See-Mode Technologies

See-Mode Improves Stroke Detection and Prevention with Machine Learning on AWS

2021

A World without Strokes

Dr. Milad Mohammadzadeh and Dr. Sadaf Monajemi were doctoral students in Singapore when they discovered they could make a significant impact in the healthcare industry. Mohammadzadeh was researching the application of fluid dynamics in solving medical problems, and Monajemi was studying how to apply artificial intelligence (AI) and machine learning (ML) to medical imaging. After completing their PhDs, they learned through discussions with clinicians that the current methods of predicting and preventing strokes based on medical images weren’t particularly effective.

After completing their degrees, the duo started See-Mode in 2017. Their mission: a world without strokes. The MedTech company uses AI-based medical software to improve the analysis of medical images such as ultrasounds, CT, or MRI scans to streamline the image analysis workflow and ultimately predict critical risk factors of stroke. Its first product, Augmented Vascular Analysis (AVA), is a medical AI software for automated analysis and reporting of vascular ultrasound scans. Aside from Australia, See-Mode’s AVA is regulatory approved within Europe, United States of America (US), and Singapore. The startup is working with hospitals and imaging centers such as the Royal Melbourne Hospital in Australia, National University Hospital in Singapore, and the University of Washington in the US.

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An issue we wanted to solve was how we could analyze 50–100 images that would come in continuously, versus a single image from each patient at any given time. With AVA running on AWS, we can analyze that load within seconds and return immediate results to the user.”

Dr. Milad Mohammadzadeh
Cofounder and Director, See-Mode

Training ML Models on an End-to-End Cloud Platform

See-Mode decided to launch AVA on the Amazon Web Services (AWS) Cloud, choosing AWS for its broad set of capabilities and the ability to manage its ML workflows on an end-to-end platform.

“Having a centralized location for billing, security, compliance, and a scalable infrastructure for building and deploying ML models means we can spend more time focusing on our core mission of improving stroke prediction,” says Mohammadzadeh, cofounder and director of See-Mode.

In particular, See-Mode takes advantage of Amazon SageMaker to train the ML models that underpin AVA. It runs the models on ultrasound images that clinics provide and produces a detailed report identifying the locations of these dangerous plaques. Improving the speed and accuracy of stroke detection helps clinicians offer better treatment plans for their patients.

Process 50–100 Ultrasound Images Within Seconds

As a fully managed service, Amazon SageMaker enables See-Mode to easily deploy the ML models it builds in-house and scales them automatically. When the company has a peak in load, it rapidly deploys more ML models while maintaining high performance. With these highly available, quick-scaling models, AVA software can process a large amount of ultrasound images and generate an accurate report in under a minute.

“An issue we wanted to solve was how we could analyze 50–100 images that would come in continuously, versus a single image from each patient at any given time. With AVA running on AWS, we can analyze that load within seconds and return immediate results to the user. This enables clinicians to save time on reporting their medical studies and spend that time with patients who need them. Using AWS has been a major component to solving that issue,” Mohammadzadeh says.

Deploy Double-Digit ML Models at Low Cost

In addition to scalability, See-Mode required a highly elastic infrastructure to meet AVA’s large and complex requirements. “We have double-digit ML models deployed in production every day, with rapid spikes in traffic. Our underlying infrastructure has to be dynamic,” says Oliver Howden, technical lead at See-Mode. To meet this requirement, See-Mode uses Amazon Elastic Compute Cloud (Amazon EC2) instances coupled with AWS Inferentia to accelerate deep learning workloads at a low cost.

“With Amazon EC2 and AWS Inferentia, we can quickly adjust compute requirements accordingly and pay only for what we use instead of overprovisioning for hardware upfront, which is costly and time-consuming,” Howden adds.

See-Mode’s technology foundation on AWS also provides the startup with high uptime. This is a particularly important requirement for the company’s global healthcare customers, where obtaining timely information is critical.

Serving a Global Customer Base

To serve markets beyond Australia, See-Mode leverages multiple AWS Regions and Availability Zones. This enables the startup to connect from its offices in Melbourne to a customer database in a different location. Clinicians can then receive and perform analyses on ultrasound images taken only seconds earlier. Images can also stay inside sovereign geographies using AWS Local Zones to comply with regional privacy regulations.

Data from the different hospitals and imaging centers are transferred to See-Mode’s servers in batches. With the AWS Cloud, See-Mode can perform batch processing tasks using its own ML models and, at times, complements them with other AWS services such as Amazon Rekognition.

In dealing with multiple medical and imaging centers, See-Mode realized that the ultrasound images from its healthcare partners were of varying quality. See-Mode uses Amazon SageMaker to incorporate this diversity into the datasets it uses to train and continually improve the AVA software.

Platform Supports Compliance and Security

And, with an international customer base, See-Mode must adhere to the standards set by regulatory agencies around the globe. “We take advantage of the comprehensive AWS compliance controls, including support for HIPAA and GDPR,” says Howden. “For example, patient data must be anonymized before leaving Australia, and AWS enables us to easily adhere to this requirement. Furthermore, with the centralized platform that AWS provides, satisfying compliance requirements is straightforward. This is great for us and reassuring to our customers.”

The company uses Amazon GuardDuty to deliver continuous monitoring and detect threats by logging each time the server is accessed. It also relies on AWS CloudTrail to provide event history, which See-Mode uses for auditing purposes.

Continuing to Develop with ML on AWS

Following the success of AVA, See-Mode is exploring ways to innovate further with ML. Currently under research and development, See-Mode is validating its plaque composition and blood flow analysis to improve the prediction and prevention of strokes. “We have an ambitious mission and a team set to deliver on that,” says Mohammadzadeh. “And we’re just getting started.”

To Learn More

 To learn more, visit aws.amazon.com/machine-learning.


About See-Mode

Founded in 2017, See-Mode is a rapidly growing medical technology startup based in Melbourne, Australia. It uses artificial intelligence and machine learning to build tools that streamline the medical imaging workflow and enable the detection and prevention of strokes.

Benefits of AWS

  • Generates ultrasound reports in under a minute
  • Processes 50–100 ultrasound images within seconds
  • Meets global compliance and security requirements
  • Deploys double-digit ML models at low cost
  • Provides ease of management with centralized platform  

AWS Services Used

Amazon SageMaker

Amazon SageMaker helps data scientists and developers to prepare, build, train, and deploy high-quality machine learning (ML) models quickly by bringing together a broad set of capabilities purpose-built for ML.

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AWS CloudTrail

AWS CloudTrail monitors and records account activity across your AWS infrastructure, giving you control over storage, analysis, and remediation actions.

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Amazon GuardDuty

Amazon GuardDuty is a threat detection service that continuously monitors your AWS accounts and workloads for malicious activity and delivers detailed security findings for visibility and remediation.

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AWS Inferentia

AWS’s vision is to make deep learning pervasive for everyday developers and to democratize access to cutting edge infrastructure made available in a low-cost pay-as-you-go usage model.

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