StarTree
StarTree Cloud is a fully-managed real-time analytics platform designed for OLAP at massive speed and scale for user-facing applications. Powered by Apache Pinot, StarTree Cloud provides enterprise-grade reliability and advanced capabilities such as tiered storage, scalable upserts, plus additional indexes and connectors. It integrates seamlessly with transactional databases and event streaming platforms, ingesting data at millions of events per second and indexing it for lightning-fast query responses. StarTree Cloud is available on your favorite public cloud or for private SaaS deployment.
StarTree Cloud includes StarTree Data Manager, which allows you to ingest data from both real-time sources such as Amazon Kinesis, Apache Kafka, Apache Pulsar, or Redpanda, as well as batch data sources such as data warehouses like Snowflake, Delta Lake or Google BigQuery, or object stores like Amazon S3, Apache Flink, Apache Hadoop, or Apache Spark.
StarTree ThirdEye is an add-on anomaly detection system running on top of StarTree Cloud that observes your business-critical metrics, alerting you and allowing you to perform root-cause analysis — all in real-time.
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TeleRay
TeleRay is an industry-first telehealth and image management platform. TeleRay cloud-based medical image management platform allows users to securely share images with professionals (specialists, referring, clinicians) and patients. The platform has many features, including the ability to import or convert DICOM or non DICOM images, query and HL7 connectivity. Integrate with any EMR, view images on an FDA approved viewer anywhere on any device.
Complete DICOM image migration is available- set up, training, and implementation is included. Live streaming and remote control of modalities are options and great for many use cases to place professionals virtually in a room any where.
TeleRay is the most secure platform with peer 2 peer health and data communication. You can use the app to access workflow tools like waiting rooms, multi-calls, call transfer and sharing of images. It's simple and affordable.
More than 3000 locations use our service, including 38 of the top medical centers in more than 20 nations. Get started today for free.
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DatCard
Hospitals and imaging facilities are overwhelmed by the influx of discs from various external sources. The process of manually importing this data proves to be both labor-intensive and expensive. However, with the implementation of the Automated Disc Importer, these organizations can efficiently transfer large quantities of discs into either temporary cloud storage or local systems without requiring human assistance. After the discs are processed, users are able to validate the data before it is officially archived. Additionally, this system enhances security by capturing an image of each disc label during the reading process, which aids in maintaining a comprehensive audit trail. These photographs not only document the imports but also specify the origin of each disc. By leveraging the Automated Disc Importer, medical images and reports from DICOM discs are effectively processed, significantly minimizing the time previously dedicated to manual data entry, and improving overall workflow efficiency. This innovation represents a substantial advancement in the management of medical imaging data.
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RedBrick AI
RedBrick AI serves as a rapidly collaborative platform for annotating medical data, specifically designed to assist healthcare AI teams in creating high-quality training datasets across various types of radiological imagery, including CT, MRI, X-ray, Ultrasound, Fluoroscopy, and additional standard imaging techniques. The platform is adept at managing intricate tasks such as multi-series annotation and extensive DICOM studies, thanks to its native compatibility with medical data formats including DICOM and NIfTI. Furthermore, it boasts cutting-edge, user-friendly 2D and 3D web-based annotation tools, complemented by a PACS-like viewer. RedBrick AI supports a wide array of annotation use cases, including instance and semantic segmentation, landmark identification, classification, and ROI measurements, thereby enhancing the speed of annotation processes by as much as 60%. This significant improvement in efficiency can empower healthcare professionals to focus more on patient care rather than on time-consuming data preparation tasks.
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