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    PHI leakage detection for DICOM

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    Deployed on AWS
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    This pipeline detects and flags PHI entities within both the metadata and visual content of DICOM files, supporting robust compliance monitoring and streamlined violation reporting.

    Overview

    This pipeline detects and flags PHI entities within both the metadata and visual content of DICOM files, supporting robust compliance monitoring and streamlined violation reporting.

    Its primary purpose is to generate a comprehensive report detailing any PHI entities found - whether embedded in the image or present in metadata - within DICOM files that were previously subjected to de-identification.

    A potential use case for this pipeline:

    A healthcare organization receives large batches of DICOM files from third-party providers or external imaging centers. These files are expected to be de-identified according to HIPAA or other regulatory standards. However, due to inconsistencies in de-identification workflows, some files still contain Protected Health Information (PHI) - either embedded in image overlays, burned-in text, or DICOM metadata fields (e.g., patient name, institution, accession number).

    The organization uses the PHI detection pipeline to automatically scan both the pixel data and metadata, flagging any detected PHI entities. The output is a compliance report that summarizes: which files contain PHI, the type of PHI (e.g., names, IDs, dates).


    IMPORTANT USAGE INFORMATION:

    After subscribing to this product and creating a SageMaker endpoint, billing occurs on an HOURLY BASIS for as long as the endpoint is running.

    -Charges apply even if the endpoint is idle and not actively processing requests.

    -To stop charges, you MUST DELETE the endpoint in your SageMaker console.

    -Simply stopping requests will NOT stop billing.

    This ensures you are only billed for the time you actively use the service.

    Highlights

    • The pipeline cross-references DICOM metadata entities with text detected in the corresponding DICOM images. If information such as patient names, physician names, or dates found in the metadata also appears in the image text, it will be automatically identified and de-identified - even if it was not part of the initial entity recognition list
    • De-identified entities: NAME,AGE,CONTACT,LOCATION,PROFESSION,PERSON,DATE,ID,DOCTOR

    Details

    Delivery method

    Latest version

    Deployed on AWS

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    Try this product free for 15 days according to the free trial terms set by the vendor.

    PHI leakage detection for DICOM

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    Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
    Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator  to estimate your infrastructure costs.

    Usage costs (2)

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    Dimension
    Description
    Cost/host/hour
    ml.m4.2xlarge Inference (Batch)
    Recommended
    Model inference on the ml.m4.2xlarge instance type, batch mode
    $47.52
    ml.m4.2xlarge Inference (Real-Time)
    Recommended
    Model inference on the ml.m4.2xlarge instance type, real-time mode
    $47.52

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    No refunds are possible.

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    Usage information

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    Delivery details

    Amazon SageMaker model

    An Amazon SageMaker model package is a pre-trained machine learning model ready to use without additional training. Use the model package to create a model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.

    Deploy the model on Amazon SageMaker AI using the following options:
    Deploy the model as an API endpoint for your applications. When you send data to the endpoint, SageMaker processes it and returns results by API response. The endpoint runs continuously until you delete it. You're billed for software and SageMaker infrastructure costs while the endpoint runs. AWS Marketplace models don't support Amazon SageMaker Asynchronous Inference. For more information, see Deploy models for real-time inference  .
    Deploy the model to process batches of data stored in Amazon Simple Storage Service (Amazon S3). SageMaker runs the job, processes your data, and returns results to Amazon S3. When complete, SageMaker stops the model. You're billed for software and SageMaker infrastructure costs only during the batch job. Duration depends on your model, instance type, and dataset size. AWS Marketplace models don't support Amazon SageMaker Asynchronous Inference. For more information, see Batch transform for inference with Amazon SageMaker AI  .
    Version release notes

    Spark-OCR==6.0.0 Spark-Healthcare==6.0.2 Spark-NLP==6.0.1

    Additional details

    Inputs

    Summary

    Supported Dicom input format.

    Input MIME type
    application/octet-stream
    https://github.com/JohnSnowLabs/spark-nlp-workshop/tree/master/products/sagemaker/models/dicom_deid_pixels_platform_en/inputs/real-time
    https://github.com/JohnSnowLabs/spark-nlp-workshop/tree/master/products/sagemaker/models/dicom_deid_pixels_platform_en/inputs/batch

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    For any assistance, please reach out to support@johnsnowlabs.com .

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