The artificial intelligence (AI) healthcare market is growing quickly. At 40% compound annual growth, it is expected to reach $6.6 billion by 2021 and $13 billion by 2025. As AI adoption spreads across the healthcare industry, organizations must be prepared to deal with the resulting exponential data growth.
Whether AI is used to power medical imaging or genomic sequencing, the success of AI depends completely on access to those large amounts of data that can be used to identify patterns, develop predictive insights, and enable increasingly accurate autonomous systems. But this data can be anywhere, is inherently dynamic, and is often in multiple forms. As a result, IT leaders say that data silos and technology complexity are the two biggest challenges to moving AI projects into production. Projects move quickly without the limitation of where data exists. They need a true data fabric.
BacillAi™, which uses deep learning and low-cost hardware for the treatment of tuberculosis (TB) is the latest output from Cambridge Consultants’ purpose-built deep learning research facility, equipped with the NetApp® ONTAP® AI proven architecture, which is powered by NVIDIA DGX systems and NetApp cloud-connected storage.
TB is the second largest cause of death by infectious disease in the developing world, and its high mortality rate is in large part due to a lack of available, affordable diagnosis and inconsistent results acquired in patient follow-up. TB is monitored by taking a sputum sample and manually counting cells under a microscope. In low-resource countries, this is very difficult. There are few skilled staff working in difficult conditions. Clinicians may need to review ten patients per day, while for each patient they may need to count hundreds of cells through a microscope. This leads to eye strain for clinicians and poor quality, slow results for patients.
BacilAi is an end-to-end concept system that uses a smartphone to capture images from an ordinary lab-grade microscope. The system analyzes sputum sample images by using a deep learning algorithm to identify, count, and classify TB cells, in order to determine the disease state of the patient. The results of the test are returned to the clinician through a dedicated app. Having an automated system powered by AI to count cells and classify treatment progression offers various benefits, including increased accuracy, higher throughput, and the automatic digitization of results.
Cambridge Consultants works at the leading-edge of advances in AI, applying deep learning in a range of applications for the world’s most ambitions companies, including anomaly detection in telecommunications networks, self-driving cars, frictionless user interfaces and medical diagnosis and monitoring.
Built on ONTAP AI, Cambridge Consultants’ latest breakthrough is an example of how NetApp helps you tailor your data fabric and accelerate your journey to AI. With NetApp solutions for AI, you can confidently tap into growing data sources with virtually unlimited, nondisruptive scalability and performance. NetApp offers a powerful unified data platform to feed, train, and operate data-hungry AI, machine learning, and deep learning applications.
Together with our partners NVIDIA and Cambridge Consultants, we are building smart, powerful, trusted AI healthcare solutions to meet your business goals. Only NetApp solutions enable you to integrate your data fabric and streamline the flow of data through all stages: ingestion and collection at the edge; preparation, training, and inference at the core; and analysis and tiering using the world’s biggest clouds
To learn more and see NetApp solutions in action, visit NetApp this week at AI Summit in booth 504 in San Francisco!