RAYLENCE // PEDIATRIC_RADIOLOGY
RESEARCH_PROTOTYPE
Radiology Workflow System

PediatricWorkflow Copilot

AI-Assisted Pediatric Radiology

Solving administrative burnout and cognitive diagnostic error.Enabling faster results for families.

We don't replace, we empower

Core Systems

The Dual Engine

Architecture: Transformer
Module::Scribe

Automated Documentation

Transforms radiologist from "Dictator" to "Validator". AI generates structured reports from imaging data, freeing physicians to focus on diagnosis.

Integration StandardHL7 / FHIR Native
Module::SafetyNet

Satisfaction of Search Defense

A silent "AI Resident" detecting subtle secondary pathologies. Reduces cognitive tunnel vision by flagging incidental findings.

Model ArchitectureVision Transformer
System Architecture

The Data Pipeline

Phase::Ingest

The Trigger

Radiologist opens study in existing PACS. No workflow interruption. Raylence activates silently in the background via standard integration protocols.

DICOM_HANDLER
chest_pa_lateral.dcmDICOM
clinical_context.jsonJSON
PROTOCOL: HL7/DICOMPACS_INTEGRATED
inference_pipeline.logPROCESSING
01> INITIALIZING_PIPELINE...
02> LOADING_MODEL_CONFIG...
03> PREPROCESSING_IMAGE_DATA...
04> NORMALIZING_PIXEL_VALUES...
05> DETECTING_ANATOMICAL_LANDMARKS...
06> ANALYZING_LUNG_FIELDS...
07> CHECKING_OSSIFICATION_CENTERS...
08> EVALUATING_THYMUS_REGION...
09> QUALITY_GATE_CHECK: PASSED
10> RUNNING_PRIMARY_INFERENCE...
11> SCANNING_FOR_PATHOLOGY...
12> GENERATING_STRUCTURED_FINDINGS...
13> CROSS_REFERENCING_LANDMARKS...
14> INITIATING_SECONDARY_SCAN...
15> SAFETY_NET_MODULE: ENGAGED
16> FLAGGING_INCIDENTAL_FINDINGS...
17> COMPILING_REPORT_DRAFT...
18> READY_FOR_VALIDATION...
01> INITIALIZING_PIPELINE...
02> LOADING_MODEL_CONFIG...
03> PREPROCESSING_IMAGE_DATA...
04> NORMALIZING_PIXEL_VALUES...
05> DETECTING_ANATOMICAL_LANDMARKS...
06> ANALYZING_LUNG_FIELDS...
07> CHECKING_OSSIFICATION_CENTERS...
08> EVALUATING_THYMUS_REGION...
09> QUALITY_GATE_CHECK: PASSED
10> RUNNING_PRIMARY_INFERENCE...
11> SCANNING_FOR_PATHOLOGY...
12> GENERATING_STRUCTURED_FINDINGS...
13> CROSS_REFERENCING_LANDMARKS...
14> INITIATING_SECONDARY_SCAN...
15> SAFETY_NET_MODULE: ENGAGED
16> FLAGGING_INCIDENTAL_FINDINGS...
17> COMPILING_REPORT_DRAFT...
18> READY_FOR_VALIDATION...
Phase::Processing

The Engine

Data routed to Raylence servers for instant processing. Pediatric-specific foundation models analyze anatomical landmarks, detect pathologies, and draft reports in parallel.

Phase::Delivery

The Result

Structured report and safety findings delivered within seconds. Ready for radiologist validation and sign-off.

Raylence Workflow Interface
CONCEPT_VIEW
Raylence Interface
INTERFACE_CONCEPT
Purpose-Built

Why specifically
pediatric?

PROBLEM

Adult models interpret growth plates as fractures. They flag the thymus as a mass. They miss the subtle signs that only appear in developing bodies.

APPROACH

Raylence is designed exclusively for ages 0-17 to recognize ossification centers, normal thymus variants, and the developmental nuances that define pediatric radiology.

0-17

Target Age Range

Chest

Primary Modality

Multi

Modal Input

PACS

Integration

Questions

FAQ

Contact

Get In Touch.

Have any questions? Curious about our journey? Feel free to reach out and our team will get back to you shortly.