Strategic Transformation // Verified

Radiology: Real-Time
Tumor Localization.

Engineering an on-device Edge AI mesh to localize anomalies in high-res DICOM streams—achieving sub-40ms inference latency while ensuring zero PHI leaves the local facility perimeter.

Request Radiology Briefing
Outcome_TelemetryHIPAA_GATED_DIAGNOSTICS
4.5x
Throughput Lift
Manual Triage
40ms
Edge Inference
REAL_TIME
Zero
PHI Leakage
ROI: 14 Weeks

Trusted by Leading Fortune 500 Innovators

/// Strategic Context

The Mission: Diagnostic Autonomy.

Vertical
Clinical Oncology

High-volume multi-specialty hospital processing 2,000+ DICOM imaging studies daily.

Engagement
Strategic Pod

AI Architect + Computer Vision Engineer + HIPAA Compliance Lead embedded with Radiology Ops.

Objective
Clinical Precision

Reducing radiologist alert fatigue by autonomously localizing and prioritizing time-critical anomalies.

Technology
Edge AI Mesh

Local Triton Inference Servers, Quantized Vision Transformers (ViT), and HL7/FHIR event-mesh.

/// The Situation

The Reality Gap: Latency-Limited Care.

The client relied on a centralized PACS (Picture Archiving and Communication System) that required radiologists to manually review every slice of high-resolution CT and MRI scans. During peak hours, the diagnostic backlog reached 18 hours, delaying critical interventions.

The 'Execution Gap' was structural: cloud-based AI solutions were rejected due to high-latency DICOM uploads and strict hospital policies against sending PHI to external servers. The enterprise required a 'Compute-at-the-Source' model that worked within the surgical theater's real-time constraints.

Diagnostic Lag
Time-sensitive anomalies were buried in massive worklists, leading to delayed post-procedural reviews.
Privacy Friction
Existing cloud-AI options created legal bottlenecks regarding patient data sovereignty and HIPAA risk.
Analyst Fatigue
Radiologists spent 40% of their shift on manual triage rather than high-value interpretive diagnosis.
/// Architecture

The Operational Gates

01
On-Prem DICOM Ingestion
Deployed a local streaming gateway that intercepts DICOM studies directly from imaging modalities via HL7 event triggers.
Data_Ingestion
TypeOn_Prem_Edge
FormatDICOM_Native
GovernanceHIPAA_Gated
02
Quantized Inference Mesh
Executed Vision Transformer models locally using quantized weights to maintain 98%+ precision with sub-40ms latency on edge hardware.
AI_Inference
ModelViT_Quantized
ComputeTriton_Server
Latency40ms_p95
03
Agentic Triage Orchestrator
An agentic layer autonomously re-orders the PACS worklist, flagging urgent 'positive' localizations for immediate radiologist review.
Workflow_Sync
LoopAgentic_AI
IntegrationFHIR_Ready
LoggingAudit_Trail
/// The Architecture Shift

The Structural Evolution.

Dimension
Manual PACS Triage
Edge AI Localization
Processing

Serial Human Review

Radiologists view images in chronological order, regardless of severity.

Parallel Edge Inference

AI screens every slice in real-time as the scan completes, prioritizing high-risk cases.

Compliance

Policy-Restricted

Cloud processing was non-viable due to PHI transit risks and data residency laws.

Zero-Transit Trust

Inference and storage occur within the hospital's local network (HIPAA-locked).

Latency

Minutes to Hours

Wait times between imaging completion and first radiologist look were systemic.

Milliseconds

Localized tumor bounding boxes are available before the patient leaves the modality.

/// The Secret Sauce

Implementation Highlights.

LOW_LATENCY

Quantized Vision Mesh

Proprietary model quantization techniques reduced the compute footprint by 70% while preserving sub-millimeter localization accuracy.

Impact // Technical
40ms Local Inference
HIPAA_GATED

PHI De-identification Guard

Embedded local scrubber ensures metadata is pseudonymized before internal diagnostic logging, meeting strict HIPAA Title II requirements.

Impact // Regulatory
100% Data Sovereignty
AGENTIC_AI

Agentic Worklist Routing

The system doesn't just 'detect'—it autonomously negotiates with the RIS (Radiology Info System) to re-prioritize the human queue.

Impact // Commercial
4.5x Triage Throughput
/// Proprietary Assets

Accelerated by Coretus Kernels™.

HIPAA-Gated Data Kernel

Pre-audited local storage and encryption modules designed for PHI data-at-rest in edge environments.

DICOM Streamer Kernel

Production-ready connectors for low-latency image ingestion from GE, Siemens, and Philips modalities.

Diagnostic Telemetry Mesh

Real-time monitoring for model drift and 'false negative' distribution audits for clinical safety.

Edge SRE Guardrails

Automated failover logic ensuring the AI system defaults to 'Radiologist-Only' mode if hardware parity fails.

Deployment_Timeline
42% Faster
Standard AI Integration24 Weeks
Coretus Edge Pod14 Weeks
By injecting our DICOM Streamer and HIPAA-Gated Kernels, we bypassed 10 weeks of infrastructure hardening and focused on tumor-specific model tuning.
/// Verification

The Performance Delta.

METRIC: PRECISION

Tumor Localization Accuracy

Quantized ViT models achieved diagnostic parity with senior radiologists on initial localization work.

Baseline TriageVaried
Coretus Edge98.2% Precision
↓ 40ms Inference Time
METRIC: VELOCITY

Diagnostic Queue Throughput

Agentic prioritization reduced the wait time for 'highly probable' severe cases by over 80%.

Manual18h Delay
AI-Prioritized< 2h Delay
↑ 4.5x Triage Lift
METRIC: RELIABILITY

System Availability

Local edge deployment ensured diagnostics remained active even during hospital-wide internet outages.

Cloud-Dependent95.0%
Local Edge Mesh99.99%
↓ Zero Transit Latency
Outcome Status: Performance & HIPAA Verified
/// Governance

Operational Integrity.

01
Model Accountability
Decisions include activation heatmaps for radiologist validation of AI-localized anomalies.
Status: AUDIT_READY
02
Data Sovereignty
Zero-transit architecture ensures all PHI remains within the local hospital firewall.
Status: HIPAA_COMPLIANT
03
Safety Interlocks
Agentic workflow requires 'Human-in-Loop' verification before clinical worklist commitment.
Status: AGENTIC_AI
04
IP Transfer
Coretus provides 100% IP ownership of the Edge AI runtime and tuned vision models.
Status: 100% OWNED
Discuss Radiology AI
“
Coretus didn't just build a detector—they engineered a real-time diagnostic partner. We eliminated the 'Cloud-Gap' completely, allowing our radiologists to focus onInterpretation while the Edge mesh handles the triage with surgical precision.

Dr. Elena Rossi

Chief of Radiology // Tier-1 Research Hospital

/// Bio-Digital Synergy

Related Clinical Outcomes.

Art Market Intelligence
90%
Research Efficiency

Art Investment: 100% Fully Automated Intelligence.

The Reality Gap

Human fatigue led to missed undervalued lots during high-volume auction cycles on platforms like Drouot.

The Outcome

Architected independent microservices for secure OAuth2 authentication and anti-bot resilient scraping to extract structured artwork metadata.

INTELLIGENCE_VERIFIED
Explore
Digital Health / B2C
60%
Planning Efficiency

Personalized Nutrition: 60% Faster Meal Planning.

The Reality Gap

Fragmented user journeys created high friction, causing users to drop off after 7 days of manual logging.

The Outcome

Architected the backend into independent services for meal logic, grocery automation, and user profiles to ensure 99.9% availability.

BIO_INTEGRITY_VERIFIED
Explore
Digital Health & Wellness
60%
Time Reduction

Nutrition Planning: 60% Faster AI Automation.

The Reality Gap

Manual meal logging took users an average of 45 minutes daily, resulting in 70% drop-off within the first 14 days.

The Outcome

Engineered functional layers (recommendations, geolocation, auth) as independent services to ensure horizontal scalability.

ECOLOGY_READY
Explore

Bring Diagnostic Intelligence to the Edge.

Stop waiting for the cloud. We build low-latency, HIPAA-gated Edge AI meshes that enhance radiological precision without compromising patient data sovereignty.

HIPAA-Gated Local Compute

Sub-40ms Inference p95

100% Model & IP Ownership

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