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.”

Surgical Data Science Collective (SDSC) is a data platform that provides surgeons with access to data and quantitative insights about procedures to expedite the training process and democratize access to safe surgery. Working with Encord, SDSC has increased the speed of annotations by 10x while simultaneously improving precision and accuracy. Customer Meet SDSC SDSC is a non-profit organization dedicated to transforming surgery from an art to a science. With five core products, SDSC provides essential metrics on various procedures once videos are uploaded to the platform. For instance, the Kinematics model captures the movement of specific tools during a surgical procedure. As Director of Machine Learning (ML), Margaux Masson-Forsythe is responsible for leading the ML roadmap at SDSC, defining the strategy of generating high-quality training data, managing the data pipeline, and overseeing the ML team. Problem A vast amount of video data requires technical knowledge for annotation and a need to connect their training data platform to a wider pipeline. Before switching to Encord, the SDSC team faced three common problems: quantity of data, poor quality of annotations, and a lack of customizability and integrations. Firstly, they faced a challenge in dealing with the vast amount of video data that required annotation. With each procedure split into 20 clips and each clip lasting approximately 15 minutes, the team had several Terabytes of data to annotate. Their previous tool suffered from a lot of latency issues when rendering videos, which hindered the labelers’ ability to effectively annotate at speed. Secondly, the team discovered that around 20% of the annotations they had previously conducted were incorrect, with most of these coming in the form of inconsistent naming conventions on the same objects. Using Encord Active’s automated label error detection feature, the team could identify these errors that they attributed to: i) the absence of a robust annotation toolkit and ii) the requirement for a high level of technical knowledge and expertise to conduct and review annotations. Lastly, the team had difficulty programmatically interacting with their training data platform and integrating it into their wider model production pipeline. They needed a working and usable Python SDK to create automated training data pipelines. Solution Leveraging Encord’s comprehensive Training Data Platform to conduct video annotations with state-of-the-art tools and unparalleled support. After reviewing several solutions, the SDSC team chose to integrate Encord into their data pipelines. On the onboarding process, Margaux noted “Getting started with Encord and integrating it into our workflow was really fast. The thing that I find the most valuable is the flexibility of how we can integrate the Encord pipeline into our own pipeline, we use the Python SDK a lot”. By natively rendering videos in the Encord platform, SDSC’s team was able to speed up annotation while increasing precision. Margaux praised the platform's support for video annotation, noting “How smooth [Encord Annotate] was and all of the different tools that come with labeling videos” and that “[Encord] definitely was the best platform we’ve seen and we were looking around different platforms”. In order to solve their issues with incorporating expert review into their annotation workflows, the team used Encord Annotate's workflows to automate review by their labeling manager. Margaux explained that with Encord “We have a better reviewing system [...] that is the key component to having better quality datasets that we were missing before”. This allowed the annotators to get up to speed with complex annotations a lot quicker, without requiring experts to conduct annotations themselves. Margaux praised Encord's analytics capabilities, noting that “Now I have this whole system where I get analytics from Encord and we’re going to populate that into a dashboard so we can see how the annotation is going up”. She also appreciated how quick Encord was to incorporate Meta’s Segment Anything Model (SAM) into the platform, stating “One feature that made me go with Encord was the integration of SAM in the [Encord Annotate] platform which was done really quickly after the model was released so I knew when there was a new computer vision model released it will be integrated into the platform quite fast - which is something that was also a really good point”. Results 10x faster annotation whilst moving towards 0% annotation errors (previously 20%) After integrating Encord into their wider data pipelines, SDSC was able to produce high-quality training data with quick annotations. With the help of Encord Active, the team identified that approximately 20% of the annotations completed on the previous tool were incorrect. The team is now “aiming to have 0% bad annotations” with the use of Encord’s platform. Margaux discussed an upcoming project where SDSC would be annotating 100 hours of procedures (20 procedures at 5 hours per procedure) in four months and she expressed confidence in their ability to complete the task with Encord, in conjunction with their wider Active Learning pipeline. According to Margaux,“... we know we can do that now with Encord because of the whole process that we have, which compared to what we had before, it would be maybe one procedure every two months even, much slower”. This represents a 10x increase in efficiency, as SDSC would have previously been able to annotate only around 10 hours (2 procedures) in the same time frame.  As SDSC continues to grow and increase model production, they will further scale their use of Encord Annotate in addition to building out more mature Active Learning pipelines using Encord Active, given their initial success with the automated label error detection feature.

Treeconomy uses Encord to collect reliable, granular data on trees. This is used to more accurately measure the carbon content in forests. More reliable carbon credit data bolsters Treeconomy’s reputation, allows the company to charge 50-150% more for carbon credits, and enables them to redirect more funds to reforestation efforts.  Customer Treeconomy, an Earth Tech company, was created to better incentivize the planting of trees. It does so by accurately quantifying carbon captured and stored by trees (using sophisticated technology), packaging them as carbon offsets, and selling these high-quality carbon offsets to industries. Profits generated benefit landowners and incentivize them to preserve forests and plant trees.  What sets Treeconomy apart from competitors is its use of world-class remote sensing, machine learning, and monitoring tools. This enables Treeconomy to more accurately measure carbon offsets and detect changes to a project location that could alter carbon levels. Doing so helps to assure investors that their nature-based carbon credits are real, that trees are really growing, and that the project is delivering on its impact claims.  Problem Accurately measuring the amount of carbon in a forest is extremely important for carbon offsetting. Errors can lead to greenwashing and a loss of trust in the carbon market. As Treeconomy Co-Founder Robert Godfrey notes “Right now there are a number of companies being called out and caught with bad credits on their books, where projects have maybe grossly overestimated their climate impact.”  Yet measuring carbon in forests is difficult. Traditionally, this was done by meticulously going from tree to tree with a tape measure and making broad extrapolations based on these measurements. The process was time-consuming and error-prone. Monitoring changes in the forest - like growth or deforestation - was similarly laborious.  To address these challenges, Treeconomy explored open-source data and satellite imagery but found they had limitations. For example, satellite imagery still required them to manually count trees, a time-consuming and inefficient process. They created a computer vision algorithm to detect tree crowns from high-resolution drone and satellite data imagery but determined that larger and more relevant data sets were required to produce more accurate results.  Solution Treeconomy turned to Encord as an intuitive and flexible data annotation tool capable of labeling hundreds of images at once. The tool has enabled Treeconomy to train its computer vision algorithm to accurately count the number of trees in a given location. One of the big advantages of Encord for Treeconomy is its compatibility with Microsoft Azure and ease of use. This enabled the team to seamlessly integrate their existing data sets, thereby saving time and effort.  Using Encord resulted in a positive discovery for Treeconomy: The tool could facilitate their future plans of labeling specific tree species - not just counting trees - and remove the need to hire staff for the task.  Results Encord has helped Treeconomy gather more accurate data and save time. As Godfrey explains, “Encord helps us to offer a ‘best in class’ capability for counting trees. It has helped us to improve the computer vision algorithm that allows us to delineate individual tree tops. [We were able] to count the entire forest in two minutes as opposed to two weeks.”  By accurately quantifying trees in a forest, Treeconomy has been able to create more reliable carbon offsets. According to Godfrey, this has allowed the company to package and sell these offsets at premiums ranging from 50% to 150% above those of alternative options.  This increased revenue is not only good for the company’s bottom line but is channeled back into the communities, benefiting landowners and supporting reforestation efforts. Ultimately, these actions reinforce Treeconomy's dedication to transforming sustainable land use into an economically rewarding and competitive venture. {{try_encord}}