Artificial intelligence's (AI) applicability in cardiac imaging is rapidly growing and was a major topic of discussion at this year's EuroPCR 2022 meeting. Many session speakers discussed how they are using AI tools in their day-to-day practice and in their research to improve decision-making and patient/research outcomes. It's no secret, however, that AI tools are only as good as the data sets and the thousands of expert opinions used to power them. Implementing AI applications in our day-to-day practice, from an operations standpoint, could mean adjusting clinician workflows and setting aside time to set up and train on the new systems. And from an efficacy standpoint, it leaves clinicians wary of result accuracy, especially if they are unsure how good the data used to power the technology really is.

One of the imaging technologies that many robotics companies are integrating into their sensor packages is Light Detection and Ranging, or LiDAR for short. Currently commanding great attention and investment from self-driving car developers, the approach essentially works like radar, but instead of sending out broad radio waves and looking for reflections, it uses short pulses of light from lasers. Traditional time-of-flight LiDAR, however, has many drawbacks that make it difficult to use in many 3D vision applications. Because it requires detection of very weak reflected light signals, other LiDAR systems or even ambient sunlight can easily overwhelm the detector. It also has limited depth resolution and can take a dangerously long time to densely scan a large area such as a highway or factory floor.

Entering 2022, the world continues to endure the pandemic. But the security industry has, no doubt, continued to shift, adapt, and develop in spite of things. Several trends have even accelerated. Beyond traditional "physical security," a host of frontiers like AI, cloud computing, IoT, and cybersecurity are being rapidly pioneered by entities big and small in our industry. By all appearances, the security industry is in a stage of redefining itself.

Bioimaging technologies are the eyes that allow doctors to see inside the body in order to diagnose, treat, and monitor disease. Ge Wang, an endowed professor of biomedical engineering at Rensselaer Polytechnic Institute, has received significant recognition for devoting his research to coupling those imaging technologies with artificial intelligence in order to improve physicians' "vision." In research published today in Patterns, a team of engineers led by Wang demonstrated how a deep learning algorithm can be applied to a conventional computerized tomography (CT) scan in order to produce images that would typically require a higher level of imaging technology known as dual-energy CT. Wenxiang Cong, a research scientist at Rensselaer, is first author on this paper. Wang and Cong were also joined by coauthors from Shanghai First-Imaging Tech, and researchers from GE Research.

Bioimaging technologies are the eyes that allow doctors to see inside the body in order to diagnose, treat, and monitor disease. Ge Wang, an endowed professor of biomedical engineering at Rensselaer Polytechnic Institute, has received significant recognition for devoting his research to coupling those imaging technologies with artificial intelligence in order to improve physicians' "vision." In research published today in Patterns, a team of engineers led by Wang demonstrated how a deep learning algorithm can be applied to a conventional computerized tomography (CT) scan in order to produce images that would typically require a higher level of imaging technology known as dual-energy CT. Wenxiang Cong, a research scientist at Rensselaer, is first author on this paper. Wang and Cong were also joined by coauthors from Shanghai First-Imaging Tech, and researchers from GE Research.

The Boston Fire Department started to use emerging technology to fight fires in the last couple of years. In collaboration with Karen Panetta, an IEEE fellow and dean of Graduate Education at Tufts University's School of Engineering, the department is using AI for object recognition. The goal is to be able to use a drone or robot that can locate objects in a burning building. Panetta worked with the department to develop prototype technology that leverages IoT sensors and AI in tandem with robotics to help first responders "see" through blazes to detect and locate objects – and people. The AI technology she developed analyzes data coming from sensors that firefighters wear, and it recognizes objects that can be navigated in a fire.

"A Google Maps for surgeons" is how Perimeter Medical Imaging AI Inc. (TSXV: PINK) President and CFO Jeremy Sobotta described the AI software currently being developed by the company to complement its FDA-cleared medical imaging system at a recent investment conference. Perimeter is a medical technology company working to transform cancer surgery by creating ultra-high-resolution, real-time, advanced imaging tools to address unmet medical needs. The imaging tools have already been developed and are approved in ophthalmology and cardiology (optical coherence tomography or OCT). Perimeter is using this imaging technology (OTIS or Optical Tissue Imaging Console) to assess the tissues surrounding the known cancerous target area to determine whether more tissue should be removed during the ongoing surgery. The imaging technology has the ability to rapidly image large and complex surfaces.

The increased sophistication of artificial neural networks (ANNs) coupled with the availability of AI-powered chips have driven am unparalleled enterprise interest in computer vision (CV). This exciting new technology will find myriad applications in several industries, and according to GlobalData forecasts, it would reach a market size of $28bn by 2030. The increasing adoption of AI-powered computer vision solutions, consumer drones; and the rising Industry 4.0 adoption will drive this phenomenal change. Deep learning has bought a new change in the role of machine vision used for smart manufacturing and industrial automation. The integration of deep learning propels machine vision systems to adapt itself to manufacturing variations.

NexOptic's ALIIS solution powered by NVIDIA (Nasdaq:NVDA, $250 billion market cap) Jetson Edge AI platform to unlock new application paths in robotics, smart cities, industrial automation, and healthcare NexOptic Technology Corp. (OTCQB:NXOPF, TSXV:NXO) is led by a blue chip team, including turnaround specialist and former CEO of Lexmark International, Rich Geruson. Their remarkable A.I. imaging technology has been noticed by numerous multinationals. NexOptic's collaborations with Qualcomm (Nasdaq:QCOM, $103 billion) and now Ndiva's "Preferred Partner" network puts them into an elite group of AI firms gaining integrated access to an A-list customer base. NexOptic further announced on July 20th the introduction of a revolutionary AI for neural image signal processors (ISP's) – a fusion of leading edge and traditional imaging technologies, further expanding their defensive intellectual property portfolio strategy. NextGen machines, cars, cities and platforms need to "see" efficiently and process instantly.

CERA researchers are investigating the use of artificial intelligence to help detect signs of keratoconus, thanks to new funding from the Perpetual 2020 IMPACT Philanthropy Application Program. CERA Senior Research Fellow Dr Srujana Sahebjada is devoted to improving the quality of life for people with keratoconus, a condition that affects the cornea, the clear front window of the eye. Keratoconus usually affects teenagers and young adults. For people with this condition, the cornea gets thinner over time and develops a bulging cone-like shape, which causes vision problems. In advanced cases, a corneal transplant will be required to correct or restore vision.