BioClinica, a global CRO specializing in technology-enhanced solutions for clinical trials, announced the successful completion of the first phase of a big data research project to develop novel algorithms for the automated analysis of medical images in clinical trials. This work was made possible by a strategic partnership with the Center for Dynamic Data Analytics (CDDA), a National Science Foundation (NSF) sponsored cooperative research center focused on developing scalable methods for handling large amounts of complex industry-related data.
The BioClinica-CDDA project is part of the National Big Data and Research and Development Initiative, aimed at addressing the challenges and opportunities of Big Data and highlighted by the Obama Administration as a high-impact collaboration promoting "Data to Knowledge to Action."
Under the research project Quantitative Tissue Assessment, scientific experts from BioClinica and Rutgers University worked together to define, direct and develop important quantitative tools for the clinical assessment of new therapies for muscle, liver and spleen diseases. These tools enable the extraction of quantitative data from medical images with increased automation and precision. The quantification of muscle tissue is becoming an increasingly important measurement in aging populations for assessing sarcopenia and other musculoskeletal diseases. Additionally, tools for the evaluation of liver and spleen are needed for clinical trials for non-alcoholic steato-hepatitis (NASH) and for a number of orphan diseases.
"These types of advances are the first steps in the application of big data that are being brought to bear on the diagnosis and development of new drugs in areas of unmet medical need," said Colin Miller, Ph.D., senior vice president of medical affairs at BioClinica. The methods developed here will be put into production to facilitate the analysis of large imaging datasets that arise from ongoing global, multi-site clinical trials.
BioClinica and the CDDA have additional projects in the works, including the development of scalable algorithms for automated image analysis for additional therapeutic areas and unmet medical needs.
