Heart disease and cancer are the leading causes of death in the United States, and it’s increasingly understood that they share common risk factors, including tobacco use, diet, blood pressure, and obesity. Thus, a diagnostic tool that could screen for cardiovascular disease while a patient is already being screened for cancer, has the potential to expedite a diagnosis, accelerate treatment, and improve patient outcomes. 

In research published today in Nature Communications, a team of engineers from Rensselaer Polytechnic Institute and clinicians from Massachusetts General Hospital developed a deep learning algorithm that can help assess a patient’s risk of cardiovascular disease with the same low-dose computerized tomography (CT) scan used to screen for lung cancer. This approach paves the way for more efficient, more cost-effective, and lower radiation diagnoses, without requiring patients to undergo a second CT scan. 

“In this paper, we demonstrate very good performance of a deep learning algorithm in identifying patients with cardiovascular diseases and predicting their mortality risks, which shows promise in converting lung cancer screening low-dose CT into a dual screening tool,” said Pingkun Yan, an assistant professor of biomedical engineering and member of the Center for Biotechnology and Interdisciplinary Studies (CBIS) at Rensselaer.

Numerous hurdles had to be overcome in order to make this dual screening possible. Low-dose CT images tend to have lower image quality and higher noise, making the features within an image harder to see. Using a large dataset from the National Lung Screening Trial (NLST), Yan and his team used data from more than 30,000 low-dose CT images to develop, train, and validate a deep learning algorithm capable of filtering out unwanted artifacts and noise, and extracting features needed for diagnosis. Researchers validated the algorithm using an additional 2,085 NLST images.

The Rensselaer team also partnered with Massachusetts General Hospital, where researchers were able to test this deep learning approach against state-of-the-art scans and the expertise of the hospital’s radiologists. The Rensselaer-developed algorithm, Yan said, not only proved to be highly effective in analyzing the risk of cardiovascular disease in high-risk patients using low-dose CT scans, but it also proved to be equally effective as radiologists in analyzing those images. In addition, the algorithm closely mimicked the performance of dedicated cardiac CT scans when it was tested on an independent dataset collected from 335 patients at Massachusetts General Hospital.

“This innovative research is a prime example of the ways in which bioimaging and artificial intelligence can be combined to improve and deliver patient care with greater precision and safety,” said Deepak Vashishth, the director of CBIS.

Yan was joined in this work by Ge Wang, an endowed chair professor of biomedical engineering at Rensselaer and fellow member of CBIS. The Rensselaer team was joined by Dr. Mannudeep K. Kalra, an attending radiologist at Massachusetts General Hospital and professor of radiology with Harvard Medical School. This research was funded by the National Institutes of Health National Heart, Lung, and Blood Institute. 

The post Deep Learning Enables Dual Screening for Cancer and Cardiovascular Disease appeared first on Artificial Intelligence.

A mobile data handling concept developed by Germany-based specialist ViGEM should, according to the company, make 24/7 utilization of test fleets for ADAS and AD validation driving feasible.

The system is based on the fast, uncomplicated exchange and shipment of robust removable data storage devices independent from the vehicle under test. As ViGEM’s CEO, Markus Trauth, explained, “Thanks to the robustness of our removable data storages and the mobile data handling concept, our customers are able to reduce the downtime of their vehicles to a minimum compared to other data transfer methods. Test fleets can thus be utilized almost 24/7, which holds significant potential for cost savings for ViGEM customers.”

In practice this means that large amounts of test data can be made quickly available to manufacturers’ development departments while testing is still ongoing. Working in cooperation with OEMs, ViGEM says it has developed a suite of integrated hardware and software components (Car Communication Analyzer – CCA) for the efficient validation of advanced driving functions. By ensuring seamless interaction between elements such as dataloggers, robust storage media and fast copy stations, featuring transfer rates of up to 50Gb/s, maximum data reliability and security are ensured.

For example, the SSDs installed in its CCA 9010 solution, with up to 64TB storage capacity and data rates of up to 25Gb/s, can continuously record raw data for approximately eight hours. Their shock- and vibration-tested metal housings can withstand even the highest mechanical stresses and allow operating temperatures from -20°C to +65°C, meaning they can be removed from vehicles and transported safe in the knowledge the data they contain is secure.

The post Mobile big data storage solution enables 24/7 AD and ADAS testing appeared first on Artificial Intelligence.