RadioLinc Agent¶

Overview¶

RadioLinc is BrainSAIT's AI agent specialized in diagnostic imaging analysis. It provides automated interpretation support for X-rays, CT scans, and other radiological studies to assist clinical workflows and support claims documentation.

Core Capabilities¶

1. Image Analysis¶

Supported Modalities: - X-Ray (CR/DR) - CT (Computed Tomography) - MRI (Magnetic Resonance Imaging) - Ultrasound - Mammography

Analysis Types: - Abnormality detection - Measurement extraction - Comparison studies - Quality assessment

2. Triage Scoring¶

Priority Classification: - Critical - Immediate attention - Urgent - Same-day review - Routine - Standard workflow

3. Report Generation¶

Output Components: - Findings description - Impression summary - Recommendations - Code suggestions

Architecture¶

graph TB
    A[DICOM Images] --> B[Image Preprocessor]
    B --> C[AI Analysis Engine]
    C --> D[Finding Detector]
    D --> E[Report Generator]

    F[Clinical Context] --> C
    G[Reference Database] --> D

    E --> H[Structured Report]
    E --> I[FHIR DiagnosticReport]

Clinical Use Cases¶

Emergency Triage¶

Scenario: Rapid assessment of ER imaging

Process: 1. Receive study from PACS 2. AI analysis within seconds 3. Critical findings alert 4. Priority assignment

Detectable Emergencies: - Pneumothorax - Pulmonary embolism - Intracranial hemorrhage - Fractures - Foreign bodies

Quality Assurance¶

Scenario: Second-read verification

Process: 1. Compare AI findings to radiologist report 2. Flag discrepancies 3. Track concordance rates 4. Quality metrics reporting

Claims Support¶

Scenario: Document imaging for claims

Process: 1. Extract relevant findings 2. Match to diagnosis codes 3. Support medical necessity 4. Generate structured data

Supported Findings¶

Chest X-Ray¶

CT Head¶

Musculoskeletal¶

Integration¶

PACS Integration¶

Standards: - DICOM receive (SCP) - DICOM send (SCU) - WADO-RS - DICOMweb

Workflow:

sequenceDiagram
    participant PACS
    participant RL as RadioLinc
    participant RIS

    PACS->>RL: Send study
    RL->>RL: Analyze
    RL->>PACS: Store results
    RL->>RIS: Update worklist

RIS Integration¶

• Worklist management
• Report distribution
• Status updates
• Priority alerts

API Endpoints¶

Analyze Study:

POST /api/radiolinc/analyze
{
  "study_uid": "1.2.3.4.5",
  "modality": "CR",
  "body_part": "CHEST",
  "priority": "STAT"
}

Get Results:

GET /api/radiolinc/results/{study_uid}

Output Formats¶

Structured Report¶

{
  "study_uid": "1.2.3.4.5",
  "modality": "CR",
  "body_part": "CHEST",
  "triage_score": "urgent",
  "findings": [
    {
      "type": "opacity",
      "location": "right lower lobe",
      "confidence": 0.94,
      "measurement": "3.2 cm",
      "impression": "Consolidation consistent with pneumonia"
    }
  ],
  "impression": "Right lower lobe pneumonia",
  "recommendations": [
    "Clinical correlation recommended",
    "Follow-up imaging in 4-6 weeks"
  ],
  "codes": {
    "icd10": ["J18.1"],
    "cpt": ["71046"]
  }
}

FHIR DiagnosticReport¶

{
  "resourceType": "DiagnosticReport",
  "status": "final",
  "code": {
    "coding": [{
      "system": "http://loinc.org",
      "code": "24634-0",
      "display": "XR Chest 2 Views"
    }]
  },
  "conclusion": "Right lower lobe pneumonia",
  "conclusionCode": [{
    "coding": [{
      "system": "http://hl7.org/fhir/sid/icd-10",
      "code": "J18.1"
    }]
  }]
}

Performance Metrics¶

Quality & Safety¶

Alert Management¶

Critical Alert Workflow: 1. AI detects critical finding 2. Immediate notification 3. Radiologist verification 4. Clinical team alert 5. Acknowledgment tracking

Audit Trail¶

• All analyses logged
• Findings documented
• Alerts tracked
• Outcomes recorded

Regulatory Compliance¶

• FDA 510(k) pathway
• SFDA registration
• CE marking
• Quality management system

Configuration¶

Modality Settings¶

modalities:
  CR:
    body_parts:
      - CHEST
      - ABDOMEN
      - EXTREMITY
    analysis_types:
      - abnormality_detection
      - measurement
    alert_conditions:
      - pneumothorax
      - fracture

Alert Thresholds¶

alerts:
  critical:
    confidence: 0.9
    notification: immediate
  urgent:
    confidence: 0.85
    notification: within_1_hour
  routine:
    confidence: 0.8
    notification: standard

Best Practices¶

Image Quality¶

1. Proper patient positioning
2. Adequate exposure
3. Complete anatomy coverage
4. Artifact minimization

Clinical Context¶

1. Provide relevant history
2. Include prior studies
3. Specify clinical question
4. Document comparison needs

Result Review¶

1. Verify AI findings
2. Consider clinical context
3. Apply clinical judgment
4. Document interpretation

Related Documents¶

• DocsLinc Agent
• ClaimLinc Agent
• Automation Pipeline
• FHIR R4 Profile

Last updated: January 2025