Improving medical imaging workflows with AWS HealthImaging and SageMaker
AWS Architecture BlogImproving medical imaging workflows with AWS HealthImaging and SageMakerby Sukhomoy Basak and Hassan Mousaid | on31 JUL 2023| inAmazon SageMaker,Amazon Simple Queue Service (SQS),Amazon Simple Storage Service (S3),Announcements,Artificial Intelligence|Permalink|Comments|ShareMedical imaging plays a critical role in patient diagnosis and treatment planning in healthcare. However, healthcare providers face several challenges when it comes to managing, storing, and analyzing medical images. The process can be time-consuming, error-prone, and costly.There’s also a radiologist shortage across regions and healthcare systems, making the demand for this specialty increases due to an aging population, advances in imaging technology, and the growing importance of diagnostic imaging in healthcare.As the demand for imaging studies continues to rise, the limited number of available radiologists results in delays in available appointments and timely diagnoses. And while technology enables healthcare delivery improvements for clinicians and patients, hospitals seek additional tools to solve their most pressing challenges, including:Professional burnout due to an increasing demand for imaging and diagnostic servicesLabor-intensive tasks, such as volume measurement or structural segmentation of imagesIncreasing expectations from patients expecting high-quality healthcare experiences that match retail and technology in terms of convenience, ease, and personalizationTo improve clinician and patient experiences, run your picture archiving and communication system (PACS) with an artificial intelligence (AI)-enabled diagnostic imaging cloud solution to securely gain critical insights and improve access to care.AI helps reduce the radiologist burndown rate through automation. For example, AI saves radiologist chest x-ray interpretation time. It is also a powerful tool to identify areas that need closer inspection, and helps capture secondary findings that weren’t initially identified. The advancement of interoperability and analytics gives radiologist a 360-degree, longitudinal view of patient health records to provide better healthcare at potentially lower costs.AWS offers services to address these challenges. This blog post discussesAWS HealthImaging(AWS AHI) andAmazon SageMaker, and how they are used together to improve healthcare providers’ medical imaging workflows. This ultimately accelerates imaging diagnostics and increases radiology productivity. AWS AHI enables developers to deliver performance, security, and scale to cloud-native medical imaging applications. It allows ingestion of Digital Imaging and Communication in Medicine (DICOM) images. Amazon SageMaker provides end-to-end solution for AI and machine learning.Let’s explore an example use case involving X-rays after an auto accident. In this diagnostic medical imaging workflow, a patient is in the emergency room. From there:The patient undergoes an X-ray to check for fractures.The scanned acquisition device images flow to the PACS system.The radiologist reviews the information gathered from this procedure and authors the report.The patient workflow continues as the reports are made available to the referring physician.Next-generation imaging solutions and workflowsHealthcare providers can use AWS AHI and Amazon SageMaker together to enable next-generation imaging solutions and improve medical imaging workflows. The following architecture illustrates this example.Figure 1: X-ray images are sent to AWS HealthImaging and an Amazon SageMaker endpoint extracts insights.Let’s review the architecture and the key components:1. Imaging Scanner: Captures the images from a patient’s body. Depending on the modality, this can be an X-ray detector; a series of detectors in a CT scanner; a magnetic field and radio frequency coils in an MRI scanner; or an ultrasound transducer. This example uses an X-ray device.AWS IoT Greengrass: Edge runtime and cloud service configured withDICOM C-Store SCPthat receives the images and sends it toAmazon Simple Storage Service(Amazon S3). The images along with the related metadata are sent to Amazon S3 andAmazon Simple Queue Service(Amazon SQS) respectively, that triggers the workflow.2. Amazon SQS message queue: Consumes event from S3 bucket and triggers anAWS Step Functionsworkflow orchestration.3. AWS Step Functions runs the transform and import jobs to further process and import the images into AWS AHI data store instance.4. The final diagnostic image—along with any relevant patient information and metadata—is stored in the AWS AHI datastore. This allows for efficient imaging date retrieval and management. It also enables medical imaging data access with sub-second image retrieval latencies at scale, powered by cloud-native APIs and applications from AWS partners.5. Radiologists responsible for ground truth for ML images perform medical image annotations usingAmazon SageMaker Ground Truth. They visualize and label DICOM images using a custom data labeling workflow—a fully managed data labeling service that supports built-in or custom data labeling workflows. They also leverage tools like3D Slicerfor interactive medical image annotations.6. Data scientists build or leverage built-in deep learning models using the annotated images on Amazon SageMaker. SageMaker offers a range of deployment options that vary from low latency and high throughput to long-running inference jobs. These options include considerations for batch, real-time, ornear real-time inference.7. Healthcare providers use AWS AHI and Amazon SageMaker to run AI-assisted detection and interpretation workflow. This workflow is used to identify hard-to-see fractures, dislocations, or soft tissue injuries to allow surgeons and radiologist to be more confident in their treatment choices.8. Finally, the image stored in AWS AHI is displayed on a monitor or other visual output device where it can be analyzed and interpreted by a radiologist or other medical professional.TheOpen Health Imaging Foundation(OHIF) Viewer is an open source, web-based, medical imaging platform. It provides a core framework for building complex imaging applications.Radical ImagingorArterysare AWS partners that provide OHIF-based medical imaging viewer.Each of these components plays a critical role in the overall performance and accuracy of the medical imaging system as well as ongoing research and development focused on improving diagnostic outcomes and patient care. AWS AHI uses efficient metadata encoding, lossless compression, and progressive resolution data access to provide industry leading performance for loading images. Efficient metadata encoding enables image viewers and AI algorithms to understand the contents of a DICOM study without having to load the image data.SecurityThe AWS shared responsibility model applies to data protection in AWS AHI and Amazon SageMaker.Amazon SageMaker is HIPAA-eligible and can operate with data containing Protected Health Information (PHI). Encryption of data in transit is provided by SSL/TLS and is used when communicating both with the front-end interface of Amazon SageMaker (to the Notebook) and whenever Amazon SageMaker interacts with any other AWS services.AWS AHI is also HIPAA-eligible service and provides access control at the metadata level, ensuring that each user and application can only see the images and metadata fields that are required based upon their role. This prevents the proliferation of Patient PHI. All access to AWS AHI APIs is logged in detail inAWS CloudTrail.Both of these services leverageAWS Key Management service(AWS KMS) to satisfy the requirement that PHI data is encrypted at rest.ConclusionIn this post, we reviewed a common use case for early detection and treatment of conditions, resulting in better patient outcomes. We also covered an architecture that can transform the radiology field by leveraging the power of technology to improve accuracy, efficiency, and accessibility of medical imaging.Further readingAWS HealthImaging PartnersAWS features AWS HealthImaging at RSNA22Annotate DICOM images and build an ML model using the MONAI framework on Amazon SageMakerMLOps deployment best practices for real-time inference model serving endpoints with Amazon SageMakerTAGS:artificial intelligence,healthcareSukhomoy BasakSukhomoy Basak is a Solutions Architect at Amazon Web Services, with a passion for Data and Analytics solutions. Sukhomoy works with enterprise customers to help them architect, build, and scale applications to achieve their business outcomes.Hassan MousaidHassan Mousaid, PhD is a Principal Solutions Architect at Amazon Web Services supporting Healthcare and Life Sciences (HCLS) customers to accelerate the process of bringing ideas to market using Amazon's mechanisms for innovation. He has 20 years experience in enterprise and cloud transformation, healthcare IT, and medical imaging.CommentsView Comments
Medical imaging plays a critical role in patient diagnosis and treatment planning in healthcare. However, healthcare providers face several challenges when it comes to managing, storing, and analyzing medical images. The process can be time-consuming, error-prone, and costly. There’s also a radiologist shortage across regions and healthcare systems, making the demand for this specialty increases […]
Sukhomoy Basak
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