LOS ANGELES – RadNet, Inc. (NASDAQ: RDNT), a national leader in providing high-quality, low-cost, ambulatory diagnostic imaging services to inpatients, reported that its artificial lung intelligence subsidiary, Aidence, and google healthA division of Alphabet, Inc. (NASDAQ: GOOG), announce a license agreement GoogleHealth AI research model to predict malignant lung nodules in CT imaging. Aidence will develop, validate and commercialize this model to support the early and accurate diagnosis of lung cancer and the reduction of unnecessary procedures in screening programs.
According to the 2020 NELSON study, lung cancer screening with low-dose CT has been shown to significantly reduce lung cancer mortality by up to 24% in men and 33% in women. Screening initiatives are increasingly being implemented in Europelike them Great Britain Targeted lung health checks. in the The United Stateseligibility criteria have recently been expanded, further reflecting the usefulness of lung cancer screening.
A major difficulty in lung cancer screening is determining the type of lung nodules detected. Most of these nodules are noncancerous. However, correctly identifying and diagnosing such nodules can be time-consuming, costly, fearful for patients and their families, and sometimes invasive, requiring follow-up CT scans or surgical intervention.
dr Raymond Osarogiagbon, Chief Scientist, Baptist Memorial Health Care Corp and Director, Multidisciplinary Thoracic Oncology Program, Baptist Cancer Center, Memphis, Tennessee, stated: “One of the most exciting developments in modern public health care is the early detection of lung cancer. Unfortunately, the reality that most of these nodules will be benign presents a real challenge that calls for a technological solution. Artificial intelligence is one such solution.”
dr Osarogiagbon continued, ‘The world looks forward to the rapid development and validation of software that will improve our ability to find the many lung cancer needles in the vast haystack that is made up of CT detected lung nodules in today’s clinical practice.’
Deep learning, a subset of AI, has been shown to aid in risk assessment of malignant pulmonary nodules. In a 2018 study published in Nature, scientists agreed google health presented a highly accurate model for malignancy classification that consistently matches the performance of experienced radiologists.
Aidence has also built a deep learning model for this purpose. Aidence’s algorithm successfully predicts lung cancer from a single scan and was recognized in the 2017 Kaggle Challenge. Its robust performance was later confirmed in a clinical study comparing its performance to that of 11 radiologists in 300 cases.
Aidence and Google Health intend to complete an AI application to predict malignancies in lung nodules. In this collaboration google health will make its scientific expertise available. Aidence will further develop the model into a solution for clinical practice in compliance with relevant data protection requirements and regulatory standards and bring it to market. The development of this AI application is a letter of intent and no regulatory market applications have been made and no sell orders are being accepted.
Outside of this collaboration with google healthAidence has a proven track record of deploying AI in hospitals and clinics around the world Europe. Its application, VeyeLung Nodules, is currently running in over 80 routine practices and lung cancer screening facilities.
Mark Jan Harte, Co-Founder and CEO of Aidence, said, “At Aidence, our mission is to give lung cancer patients a chance. This strategic partnership with google health allows us to accelerate and expand our efforts to achieve this goal.’
Mr Hardness continued, “We are excited to be working on a powerful deep learning model for predicting malignant pulmonary nodules, based on the work of the teams at Aidence and Google, and are making sure to meet all other requirements necessary for successful deployment of AI in the clinic Practice are in place, such as clinical validation, certification and integration into clinical workflow.’
Akib Uddin, Product Manager at google health, commented, “At Google Health, we want to be an active, catalytic force to demonstrate the real-world benefits of AI in healthcare. We know how important lung cancer screening is in saving lives, and we’re excited to play a role in advancing the impact at scale by supporting great partners like Aidence with our research.’
Around RadNet, Inc.
RadNet, Inc.is the leading national provider of free-standing, in-patient diagnostic imaging services and related information technology solutions (including artificial intelligence) in The United States based on number of sites and annual imaging revenue. RadNet has a network of 349 owned and/or operated outpatient imaging centers. RadNets include markets Arizona, California, Delaware, Florida, Maryland, New Jersey and new York. Along with affiliated radiologists, including full-time and per diem staff and technologists, RadNet employs a total of around 9,000 people. Visit http://www.radnet.com for more information.
Around google health
A division of Alphabet, Inc., google health is our company-wide effort to help billions of people live healthier lives. We are working towards that vision by engaging people in their everyday moments and empowering them to stay healthy, and working with care teams and the public health community to provide more accurate, accessible and equitable care. Our teams apply our expertise and technology to improve health outcomes worldwide – with high-quality information and tools to help people manage their health and well-being, solutions to transform healthcare, research to advance the use of artificial intelligence for the screening and diagnosis of disease; and data and insights for the public health community. https://health.google/
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