Israeli eHealth platform developer, Zebra Medical Vision, has launched a closed beta of its Medical Imaging Research platform that will enable researchers to quickly develop imaging algorithms and insights based on large scale datasets and advanced processing power.
Due to an aging global population the demand for medical imaging is rapidly increasing, and according to Zebra, fast and accurate diagnosis is getting increasingly difficult to achieve with existing Radiology resources. Therefore, there is a growing need for accurate and automated tools to enable high quality diagnostic insights at scale.
“Advances in machine learning and computer vision have made it possible to create diagnostic quality algorithms based on big data, that surpass current reading accuracy rates,” said Zebra Medical CEO, Elad Benjamin. “Such algorithms will reduce false positives, identify false negatives, provide earlier diagnosis of cancer or other diseases and unlock incidental findings hidden in the vast amounts of imaging data that resides within archives of health providers.”
Khosla Ventures founder, Vinod Khosla, added that: “Zebra is combining the power of machine learning, computer vision and big data to do just that in medical imaging – creating a sandbox through which imaging innovation can occur and be delivered to patients.”
Zebra’s platform offers a cloud-based, fully hosted research and development environment. This includes access to large datasets of structured, de-identified studies, storage, GPU computing power and support for a multitude of research tools. The platform also enables research groups to collaborate and create joint tools.
“Zebra is the only platform today that offers such seamless access to both the tools and the needed datasets and research environment – and at such a large scale,” said Professor of Radiology, Chair of Radiology at Erasmus University Medical Centre Rotterdam and past President of the European Society of Radiology, Gabriel Krestin. “This will finally enable providers to bring medical imaging into the fold of large scale clinical analysis and population management.”