Azure Cognitive Services Health Insights Radiology Insights client library for Python - version 1.0.0

Artículo
09/12/2024

Health Insights is an Azure Applied AI Service built with the Azure Cognitive Services Framework, that leverages multiple Cognitive Services, Healthcare API services and other Azure resources.

Radiology Insights is a model that aims to provide quality checks as feedback on errors and inconsistencies (mismatches) and ensures critical findings are identified and communicated using the full context of the report. Follow-up recommendations and clinical findings with measurements (sizes) documented by the radiologist are also identified.

Getting started

Prequisites

Python 3.8+ is required to use this package.
You need an Azure subscription to use this package.
An existing Cognitive Services Health Insights instance.

For more information about creating the resource or how to get the location and sku information see here.

Installing the module

python -m pip install azure-healthinsights-radiologyinsights

This table shows the relationship between SDK versions and supported API versions of the service:

SDK version	Supported API version of service
1.0.0	2024-04-01

Authenticate the client

Get the endpoint

You can find the endpoint for your Health Insights service resource using the Azure Portal or Azure CLI

# Get the endpoint for the Health Insights service resource
az cognitiveservices account show --name "resource-name" --resource-group "resource-group-name" --query "properties.endpoint"

Create a RadiologyInsightsClient with DefaultAzureCredential

DefaultAzureCredential provides different ways to authenticate with the service. Documentation about this can be found here

import os
from azure.identity import DefaultAzureCredential
from azure.healthinsights.radiologyinsights import RadiologyInsightsClient

credential = DefaultAzureCredential()
ENDPOINT = os.environ["AZURE_HEALTH_INSIGHTS_ENDPOINT"]
radiology_insights_client = RadiologyInsightsClient(endpoint=ENDPOINT, credential=credential)

Long-Running Operations

Long-running operations are operations which consist of an initial request sent to the service to start an operation, followed by polling the service at intervals to determine whether the operation has completed or failed, and if it has succeeded, to get the result.

Methods that support healthcare analysis, custom text analysis, or multiple analyses are modeled as long-running operations. The client exposes a begin_<method-name> method that returns a poller object. Callers should wait for the operation to complete by calling result() on the poller object returned from the begin_<method-name> method. Sample code snippets are provided to illustrate using long-running operations below.

Key concepts

Once you've initialized a 'RadiologyInsightsClient', you can use it to analyse document text by displaying inferences found within the text.

Age Mismatch
Laterality Discrepancy
Sex Mismatch
Complete Order Discrepancy
Limited Order Discrepancy
Finding
Critical Result
Follow-up Recommendation
Communication
Radiology Procedure

Radiology Insights currently supports one document from one patient. Please take a look here for more detailed information about the inferences this service produces.

Examples

For each inference samples are available that show how to retrieve the information either in a synchronous (block until operation is complete, slower) or in an asynchronous way (non-blocking, faster). For an example how to create a client, a request and get the result see the example in the sample folder.

Age Mismatch
Complete Order Discrepancy
Critical Result
Finding
Follow-up Communication
Follow-up Recommendation
Laterality Discrepancy
Limited Order Discrepancy
Radiology Procedure
Sex Mismatch

Running the samples

Open a terminal window and cd to the directory that the samples are saved in.
Set the environment variables specified in the sample file you wish to run.
Run the sample. Example: python <sample_name>.py

Create a request for the RadiologyInsights service

doc_content1 = """CLINICAL HISTORY:   
        20-year-old female presenting with abdominal pain. Surgical history significant for appendectomy.
        COMPARISON:   
        Right upper quadrant sonographic performed 1 day prior.
        TECHNIQUE:   
        Transabdominal grayscale pelvic sonography with duplex color Doppler and spectral waveform analysis of the ovaries.
        FINDINGS:   
        The uterus is unremarkable given the transabdominal technique with endometrial echo complex within physiologic normal limits. The ovaries are symmetric in size, measuring 2.5 x 1.2 x 3.0 cm and the left measuring 2.8 x 1.5 x 1.9 cm.\n On duplex imaging, Doppler signal is symmetric.
        IMPRESSION:   
        1. Normal pelvic sonography. Findings of testicular torsion.
        A new US pelvis within the next 6 months is recommended.
        These results have been discussed with Dr. Jones at 3 PM on November 5 2020."""

        # Create ordered procedure
        procedure_coding = models.Coding(
            system="Http://hl7.org/fhir/ValueSet/cpt-all",
            code="USPELVIS",
            display="US PELVIS COMPLETE",
        )
        procedure_code = models.CodeableConcept(coding=[procedure_coding])
        ordered_procedure = models.OrderedProcedure(description="US PELVIS COMPLETE", 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))

Get Age Mismatch Inference information

for patient_result in radiology_insights_result.patient_results:
    for ri_inference in patient_result.inferences:
        if ri_inference.kind == models.RadiologyInsightsInferenceType.AGE_MISMATCH:
            print(f"Age Mismatch Inference found")

Get Complete Order Discrepancy Inference information

for patient_result in radiology_insights_result.patient_results:
            for ri_inference in patient_result.inferences:
                if ri_inference.kind == models.RadiologyInsightsInferenceType.COMPLETE_ORDER_DISCREPANCY:
                    print(f"Complete Order Discrepancy Inference found")

Get Critical Result Inference information

for patient_result in radiology_insights_result.patient_results:
    for ri_inference in patient_result.inferences:
        if ri_inference.kind == models.RadiologyInsightsInferenceType.CRITICAL_RESULT:
            critical_result = ri_inference.result
                print(
                f"Critical Result Inference found: {critical_result.description}")

Get Finding Inference information

for patient_result in radiology_insights_result.patient_results:
            counter = 0
            for ri_inference in patient_result.inferences:
                if ri_inference.kind == models.RadiologyInsightsInferenceType.FINDING:
                    counter += 1
                    print(f"Finding Inference found")

Get Follow-up Communication information

for patient_result in radiology_insights_result.patient_results:
    for ri_inference in patient_result.inferences:
        if ri_inference.kind == models.RadiologyInsightsInferenceType.FOLLOWUP_COMMUNICATION:
            print(f"Follow-up Communication Inference found")

Get Follow-up Recommendation information

for patient_result in radiology_insights_result.patient_results:
    for ri_inference in patient_result.inferences:
        if ri_inference.kind == models.RadiologyInsightsInferenceType.FOLLOWUP_RECOMMENDATION:
            print(f"Follow-up Recommendation Inference found")

Get Laterality Discrepancy information

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")

Get Limited Order Discrepancy information

for patient_result in radiology_insights_result.patient_results:
    for ri_inference in patient_result.inferences:
        if ri_inference.kind == models.RadiologyInsightsInferenceType.LIMITED_ORDER_DISCREPANCY:
            print(f"Limited Order Discrepancy Inference found")

Get Radiology Procedure information

for patient_result in radiology_insights_result.patient_results:
    for ri_inference in patient_result.inferences:
        if ri_inference.kind == models.RadiologyInsightsInferenceType.RADIOLOGY_PROCEDURE:
            print(f"Radiology Procedure Inference found")

Get Sex Mismatch information

for patient_result in radiology_insights_result.patient_results:
    for ri_inference in patient_result.inferences:
        if ri_inference.kind == models.RadiologyInsightsInferenceType.SEX_MISMATCH:
            print(f"Sex Mismatch Inference found")

For detailed conceptual information of this and other inferences please read more here.

Troubleshooting

General

Health Insights Radiology Insights client library will raise exceptions defined in Azure Core.

Logging

This library uses the standard logging library for logging.

Basic information about HTTP sessions (URLs, headers, etc.) is logged at INFO level.

Detailed DEBUG level logging, including request/response bodies and unredacted headers, can be enabled on the client or per-operation with the logging_enable keyword argument.

See full SDK logging documentation with examples here.

Next steps

Contributing

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.microsoft.com.

When you submit a pull request, a CLA-bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., label, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.

This project has adopted the Microsoft Open Source Code of Conduct. For more information, see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.

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