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": ["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"
})
aizech