Goto

Collaborating Authors

Imaging analytics Zebra Medical Vision

#artificialintelligence

Our solutions provide automated analysis of millions of real-time and retrospective imaging studies. This allows early identification of disease, and implementation of decision support tools for population health and risk management. Our development team and our dedicated partners are creating the hundreds of algorithmic tools and insights needed to bring clinical diagnostic support to the next level. A sample of our algorithms can be seen here, with a multitude of additional insights planned for release over the coming months.


Zebra Medical Vision and DePuy Synthes to bring AI to orthopaedic care

#artificialintelligence

Israel's Zebra Medical Vision has teamed up with Johnson & Johnson subsidiary DePuy Synthes to jointly bring cloud-based AI solutions to the orthopaedic and bone health industry. Under the partnership, Zebra Medical Vision's machine learning algorithms will be used to create three-dimensional models of patients from X-ray images. Conventional orthopaedic procedures are based on two-dimensional CT scans or MRI imaging to assist with pre-operative planning. However, CT scans and MRI imaging can be expensive, associated with more radiation and are painful for some patients. Zebra Medical Vision co-founder and CEO Eyal Gura said: "We are thrilled to start this collaboration and have the opportunity to impact and improve orthopaedic procedures and outcomes in areas including the knee, hip, shoulder, trauma, and spine care.


Q&A: Stanford's Curtis Langlotz on teaching AI to medical imaging students

#artificialintelligence

The hype around artificial intelligence (AI) in medical imaging has led to plenty of discussions of its impact in clinical and academic spaces. To explore current and future implementations of AI in medical imaging at academic institutions, Health Imaging spoke with Curtis Langlotz, PhD, Stanford University's Medical Informatics Director for Radiology. Health Imaging: Where do you think AI will first be deployed in medical imaging? Curtis Langlotz, PhD: Over the next decade, AI will be deployed throughout the image life cycle from image production to image interpretation. For example, machine learning algorithms will produce clearer images using less radiation and will alert technologists to suboptimal images at the scanner console.


AI Solutions – Zebra Medical Vision

#artificialintelligence

Zebra's Imaging Analytics Engine receives imaging scans from various modalities and automatically analyzes them for a number of different clinical findings, in a timely manner and full synergy with radiology workflow. Zebra uses a proprietary database of millions of imaging scans, along with machine and deep learning tools, to create software that analyzes data in real time with human level accuracy – providing radiologists the assistance they need to manage ever growing workloads, without sacrificing quality. Providers use Zebra to alert them of patients at high risk of cardiovascular, lung, bone and other diseases. With Zebra – focus can be provided to the right patients, at the right time – saving overall costs while improving care. Our Imaging Analytics Engine uncovers brain, lung, liver, cardiovascular and bone disease in CT scans, 40 different conditions in X-rays scans, and breast cancer in 2D mammograms with an ever growing pipeline.


How is Computer Vision Making a Difference in Healthcare?

#artificialintelligence

Computer vision has made the transition from being the subject of sci-fi movies to an actual technology which you can find in top hospitals. This visual branch of artificial intelligence is helping more doctors better diagnose patients, prescribe the right treatments and monitor the evolution various diseases. Technopedia defines computer vision as a distinctive field of computer science which helps computers to see, identify, and process images in a way which is similar to the way humans perform this task. It is part of artificial intelligence since to identify objects and take decisions based on what it sees it is necessary to make an in-depth analysis. Predicting the applications of a computer vision solution for medical use has to do with extending current ways this technology is already being used and adding a layer of creativity and imagination.