Accelerating Clinical Imaging Intelligence with HIPAA-Compliant AI Solutions

Client Overview

A clinical imaging intelligence company is accelerating the approval and adoption of life-saving therapies to patients through an AI-powered digital biomarker solution. With proprietary imaging biomarkers for precise quantitative endpoints and a trial imaging management solution, the company's solution is helping biopharma sponsors save millions in drug development costs.

Challenge

The client needed to enhance their platform with a robust solution for de-identifying protected health information (PHI) from Computed Tomography (CT) scans to comply with HIPAA safe harbor rules. While they had an existing process for metadata de-identification, they lacked a solution for removing sensitive information embedded within medical images themselves.

Additionally, the client wanted to establish foundational MLOps infrastructure to enable their data scientists to effectively develop, deploy, and manage machine learning models at scale for their life sciences applications.

Solution

Caylent designed and implemented a comprehensive AWS-based solution with two main components:

1. HIPAA-Compliant Medical Image De-identification

A scalable workflow was created to process clinical imaging data while ensuring compliance with HIPAA safe harbor rules:

• Automated De-identification Pipeline: Using AWS Step Functions to orchestrate the end-to-end process
• Advanced Image Analysis: Leveraging Amazon Rekognition and Amazon Textract for text detection in medical images
• Medical-Specific PHI Recognition: Utilizing Amazon Comprehend Medical to identify and redact sensitive healthcare information
• Secure Processing: AWS Lambda functions to reassemble de-identified components
• Compliant Storage: Storing de-identified DICOM files in secure Amazon S3 buckets

2. Life Sciences MLOps Infrastructure

To support the client's machine learning initiatives in clinical imaging analysis:

SageMaker Studio Environment: Configured with Jupyter notebooks, EFS volumes, and security policies

End-to-End MLOps Pipelines:

• Continuous integration workflow
• Model training workflow
• Model registry for versioning
• Continuous delivery workflow with approval gates
• Monitoring workflow with drift and bias detection

Containerization: SageMaker-native containerization for Python models

Governance: Resource tagging, AWS CloudWatch, AWS EventBridge, and AWS SageMaker Governance

Results

The implementation delivered significant benefits to the client's life sciences operations:

1. Enhanced HIPAA Compliance: Automated de-identification of both metadata and images in clinical CT scans, ensuring full compliance with HIPAA safe harbor rules
2. Accelerated Biomarker Development: The MLOps infrastructure enabled data scientists to develop and deploy clinical imaging biomarkers more efficiently
3. Improved Digital Biobank: Enhanced their highly curated, structured repository of imaging and clinical data with properly de-identified content
4. Streamlined Clinical Trial Imaging: More efficient processing of trial imaging data while maintaining regulatory compliance
5. Foundation for AI Innovation: Established infrastructure for future machine learning initiatives in clinical imaging analysis
6. Scalable Solution: Architecture designed to handle growing volumes of clinical imaging data with AWS managed services

The solution specifically addressed the unique requirements of life sciences data processing, including handling single-channel grayscale medical images with intensity values of 0-10,000, secondary captures, videos, and annotations - all while maintaining strict compliance with healthcare data privacy regulations.

By implementing this solution, the client strengthened their position as a leader in clinical imaging intelligence, further enhancing their ability to accelerate the development and approval of life-saving therapies.