Artificial Intelligence: Arterys AI has not had a bad year yet. Between breakthrough technologies and soaring funding rounds, there was no shortage of strong candidates to choose from in 2017. Ambra Health CEO Morris Panner, JD, gave the nod to Arterys. The 10-year-old San Francisco, California, company both started and ended 2017 in style. In January, it received a first-of-its-kind FDA approval for its cloud-based technology, which applies AI and deep learning to medical imaging analysis.
Around the world, researchers in startups, academic institutions and online communities are developing AI models for healthcare. Getting these models from their hard drives and into clinical settings can be challenging, however. Developers need feedback from healthcare practitioners on how their models can be optimized for the real world. So, San Francisco-based AI startup Arterys built a forum for these essential conversations between clinicians and researchers. Called the Arterys Marketplace, and now integrated with the NVIDIA Clara Deploy SDK, the platform makes it easy for researchers to share medical imaging AI models with clinicians, who can try it on their own data.
We're excited to share an interview with Dr. Mark Traill, who is the owner of MammoBot LLC. Mammobot is a cloud-based startup that offers a double reading of screening 2D or 3D mammograms using artificial intelligence algorithms. Clients can upload mammogram studies that become part of the investigational protocol prior to pending FDA approval of the algorithm. Review of the mammogram study by AI algorithms has the potential of detecting early breast cancer that was missed on the initial human review of images. Ambra Health: When did Mammobot begin?
The Arterys Cardio DLTM application is vendor agnostic and was developed using data from several thousand cardiac cases. The software produces editable automated contours, providing precise and consistent ventricular function in seconds. The trained deep learning algorithm was validated as producing results within an expected error range comparable to that of an experienced clinical annotator. This clearance enables Arterys to make use of its unique clinical annotation platform, which collects ground-truth data every time a user views a study on Arterys.com. In the future, the deep learning model can be optimized as new data is collected from all global users.
The first FDA approval for a machine learning application to be used in a clinical setting is a big step forward for AI and machine learning in healthcare and industry as a whole. Arterys's medical imaging platform has been approved to be put into use to help doctors diagnose heart problems. It uses a self-teaching artificial neural network which has learned from 1,000 cases so far, and will continue to improve its knowledge and understanding of how the heart works with each new case it examines. In order to be approved by the US Food and Drug Administration (FDA), it had to pass tests to show it can produce results at least as accurately as humans are currently able to. The key difference though is that Arterys takes an average of 15 seconds to produce a result for one case, which a professional human analyst would expect to spend between 30 minutes to an hour working on.