MINNEAPOLIS, Dec. 1, 2016 /PRNewswire/ -- NTT DATA Services, formerly Dell Services, today announced its partnership with Imbio, which will provide algorithms for lung analytics. The algorithms are designed to further enhance automated tools to assist radiology in identifying and quantifying disease present in existing and new imaging studies. Imbio algorithms provide for fully-automated quantification and visualization of lung structure, which is key to personalized diagnosis, surgical planning and therapy response monitoring. The Unified Clinical Archive (UCA) services from NTT DATA Services include a universal imaging analytics solution that supports machine vision and neural networks to more rapidly identify the extent of disease and accelerate the identification of incidental findings for early detection of potential chronic disease.
"We are working to break down the barriers to broader use of clinically valuable quantitative imaging," said Dave Hannes, COO of Imbio. "This partnership with NTT DATA Services will help accelerate the availability and scalability in the cloud for lung analysis tools. Working with the UCA solution is directly aligned with our focus on fully-automated applications to improve productivity, while making richer, personalized patient analysis accessible to all physicians." According to Dr. Carl Fuhrman of UPMC Radiology, "The concept of 'personalized medicine' is at the forefront of current medical practice with medical decisions and treatment being tailored to the individual patient. In radiology, the descriptive terminology regarding chronic obstructive pulmonary disease, such as 'mild' or 'moderate,' are no longer acceptable to accurately assess disease. Imbio allows for accurate and reproducible quantitative measurements of COPD, providing more specific measurement of disease severity and progression over serial examinations, which is crucial to our mission of personalized medicine." The UCA platform for imaging analytics has begun the journey of building out a broad range of machine vision and artificial intelligence tools that will enable imaging diagnosticians in radiology, cardiology and oncology to profoundly increase their insights with tools that are independent of picture archive and communication system applications but are integrated into the reporting process. The result is a greater quality and value brought from enhancing the level of detailed analysis that can be quantitatively used to help understand the anatomical imaging. These results can be used in driving patient segmentation for risk and assessing the impact of care pathways to drive more personalized individual care decisions, as well as radiology's participation in population health.