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.

Automated harvesting and analytics company Four Growers uses Encord’s platform and annotators to help build its training datasets from scratch labeling millions of instances of greenhouses and plants. So far, Encord has halved the time it takes Four Growers to get its datasets in working order.  Customer: Meet Four Growers Four Growers was founded to provide healthy, affordable, local produce by reducing the production costs of greenhouse growers through autonomous harvesting robots.  The GR-100 robot has a robotic arm, calibrated by four stereo cameras that allow it to precisely detect and harvest produce using computer vision, focusing on tomatoes initially. Now in its second iteration, the GR-100 also offers heat mapping and yield forecasting abilities, as well as patented packing technology.  By automating the harvesting process, Four Growers hopes to minimize food waste, increase quality, and contribute to making produce more affordable for everyone.  Problem: Insufficient Access to Annotated Datasets Hinders Development of Automated Harvesting Models Currently, commercial greenhouses are expansive and it is impossible for manual pickers to get all of the ripe fruits on time. Over time, this leads to a large quantity of fruit just rotting in the greenhouse.  Hoping to expedite this process, Four Growers set about developing their harvesting and analytics robot. But when it came to training its deep learning models for automated harvesting, Four Growers found that while there is a lot of awareness around growing in greenhouses, there was a distinct lack of publicly available data sets to draw from.  This meant that the company had to build its own datasets from scratch, initially employing a number of freelance contractors to manually annotate all of the data, which consisted mostly of images of greenhouses and tomato plants.  This process was incredibly time-consuming and manual. By having multiple external annotators using various tools, the Four Growers team had to often deal with multiple different annotation platforms at once, consolidating the data into one succinct database at a later stage.  For Mira Murali, Computer Vision Engineer at Four Growers, this manual approach was eating into her schedule every week taking as long as 10 hours to manually review and condense data from multiple annotators that came through in various formats. However, despite the huge amount of time, it was taking to have the data manually labeled and then verified by the team at Four Growers, it felt like a necessary task given the importance of having clean accurate data to begin with.   “You can tweak your models all you want but if your data at the beginning is wrong, then no matter how good your model is, you won’t get the end resource that you want,” Mira says. Solution: Encord Annotate for automated labeling and a team of annotators The team at Four Growers needed a data annotation tool that could handle the labeling of millions of object instances at the same time and this is where Encord came into the mix.  Initially, the team was attracted to Encord because not only did the platform offer automatic annotating, but it also allowed Four Growers to work with an in-house team of human annotators. So rather than having different freelance contractors using different systems that had to be later consolidated, this streamlined approach instantly gave the Four Growers team time back in their days to focus on innovating their product and growing the business.  “The fact that Encord was able to offer both an automatic labeling tool as well as a team of annotators that we could use was very attractive. We need that flexibility at this stage in our journey,” says Murali.   Encord Active is another tool beginning to be utilized by the Four Growers team. It is an open-source toolkit that can be used for testing, validating, and evaluating your models. This allows Four Growers to further segment their datasets and build on their predictive model, making the harvesting even more precise. Results: Significant Time Reduction and Streamlined Annotation Process Achieved with Encord's Platform For Four Growers, a fast-growing and lean business, the biggest win with using Encord has been the time saved. Now, instead of spending hours upon hours dealing with contractors and re-verifying all of that work, the team can simply upload their images to Encord’s platform, wait for a ping to say the annotation work is complete, and focus on their core jobs.  Speaking about the experience with Encord’s platform, Murali says:  “Using Encord has more than halved the time it takes us to provide feedback on imagery. What could have previously taken us 2 hours now takes approximately 30 minutes.”  Four Growers currently works with a number of large commercial greenhouses located in the US, Canada, and the Netherlands. As the company continues to grow its business and expand into more regions and crops, the company plans to continue its journey with Encord, leaning into the data analysis aspect of the platform to further understand how the team can improve its model, further streamline harvesting and help reduce food waste.