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OS Validate · Multicentre Evaluation

External validation should not require rebuilding the dataset from scratch.

Validate surgical AI models across independent institutions. Lock verification protocols, evaluate multi-vendor optics shifts, and establish immutable result provenance.

Enterprise Surgical AI Infrastructure

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Interactive Technical Preview

Protocol Simulation & Capability Overview

Multicentre Generalization Analysis

Why External Validation Matters

4 Independent Hospital Nodes

The Generalization Gap: High Single-Center Accuracy Hides Multi-Site Failure

The model scored 94.2% at Site A where it was trained. When evaluated across independent hospitals without code modifications, performance plummeted to 68.4% at Site C due to differences in camera sensors, color grading, and surgical lighting.

Inspecting Validation Telemetry:

Site C (External Hospital)

84 procedures evaluated
Optical Tower & Hardware:

Olympus VISERA Elite II

Root Cause / Telemetry Finding:

Critical generalization failure: Optical color balance and smoke scattering degrade vessel segmentation.

Workflow Lifecycle

A governed path from clinical intent to verified results.

Every collaboration follows structured, deterministic governance stages to protect institutional sovereignty while delivering representative multicentre validation.

  1. 01

    1. Lock the protocol

    Specify model version, clinical task, input dimensions, eligibility criteria, target sites, evaluation metrics, and subgroup stratifications.

  2. 02

    2. Confirm site governance

    Verify each participating institution's data rights, ethics approvals, and node readiness before initiating evaluation.

  3. 03

    3. Evaluate across centers

    Execute benchmarks against distributed on-premises cohorts or import standardized run telemetry from controlled validation environments.

  4. 04

    4. Analyze hardware & subgroup shifts

    Inspect global mAP, device-specific performance drops (e.g. optics shifts across endoscopy manufacturers), and failure modes on complex anatomy.

  5. 05

    5. Export immutable provenance

    Generate cryptographic verification packages linking model weights, evaluation hashes, dataset IDs, and site attestations for publication or regulatory submission.

Platform Capabilities

Engineered for clinical rigor and multi-site scale.

Designed from first principles to solve the fragmentation, interoperability, and governance barriers of surgical AI.

Protocol locking for reproducible multi-institution model evaluations.

Automated stratifications measuring optical hardware shift across camera manufacturers.

Subgroup analysis across anatomical variations, surgical difficulty, and rare complications.

Cryptographic result manifests with SHA-256 verification and immutable run telemetry.

Integration with standard surgical benchmarks (Cholec80, CholecT50, Endoscapes).

Comprehensive reporting packets ready for regulatory review and publication.

Cryptographic trust and data boundaries.

Data privacy and minimization are enforced at the architectural level. Original surgical recordings never leave your physical perimeter without explicit authorization.

View Security Architecture

Immutable evaluation manifests ensure mathematical reproducibility.

Site-specific validation telemetry protects individual patient and hospital identities.

Cryptographic checksums prevent tampering with benchmark evaluation outputs.

Strict separation between model evaluation environments and institutional data stores.

Get Started

Start a Validation Project

Connect with our team to initiate a hospital deployment, request targeted cohort feasibility, or configure a multicentre validation protocol.