ai-detection-pipeline

name	ai-detection-pipeline
description	Integrate AI detection into PACS workflow. Also use when setting up, configuring, or optimizing AI detection systems for medical imaging. Also covers Aidoc, Nvidia Clara, Zebra Medical, MaxQ AI, and Qure AI integration.

AI Detection Pipeline

You are an expert in AI medical imaging detection pipelines. Your role is to help users integrate, configure, and optimize AI detection systems.

Supported AI Platforms

Platform	Focus Areas	Modality
Aidoc	Triage, hemorrhage, PE, C-spine	CT
Nvidia Clara	Multi-modal, general detection	CT, MRI, X-ray
Zebra Medical	Multi-finding, chest	X-ray, CT
MaxQ AI	Neuro, PE, chest	CT
Qure AI	Chest, head	X-ray, CT
Lunit	Chest, mammography	X-ray, MG
Riverain	Chest, lung nodules	X-ray

Pipeline Architecture

+-------------+     +-------------+     +-------------+     +-------------+
|    PACS     |---->|  AI Engine  |---->|   Results   |---->|  Worklist   |
|  (Source)   |     |  (Detect)   |     |   (Store)   |     |  (Alert)    |
+-------------+     +-------------+     +-------------+     +-------------+
      |                   |                   |                   |
      v                   v                   v                   v
  DICOM Send         Inference          Database             Notification
  C-STORE           GPU Compute         Results Store        Pager/Email

Aidoc Integration

API Configuration

import requests

AIDOC_API = "https://api.aidoc.com/v1"

def configure_aidoc(api_key):
    """Configure Aidoc API."""
    return {
        "base_url": AIDOC_API,
        "headers": {
            "Authorization": f"Bearer {api_key}",
            "Content-Type": "application/json"
        }
    }

def submit_study_aidoc(config, study_uid, study_url):
    """Submit study for Aidoc analysis."""
    response = requests.post(
        f"{config['base_url']}/studies",
        headers=config["headers"],
        json={
            "study_uid": study_uid,
            "study_dicom_url": study_url,
            "priority": "normal"
        }
    )
    return response.json()

Detection Types

AIDOC_DETECTIONS = {
    "ct_head": [
        "intracranial_hemorrhage",
        "mass_effect",
        "midline_shift",
        "fracture"
    ],
    "ct_chest": [
        "pulmonary_embolism",
        "pneumothorax",
        "cervical_spine_fracture"
    ],
    "ct_angiography": [
        "aortic_dissection",
        "pulmonary_embolism"
    ]
}

Retrieve Results

def get_aidoc_results(config, study_id):
    """Get AI detection results."""
    response = requests.get(
        f"{config['base_url']}/studies/{study_id}/results",
        headers=config["headers"]
    )
    return response.json()

# Response structure
{
    "study_id": "123",
    "status": "complete",
    "findings": [
        {
            "type": "intracranial_hemorrhage",
            "location": "right_temporal",
            "severity": "critical",
            "confidence": 0.95,
            "bounding_box": {"x": 100, "y": 200, "w": 50, "h": 60}
        }
    ],
    "triage_priority": "STAT"
}

Nvidia Clara Integration

Configuration

import requests

CLARA_API = "https://api.clara.nvidia.com/v1"

def configure_clara(api_key):
    """Configure Nvidia Clara."""
    return {
        "base_url": CLARA_API,
        "headers": {
            "Authorization": f"Bearer {api_key}",
            "NVIDIA-CLARA-Tenant-ID": "your-tenant"
        }
    }

def submit_clara_analysis(config, dicom_data, model="medical_imaging"):
    """Submit for Clara analysis."""
    response = requests.post(
        f"{config['base_url']}/infer/{model}",
        headers=config["headers"],
        data=dicom_data
    )
    return response.json()

Available Models

CLARA_MODELS = {
    "clara_organ_s segmentation": "Organ segmentation",
    "clara_lung_nodule": "Lung nodule detection",
    "clara_brain_tumor": "Brain tumor segmentation",
    "clara_carotid": "Carotid artery analysis"
}

Zebra Medical Integration

API Setup

ZEBRA_API = "https://api.zebra-med.com/v1"

def configure_zebra(api_key):
    """Configure Zebra Medical."""
    return {
        "base_url": ZEBRA_API,
        "api_key": api_key
    }

def analyze_chest_xray(config, dicom_url):
    """Analyze chest X-ray for multiple findings."""
    response = requests.post(
        f"{config['base_url']}/chestxray/analyze",
        headers={"Zebra-API-Key": config["api_key"]},
        json={"dicom_url": dicom_url}
    )
    return response.json()

# Available findings
ZEBRA_CHEST_FINDINGS = [
    "cardiomegaly", "lung_opacity", "pleural_effusion",
    "pneumothorax", "calcification", "pneumonia",
    "atelectasis", "lung_lesion", "fracture", "enlarged_cardiomediastinum"
]

MaxQ AI Integration

Stroke and PE Detection

MAXQ_API = "https://api.maxq.ai/v1"

def configure_maxq(api_key):
    """Configure MaxQ AI."""
    return {"base_url": MAXQ_API, "api_key": api_key}

def submit_ct_neuro(config, dicom_data):
    """Submit CT neuro for stroke detection."""
    response = requests.post(
        f"{config['base_url']}/neuro/ct",
        headers={"X-API-Key": config["api_key"]},
        data=dicom_data
    )
    return response.json()

Qure AI Integration

Chest X-ray Analysis

QURE_API = "https://api.qure.ai/v1"

def configure_qure(api_key):
    """Configure Qure AI."""
    return {"base_url": QURE_API, "api_key": api_key}

def analyze_cxr(config, dicom_url, type="comprehensive"):
    """Analyze chest X-ray."""
    response = requests.post(
        f"{config['base_url']}/cxr/analyze",
        headers={"Authorization": f"Bearer {config['api_key']}"},
        json={
            "dicom_url": dicom_url,
            "analysis_type": type
        }
    )
    return response.json()

# Analysis types
QURE_TYPES = ["tb_screening", "comprehensive", "chest_comprehensive"]

PACS Integration

DICOM Filtered SCU

def configure_pacs_filter(pacs_url, ae_title, ai_platform="aidoc"):
    """Configure PACS to filter studies for AI."""
    return {
        "pacs": {
            "url": pacs_url,
            "ae_title": ae_title,
            "modality": "CT"
        },
        "filter_criteria": {
            "Modality": "CT",
            "BodyPart": ["HEAD", "CHEST", "ABDOMEN"]
        },
        "forward_to": ai_platform,
        "receive_results": True
    }

Worklist Integration

def configure_worklist_alerts(config, alert_config):
    """Configure worklist priority alerts."""
    return {
        "worklist": config["pacs"],
        "alert_on": alert_config.get("critical_findings", True),
        "priority_override": alert_config.get("priority", "STAT"),
        "notification": {
            "method": alert_config.get("method", "worklist"),
            "integrate": alert_config.get("integrate_with", "pacs")
        }
    }

Critical Findings Alerting

Alert Configuration

def configure_alerts(config, alert_settings):
    """Configure critical findings alerts."""
    return {
        "findings": {
            "hemorrhage": {"priority": "STAT", "notify": True},
            "pulmonary_embolism": {"priority": "STAT", "notify": True},
            "pneumothorax": {"priority": "STAT", "notify": True},
            "aortic_dissection": {"priority": "STAT", "notify": True},
            "stroke": {"priority": "STAT", "notify": True}
        },
        "methods": {
            "email": alert_settings.get("email", True),
            "sms": alert_settings.get("sms", False),
            "pager": alert_settings.get("pager", False),
            "worklist": alert_settings.get("worklist", True)
        },
        "recipients": alert_settings.get("recipients", [])
    }

Batch Processing

Backlog Processing

def configure_batch_processing(config, batch_settings):
    """Configure batch processing for backlog."""
    return {
        "mode": "batch",
        "source": {
            "pacs": batch_settings.get("pacs_url"),
            "date_range": {
                "from": batch_settings.get("start_date"),
                "to": batch_settings.get("end_date")
            },
            "modality": batch_settings.get("modality", "CT")
        },
        "ai_platform": config["base_url"],
        "priority": "background",
        "results_storage": batch_settings.get("results_db")
    }

Performance Monitoring

Metrics to Track

DETECTION_METRICS = {
    "volume": ["studies_processed", "studies_per_day"],
    "timing": ["avg_processing_time", "p95_time"],
    "accuracy": ["sensitivity", "specificity", "ppv", "npv"],
    "workflow": ["alerts_sent", "alerts_responded", "time_to_read"]
}

Troubleshooting

Issue	Solution
No results received	Check PACS forwarding config
Slow processing	Check GPU availability
False positives high	Adjust confidence threshold
Integration failing	Verify DICOM connectivity

Related Skills

• pacs-workflow: For PACS integration details
• ai-quality-review: For AI output QA
• radiology-metrics: For performance monitoring
• hl7-fhir-radiology: For results notification

Examples

Example 1: Set Up Aidoc CT Head

Configure Aidoc for CT head hemorrhage detection with worklist alerts

config = configure_aidoc("your-api-key")
pacs_config = configure_pacs_filter("http://pacs:8042", "AIDOC")
alert_config = configure_alerts(config, {
    "email": True,
    "recipients": ["[e-mail]"]
})

Example 2: Batch Processing

Process backlog of 500 chest CT studies for PE detection

batch_config = configure_batch_processing(config, {
    "pacs_url": "http://pacs:8042",
    "start_date": "2026-01-01",
    "end_date": "2026-03-31",
    "modality": "CT",
    "results_db": "postgresql://ai-results/db"
})