LOS ANGELES, Nov. 28, 2022 (GLOBE NEWSWIRE) -- RadNet, Inc. (NASDAQ: RDNT), a national leader in providing high-quality, cost-effective, fixed-site outpatient diagnostic imaging services today reported that its lung artificial intelligence subsidiary, Aidence, and Google Health, a division of Alphabet, Inc. (NASDAQ: GOOG), announce an agreement to license Google Health's AI research model for lung nodule malignancy prediction on CT imaging. Aidence will develop, validate and bring this model to the market to support the early and accurate diagnosis of lung cancer and the reduction of unnecessary procedures in screening programs. Lung cancer screening with low-dose CT has been shown to significantly reduce lung cancer mortality by as high as 24% for men and 33% for women, according to the 2020 NELSON trial. Screening initiatives are increasingly being implemented in Europe, such as the UK's Targeted Lung Health Checks. In the United States, eligibility criteria have recently been broadened, further reflecting the benefit of lung cancer screening.

"For every $1 you spend developing an algorithm, you must spend $100 to deploy and support it." If you're not familiar with our industry, this may sound counterintuitive. The development of AI clinical solutions does not consist solely of modelling. It is a long and challenging process, from gathering and curating medical data to training, testing, validating, and certifying the model; deploying it in the hospitals' complex IT landscape; maintaining and improving its performance. In this article, I zoom in on the development of a'complete' AI solution, based on our approach with Veye Lung Nodules, a medical device currently used in over 70 European sites. The story of Veye is, in many ways, the story of building Aidence, from founding to our recent acquisition.

To determine whether deep learning algorithms developed in a public competition could identify lung cancer on low-dose CT scans with a performance similar to that of radiologists. In this retrospective study, a dataset consisting of 300 patient scans was used for model assessment; 150 patient scans were from the competition set and 150 were from an independent dataset. Both test datasets contained 50 cancer-positive scans and 100 cancer-negative scans. The reference standard was set by histopathologic examination for cancer-positive scans and imaging follow-up for at least 2 years for cancer-negative scans. The test datasets were applied to the three top-performing algorithms from the Kaggle Data Science Bowl 2017 public competition: grt123, Julian de Wit and Daniel Hammack (JWDH), and Aidence.

Cancer is a worldwide issue. Statistics show that 17 million cases of the disease were diagnosed across the globe last year alone. Depressingly, the same research suggests there will be 27.5 million new cancer cases diagnosed each year by 2040. Although the stats don't necessarily spell good news, it's important to note that diagnosis, treatment, and in turn, patient outcomes have improved significantly. If we look back at the 1970s, less than a quarter of people with the disease survived.

Aidence, an Amsterdam-based start-up applying Artificial Intelligence to the interpretation of medical images, today announced its raise of €2.25M in seed funding from notable investors Northzone, HenQ, Health Innovations, and the medical specialists of the Haaglanden hospital group. The investment will be used to strengthen its sales and technical teams and expand international reach. Aidence is improving healthcare using Computer-Aided Diagnostics, with a first application in lung cancer. Aidence's software enables faster, cheaper, and more accurate diagnoses of X-ray, MRI and CT images. The key enabling technology is Deep Learning, a revolutionary type of Artificial Intelligence that is capable of analysing medical images with human-level accuracy.