Big Data in Rheumatology Intelligent Data Modeling Improves the Quality of Imaging Data

Analysis of imaging data in rheumatology is a challenge. Reliability of scores is an issue for several reasons. The signal to noise ratio of most imaging techniques is rather unfavorable (too little signal in relation to too much noise). Optimal use of all available data may help increasing the credibility of imaging data, but knowledge of complicated statistical methodology and the help of skilled statisticians are required. Clinicians should appreciate the merits of sophisticated data modeling and liaise with statisticians to increase the quality of imaging results, as proper imaging studies in rheumatology imply more than a supersensitive imaging technique alone.