standard meets rigorous requirements across four critical dimensions.

Clinical Validation

Every annotation reviewed and signed off by board-certified radiologists. No automated-only labels.

Full Traceability

Complete data provenance from source through every transformation. Every change logged, every origin verified.

Bias Awareness

Systematic demographic and acquisition protocol diversity checks to identify and document representation gaps.

Regulatory Compliance

Aligned with FDA and CE Mark requirements for AI/ML medical devices. Audit-ready documentation included.

How It Works

From raw DICOM data to certified, ML-ready export — a rigorous five-stage pipeline.

Data Ingestion DICOM files & metadata 1 Validation Rules POC: 16 rules Full: 21 rules 2 Compliance Check FDA & CE Mark alignment 3 Radiologist Sign-off Board-certified review 4 Certified Export ML-ready with documentation 5

What the Validator Checks

Our validation engine runs comprehensive checks across six critical dimensions to ensure dataset integrity.

Metadata Completeness

All required DICOM tags present and correctly formatted

Annotation Consistency

Label formats, coordinate systems, and terminology aligned

Demographic Representation

Statistical checks for age, sex, and ethnicity distribution

DICOM Tag Integrity

Tag values validated against VR definitions and standards

De-identification Verification

PHI removal confirmed across all headers and pixel data

Provenance Chain Completeness

Full audit trail from original source through all transformations

Integration & Export

Datasets certified under the PMS Imaging Standard export directly into your preferred ML framework.

MONAI

NVIDIA's open-source framework for healthcare AI

nnU-Net

Self-configuring segmentation framework

Hugging Face

Datasets library for ML research

Certified vs. Typical Datasets

Dimension PMS Certified Typical Dataset
Clinical Validation Radiologist-verified Automated or unverified
Traceability Full provenance chain Partial or missing
Bias Assessment Systematic diversity checks Rarely documented
Regulatory Alignment FDA/CE ready Not addressed
Export Compatibility MONAI, nnU-Net, HF ~ Variable formats
Annotation Consistency Standardized schema Inconsistent labeling

Start Building with Certified Data

Request a sample dataset certified under the PMS Imaging Standard

Request Sample Dataset