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Clinical Imaging Infrastructure • Radiology AI

Radiology AI that survives deployment needs dataset reliability — not just accuracy.

Edge cases, scanner variation, protocol drift, and ambiguous ground truth are what break radiology AI in production. Trinzz ships radiology datasets as governed releases with quantified consensus, traceability, release gates, and monitoring triggers — so your model remains defensible and stable after deployment.

SOC 2 Type II ISO 27001:2022 HIPAA compliant PHI controls + audit logs Versioned releases
Clinical Imaging Infrastructure • Radiology AI annotation lifecycle diagram
Clinical‑grade lifecycle: model assist + expert validation + QC/consensus + release gate.

Imaging annotation — engineered for deployment

Trinzz turns imaging annotation into a governed reliability system: AI assists, experts validate, agreement is quantified, QC is systematic, and releases ship with evidence.

Clinical‑grade ground truthExpert validation + adjudication + consensus confidence — per case.
Reliability scoring (CG‑DRS)Release gates backed by quality, traceability, and drift readiness metrics.
Evidence PackQC report, consensus report, lineage export, monitoring spec.
Monitoring loopDrift triggers + edge‑case loopback to continuously improve.
Radiology reliability controls

What radiology teams actually need to deploy

This is what turns a dataset into something procurement + clinical validation can sign off on.

TraceabilityConsensusQCMonitoring
Clinical SOPs as CodeRadiology labeling guidelines enforced as policies; exceptions routed for adjudication.
Expert Consensus EngineMulti‑radiologist agreement (κ/α), dispute routing, and confidence per case.
QC + Error TaxonomyAutomated checks + structured error categories that drive remediation.
Shift Readiness (Sites/Scanners)Baseline stratification + drift thresholds (e.g., PSI) across protocols/scanners.
Traceability & LineageProvenance, reviewer identity, SOP references, timestamps — audit‑ready end‑to‑end.
Release Gates + MonitoringPass/Conditional/Fail gates + post‑release monitoring policies to prevent regressions.
What we annotate

Modalities + label types (built for clinical deployment)

Radiology programs fail when modality/protocol complexity is treated like generic labeling.

CTX‑rayMRIUltrasound
Detection + triageBounding boxes, keypoints, structured findings with reviewer confidence.
SegmentationOrgan/lesion masks with topology checks and volumetric consistency where needed.
Clinical attributesSeverity, measurements, laterality, staging fields aligned to clinical workflows.
Deployability lever: we design labels as downstream “clinical interfaces,” not just training targets.
Use cases

Where this should convert

Written for enterprise imaging teams evaluating deployment readiness.

HospitalsDiagnostics networksMed‑AI enterprises
Deployable detection/triage modelsMove from “works on test set” to “safe in hospitals”: validated labels + drift triggers.
Foundation model curationLarge-scale pretraining datasets with governed provenance and QC discipline.
Regulatory validation readinessEvidence exports designed for internal validation committees and external review.
Multi-site generalizationQuantify worst‑site gaps and build targeted edge‑case augmentation plans.
Vendor / PACS integrationDataset packaging + manifests aligned to deployment and change control.
Continuous reliability programOngoing drift checks + re-benchmarking + escalation pipeline for new failure modes.
Evidence pack

What you get with every release

Evidence reduces “trust debates” to artifacts.

QC reportConsensus reportLineage exportMonitoring spec
  • Versioned manifest + hashes + deterministic release notes
  • QC report: sampling plan, pass rates, structured error taxonomy
  • Consensus report: κ/α, adjudication summary, confidence per cohort
  • Subgroup risk register: site/scanner/protocol bins + worst-group gaps
  • Monitoring spec: PSI thresholds, alert routing, remediation playbook
  • Lineage export: who labeled what, when, under which SOP; approvals
Release Gate: Pass / Conditional / Fail based on QC strength, consensus confidence, and traceability completeness.
Reliability is not a claim. It's a tracked system — with thresholds, evidence, and release discipline.
Request a Reliability Audit Share a dataset manifest. We return CG‑DRS diagnostic + validation plan + monitoring triggers.