| 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()
{
"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()
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()
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": ["radiologist@hospital.com"]
})
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"
})
