Several industries are seeing the impact of robotics -- and medicine is no exception. While the progress of these applications has been slow compared to other industries, the impact could be huge: robotics in medicine can help to reduce human error, improve recovery time, and reduce hospital stays, ultimately enhancing patients' quality of life. The first medical robotic application appeared in 1985, when an early robotic surgical arm assisted in a neurosurgical biopsy surgery. Fifteen years later, the first fully FDA-approved system (known as the da Vinci surgery system) for laparoscopic surgery emerged, giving surgeons the ability to control surgical instruments indirectly via a console. Today, companies are leveraging advances in the tech to develop new robotic applications to explore the future of medicine -- including those related to bionics, disease discovery, and rehabilitation.
Marlborough medical device maker Hologic announced Tuesday it has received U.S. Food & Drug Administration approval for its 3DQuorum Imaging Technology to reduces image interpretation time. The company said when combined with its high-resolution imaging technology, this new technology reduces the number of images needing to be reviewed without compromising image quality or accuracy through using artificial intelligence to find the best images. The number of images to be reviewed is reduced by 66%, according to Hologic, saving an average of one hour per eight hours of image interpretation time. The technology is available for use with existing and future Hologic 3D mammography systems.
HeartVista is hoping to increase the use of CardiacMRI's. To achieve this goal, the Los Altos, CA-based company has developed the One Click Cardiac Package MRI software, which recently received FDA clearance. "It has been widely excepted in the clinical community and the scientific community that cardiac MRI is the gold standard for everything that you can almost do or diagnose in a cardiac setting," Itamar Kandel, HeartVista's CEO, told MD DI. "But the problem is that the actual scan itself is very complex; takes a lot of time, and takes a very high level of skill to even perform." He added, "You have this situation where the best technology is not approachable to the vast majority of the needs. The problem we came to solve is to bring this technology to the masses -to democratize Cardiac MRI." HeartVista's FDA-cleared Cardiac Package uses AI-assisted software to prescribe the standard cardiac views with just one click, and in as few as 10 seconds, while the patient breathes freely.
Omega Medical Imaging, manufactures of Artificial Intelligence Fluoroscopy/Cine (AIF/C) Imaging systems, just announced the Food and Drug Administration 510 (k) clearance of FluoroShield with their 2020 Cardiac Flat Panel Detector. The unique FluoroShield system allows for auto collimation during interventional fluoro or cine cases while maintaining a perspective of surrounding anatomy. The blended image incorporates a lower frequency refresh of the peripheral image area. This combined image (live fluoroscopy or cine of ROI background refreshed at a rate of once or twice per second) increases the quality of information presented during interventional procedures. Brian Fleming, President of Omega Medical Imaging states, "Until now products on the market have only been able to manage radiation to patients and staff. FluoroShield is the only system in the world that provides an actual reduction in dose. The impact of this groundbreaking solution for patients and healthcare providers is substantial. I am very grateful to be a part of a team that pushes the envelope in the development of safer healthcare solutions."
The US Food and Drug Administration (FDA) has cleared an artificial intelligence (AI) algorithm from GE Healthcare that analyzes chest x-rays for pneumothorax and helps flag suspected cases for radiologists to prioritize reading, the company announced today. The algorithm, part of a set of other quality-assurance algorithms named the Critical Care Suite, was developed to run on a GE Healthcare mobile x-ray device. The software is not yet for sale, and an outside expert expressed concern about its false positive rate. The idea for the application came from bedside clinician experience of waiting for radiologists to read chest x-rays, said Rachael Callcut, MD, MSPH, a surgeon and director of data science for the Center for Digital Health Innovation at the University of California, San Francisco. UCSF proposed developing the feature as part of a development partnership with GE Healthcare.
GE Healthcare announced the Food and Drug Administration's 510(k) clearance of Critical Care Suite, a collection of artificial intelligence (AI) algorithms embedded on a mobile X-ray device. Built-in collaboration with UC San Francisco (UCSF), using GE Healthcare's Edison platform, the AI algorithms help to reduce the turn-around time it can take for radiologists to review a suspected pneumothorax, a type of collapsed lung. Additional partners in the development of Critical Care Suite include St. Luke's University Health Network, Humber River Hospital, and CARING – Mahajan Imaging – India. A prioritized "STAT" X-ray can sit waiting for up to eight hours for a radiologist's review1. However, when a patient is scanned on a device with Critical Care Suite, the system automatically analyzes the images by simultaneously searching for a pneumothorax.
The U.S. Food and Drug Administration granted 510(k) clearance to GE Healthcare's Critical Care Suite -- a collection of artificial intelligence (AI) algorithms embedded on a mobile X-ray device, the healthcare business division of GE announced today. Using GE Healthcare's Edison platform, the AI aims to help reduce the turnaround time it takes radiologists to review suspected pneumothorax, a type of collapsed lung. "By integrating AI into every aspect of care, we will ultimately improve patient outcomes, reduce waste and inefficiencies and eliminate costly errors," said Kieran Murphy, president and CEO of GE Healthcare. If a patient is scanned on a device with Critical Care Suite, it will automatically analyze the images by searching for a pneumothorax, GE Healthcare claims. If suspected, an alert with the original X-ray is sent straight to the radiologist to review.
Often, large corporations – which may dominate a market sector like the medical imaging field, which includes computerized tomography (CT), magnetic resonance imaging (MRI) and radiography – are so large they are not able to adapt to new, even disruptive, technologies. Sometimes these are spun off into other companies, as is the case with Harris Corporation and AuthenTec, which developed the fingerprint technology used on Apple phones. Other times, a smaller, nimbler company may emerge that introduces something transformative. Such may be the case with Central Florida's Omega Medical Imaging led by Brian Fleming, which recently received FDA clearance for its FluoroShield system. EW: I want to talk about how you became CEO of Omega, but first, explain the FluoroShield system.
The field of image reconstruction has undergone four waves of methods. The first wave was analytical methods, such as filtered back-projection (FBP) for X-ray computed tomography (CT) and the inverse Fourier transform for magnetic resonance imaging (MRI), based on simple mathematical models for the imaging systems. These methods are typically fast, but have suboptimal properties such as poor resolution-noise trade-off for CT. The second wave was iterative reconstruction methods based on more complete models for the imaging system physics and, where appropriate, models for the sensor statistics. These iterative methods improved image quality by reducing noise and artifacts. The FDA-approved methods among these have been based on relatively simple regularization models. The third wave of methods has been designed to accommodate modified data acquisition methods, such as reduced sampling in MRI and CT to reduce scan time or radiation dose. These methods typically involve mathematical image models involving assumptions such as sparsity or low-rank. The fourth wave of methods replaces mathematically designed models of signals and processes with data-driven or adaptive models inspired by the field of machine learning. This paper reviews the progress in image reconstruction methods with focus on the two most recent trends: methods based on sparsity or low-rank models, and data-driven methods based on machine learning techniques.