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Case Studies

Accelerating Diagnosis to Treatment with Encord

September 18, 2023
5 mins
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Problem

Viz.ai, a health tech company specializing in AI-powered early disease detection, faced challenges with manual and time-consuming annotation processes for medical data. Their clinical AI teams relied on an in-house platform lacking automated features and streamlined collaboration tools, hindering efficiency and scalability.

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Key Result

By adopting Encord's annotation platform, Viz.ai experienced significant improvements in workflow efficiency and data accuracy. Encord's responsive support team, robust annotation features, and user-friendly interface facilitated a smooth integration and empowered teams to annotate medical data more speedily and accurately. The interpolation tool, in particular, proved to be a significant time saver, reducing labeling time and improving precision. As a result, Viz.ai's clinical AI teams could expedite processes, review medical imaging more quickly, and achieve more effective communication and project management.

Viz.ai’s revolutionary technology empowers medical teams to save lives through early detection and care coordination. The award-winning health tech company leverages Encord to accelerate processes, improve accuracy of diagnosis, and streamline collaboration among clinical AI teams. Encord’s medical training data platform and services have leveled up Viz.ai’s capabilities. They are now able to analyze data quickly and enter new areas of medical research.

Customer

Viz.ai uses artificial intelligence to speed up care and improve patient outcomes. The San Francisco-based health tech company uses its software to ingest patient data and images in real-time and use regulatory-approved AI algorithms to detect disease and notify the appropriate medical team. This allows early detection of large vessel occlusion strokes, aneurysms, pulmonary embolism, and other critical diseases. It alerts doctors and nurses, who can see the images on their phones, saving hours of the time it would otherwise take to get that patient to diagnosis and treatment.

Their slogan “time is brain” speaks to the heart of their mission. Every minute that passes during a stroke, 2 billion brain cells die, so early detection and accurate diagnosis are crucial.

Viz.ai’s success in aiding medical teams to rapidly detect diseases has allowed them to help enhance and preserve the quality of patients’ lives. And, with more success, comes more responsibility. The healthcare tech company has been rapidly growing and is constantly developing more solutions to support clinical teams worldwide. Viz.ai is now expanding into cardiovascular diseases with a recent approval by the FDA of AI-powered software for hypertrophic cardiomyopathy, an often missed but potentially deadly condition.

Problem

Early detection of a stroke, for instance, is possible thanks to Viz.ai’s abundance of data. By accessing and comparing scans with their multiple medical databases, the accuracy of their products is top of the range. However, for their teams, this has also translated into a lot of manpower and time consumed.

Previously, the clinical AI teams at Viz.ai relied on an in-house annotation platform to analyze and label medical data obtained from outside resources. “However, it lacked several features to enhance and streamline team collaboration,” explains Maayan Gerbi, Clicinal AI Specialist. “We desired more automated annotation features  to track progress amongst teams and lower  maintenance.”

It was time to search for a new solution that could support Viz.ai’s ambitious and fast-growing teams. 

Solution

After considering multiple annotation solutions, it was clear that Encord was the best fit for their needs. What stood out for Viz.ai was Encord’s highly responsive team, top-notch annotation features, as well as the user-friendly interface of the platform.

Encord’s robust support system has been remarkable. Whenever questions or issues come up, they are always supportive and helpful. This ensures that our workflows remain uninterrupted,” said Maayan. Encord’s helpful and reliable team also facilitated a smooth integration. “The annotation platform is well designed for compatibility and interpretability. We were able to effectively align it with the current systems,” added Data Manager Sarit Meshesha.

From project management, tracking, and monitoring to organizing large volumes of data in a user-friendly manner, Encord’s capabilities empower teams to annotate more speedily and accurately. Viz.ai particularly highlights the interpolation tool. “Encord constantly impresses us with the ability to think outside the box,” says Maayan. “The interpolation tool is a sophisticated, semi-automatic labeling tool that has proven to be a significant time saver for our team by reducing labeling time and improving precision.”

Results

Encord is now an integral part of Viz.ai’s workflow. The platform’s range of innovative features has allowed teams to take on more projects while staying organized. Additionally, the accuracy of the automatic and semi-automatic annotation features means less manual work and higher-quality data. The clinical AI teams can now expedite processes and review medical imaging more quickly.

The collaborative features have also proven beneficial. Viz.ai now experiences more effective communication amongst teams and better project management end-to-end. Maayan explains, “The division of tasks and clear role assignment ensures organized workflows and the ability for team members to consult certain cases with experts along the way.”

Thanks to the benefits that Viz.ai has witnessed with Encord, they now envision the platform as key to their growth and ever-challenging needs. “We plan on leveraging Encord to expand the range of use cases and include more medical conditions in our solutions… We are excited about our potential with Encord and look forward to seeing how we will evolve together in the future.”

Think Encord could be a good fit for your team as well?

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Frequently asked questions
  • Yes. In addition to being able to train models & run inference using our platform, you can either import model predictions via our APIs & Python SDK, integrate your model in the Encord annotation interface if it is deployed via API, or upload your own model weights.

  • At Encord, we take our security commitments very seriously. When working with us and using our services, you can ensure your and your customer's data is safe and secure. You always own labels, data & models, and Encord never shares any of your data with any third party. Encord is hosted securely on the Google Cloud Platform (GCP). Encord native integrations with private cloud buckets, ensuring that data never has to leave your own storage facility.

    Any data passing through the Encord platform is encrypted both in-transit using TLS and at rest.

    Encord is HIPAA&GDPR compliant, and maintains SOC2 Type II certification. Learn more about data security at Encord here.

  • Yes. If you believe you’ve discovered a bug in Encord’s security, please get in touch at security@encord.com. Our security team promptly investigates all reported issues. Learn more about data security at Encord here.

  • Yes - we offer managed on-demand premium labeling-as-a-service designed to meet your specific business objectives and offer our expert support to help you meet your goals. Our active learning platform and suite of tools are designed to automate the annotation process and maximise the ROI of each human input. The purpose of our software is to help you label less data.

  • The best way to spend less on labeling is using purpose-built annotation software, automation features, and active learning techniques. Encord's platform provides several automation techniques, including model-assisted labeling & auto-segmentation. High-complexity use cases have seen 60-80% reduction in labeling costs.

  • Encord offers three different support plans: standard, premium, and enterprise support. Note that custom service agreements and uptime SLAs require an enterprise support plan. Learn more about our support plans here.

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