In 2018, five patients at the Apex Heart Institute in Ahmedabad, India, received treatment for coronary artery disease (CAD) the same way 3 million others do each year: a small balloon was inserted into an artery in the heart and inflated, making way for the placement of a stent to keep the vital pathway open. The procedure, known as percutaneous coronary intervention (PCI), is the standard treatment for atherosclerosis, a common CAD marked by the buildup of plaque inside the arteries and a subsequent restriction of blood flow. Like many patients before them, their operation was assisted by a robot--the CorPath GRX robotic platform from Corindus, A Siemens Healthineers Company. Yet unlike anyone else before them, these five patients were part of an amazing first: their principal physician was not in the room with them during the procedure. In fact, he was 20 miles away, guiding the robot--performing the operation to perfection--from a remote workstation.

Implementing deep learning in enterprise settings requires a lot more than just downloading some open source algorithms, but with talent scarce, businesses are finding it takes creativity and an open-minded approach to achieve results. "Established industries are largely missing out on the benefits of AI," said Ryan Kottenstette, co-founder and CEO of Silicon Valley geospatial data company Cape Analytics LLC. "If you're not in the tech sector, you might be waiting a bit longer for the benefits of AI to be realized." In recent years, deep learning has taken huge strides. Algorithmic processes like neural networks, which historically lived more in the realm of mathematical theory, have moved into some enterprise use cases, like computer vision and process automation. But adoption has been uneven.

California-based startup Cape Analytics uses artificial intelligence (AI) to check out your rooftop for insurance purposes. By analysing aerial imagery, the company can tell insurers the condition of your roof, as well as automate away human inspectors originally meant for the purpose. Most of the imagery comes from airplanes, although drones and satellites also play a role, chief executive and co-founder Ryan Kottenstette told Forbes. He added that Cape Analytics also takes in Earth observations from Google. Deep analysis distinguishes Cape from other imagery analytics firms such as Orbital Insight, Kottenstette said in the Forbes report.

When you first buy a house, your insurance company doesn't know very much about it or how much insuring it will cost. That's because it first has to send out an inspector to look at the exterior of your house, take measurements, and check out your roof to see what kind of shape it's in. Cape Analytics, a Mountain View-based data analytics startup, aims to change all that. Its API-pipeline can feed an insurance company information about a house's exterior square footage, roof type, roof condition, changes in a home and more - all thanks to the use of machine learning to analyze aerial imagery. The company announced Monday that it's launching data coverage for the entirety of the continental United States - over 70 million American homes - though it's already been providing information to insurance customers like national reinsurer XL Catlin and the Florida-based Security First Insurance.

Cape Analytics has raised $14 million to use computer vision and machine learning to improve automated property underwriting for insurance companies. Formation 8 led the round, with participation from XL Innovate, Data Collective, Lux Capital, Khosla Ventures, Promus Ventures, and Montage Ventures. The funding is one more application of machine learning and computer vision for business automation. Palo Alto, Calif.-based Cape Analytics starts with images of a home to help assess the home's value and allow an insurance carrier to deliver more accurate and fast quotes. The funding will allow Cape Analytics to expand its world-class engineering and sales teams as it brings its proprietary data to more regions and customers around the United States.