Ai Detection Pipeline

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

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": ["[email protected]"]
})

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"
})