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# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
"""
FILE: sample_laterality_discrepancy_inference.py
DESCRIPTION:
The sample_laterality_discrepancy_inference.py module processes a sample radiology document with the Radiology Insights service.
It will initialize a RadiologyInsightsClient, build a Radiology Insights request with the sample document,
submit it to the client, RadiologyInsightsClient, and display the Laterality Mismatch indication and discrepancy type.
USAGE:
1. Set the environment variables with your own values before running the sample:
- AZURE_HEALTH_INSIGHTS_ENDPOINT - the endpoint to your source Health Insights resource.
- For more details how to use DefaultAzureCredential, please take a look at https://learn.microsoft.com/python/api/azure-identity/azure.identity.defaultazurecredential
2. python sample_laterality_discrepancy_inference.py
"""
import datetime
import os
import uuid
from azure.identity import DefaultAzureCredential
from azure.healthinsights.radiologyinsights import RadiologyInsightsClient
from azure.healthinsights.radiologyinsights import models
def radiology_insights_sync() -> None:
credential = DefaultAzureCredential()
ENDPOINT = os.environ["AZURE_HEALTH_INSIGHTS_ENDPOINT"]
job_id = str(uuid.uuid4())
radiology_insights_client = RadiologyInsightsClient(endpoint=ENDPOINT, credential=credential)
doc_content1 = """Exam: US LT BREAST TARGETED
Technique: Targeted imaging of the right breast is performed.
Findings:
Targeted imaging of the left breast is performed from the 6:00 to the 9:00 position.
At the 6:00 position, 5 cm from the nipple, there is a 3 x 2 x 4 mm minimally hypoechoic mass with a peripheral calcification.
This may correspond to the mammographic finding. No other cystic or solid masses visualized."""
# Create ordered procedure
procedure_coding = models.Coding(
system="Https://loinc.org",
code="26688-1",
display="US BREAST - LEFT LIMITED",
)
procedure_code = models.CodeableConcept(coding=[procedure_coding])
ordered_procedure = models.OrderedProcedure(description="US BREAST - LEFT LIMITED", code=procedure_code)
# Create encounter
start = datetime.datetime(2021, 8, 28, 0, 0, 0, 0)
end = datetime.datetime(2021, 8, 28, 0, 0, 0, 0)
encounter = models.PatientEncounter(
id="encounter2",
class_property=models.EncounterClass.IN_PATIENT,
period=models.TimePeriod(start=start, end=end),
)
# Create patient info
birth_date = datetime.date(1959, 11, 11)
patient_info = models.PatientDetails(sex=models.PatientSex.FEMALE, birth_date=birth_date)
# Create author
author = models.DocumentAuthor(id="author2", full_name="authorName2")
create_date_time = datetime.datetime(2024, 2, 19, 0, 0, 0, 0, tzinfo=datetime.timezone.utc)
patient_document1 = models.PatientDocument(
type=models.DocumentType.NOTE,
clinical_type=models.ClinicalDocumentType.RADIOLOGY_REPORT,
id="doc2",
content=models.DocumentContent(source_type=models.DocumentContentSourceType.INLINE, value=doc_content1),
created_at=create_date_time,
specialty_type=models.SpecialtyType.RADIOLOGY,
administrative_metadata=models.DocumentAdministrativeMetadata(
ordered_procedures=[ordered_procedure], encounter_id="encounter2"
),
authors=[author],
language="en",
)
# Construct patient
patient1 = models.PatientRecord(
id="patient_id2",
details=patient_info,
encounters=[encounter],
patient_documents=[patient_document1],
)
# Create a configuration
configuration = models.RadiologyInsightsModelConfiguration(verbose=False, include_evidence=True, locale="en-US")
# Construct the request with the patient and configuration
patient_data = models.RadiologyInsightsJob(
job_data=models.RadiologyInsightsData(patients=[patient1], configuration=configuration)
)
# Health Insights Radiology Insights
try:
poller = radiology_insights_client.begin_infer_radiology_insights(
id=job_id,
resource=patient_data,
)
radiology_insights_result = poller.result()
display_laterality_discrepancy(radiology_insights_result)
except Exception as ex:
raise ex
def display_laterality_discrepancy(radiology_insights_result):
for patient_result in radiology_insights_result.patient_results:
for ri_inference in patient_result.inferences:
if ri_inference.kind == models.RadiologyInsightsInferenceType.LATERALITY_DISCREPANCY:
print(f"Laterality Discrepancy Inference found")
indication = ri_inference.laterality_indication
for code in indication.coding:
print(f"Laterality Discrepancy: Laterality Indication: {code.system} {code.code} {code.display}")
print(f"Laterality Discrepancy: Discrepancy Type: {ri_inference.discrepancy_type}")
if __name__ == "__main__":
radiology_insights_sync()
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