Computerized Radiographic Outcomes for Rheumatoid Hands

描述(申请人提供)：类风湿性关节炎(RA)是一种非常痛苦的疾病，具有巨大的社会影响。近1%的人口患有类风湿性关节炎，北美医疗系统每年因关节炎造成的成本估计为10亿美元。症状包括轻微的不适和疼痛，以及随着疾病的发展到末期关节功能的丧失。这一巨大的医疗保健问题最好通过处方和有效疗法的开发来解决。为了评估这些治疗方法，需要高度准确和可重复性的方法来量化疾病的状态。目前，通过使用半定量的主观评分系统，手部胶片的放射学评估被用于评估疾病进展。然而，这些方法是主观的，并受到显著的读者差异的影响。此外，对专门培训的需要使这些系统成本高昂，难以广泛实施。目前还没有真正定量的方法来评估受影响关节的关节炎进展情况。为了解决这一需求，我们建议将复杂的图像处理、多变量分析、神经网络和回归树方法应用到手持放射成像中。我们将对类风湿性关节炎引起的放射学上可见的结构变化进行定量和系统的研究。这项工作将提供以前无法获得的客观和疾病敏感的RA进展的放射学结果指标。其结果将是一个基于计算机的系统，具有更高的疾病敏感度，将导致更准确的评估和适当的治疗处方。这项工作将对减轻这种使人衰弱的疾病的影响发挥重要作用。

DESCRIPTION (provided by applicant): Rheumatoid arthritis (RA) is a very painful condition with tremendous societal impact. Nearly one percent of the population suffers from RA and the annual cost to the North American healthcare system from arthritis in general has been estimated at $64 billion. Symptoms range from mild discomfort and pain to loss of joint function as the disease progresses to its end-stage. This enormous healthcare problem is best met by the prescription and development of effective therapies. In order to evaluate these therapies, highly accurate and reproducible methods are required to quantify the state of the disease. Radiographic evaluation of hand films is currently used to assess disease progression though the use of semi-quantitative subjective scoring systems. These methods, however, are subjective and suffer from significant reader variation. In addition, the need for specialized training makes the systems costly and difficult to implement on a widespread basis. There is currently no truly quantitative method to assess arthritis progression in the affected joints. To address this need we propose to apply sophisticated image processing, multivariate analysis, neural networks, and regression tree methods to hand radiography. We will perform a quantitative and systematic study of radiographically visible structural changes due to RA. This work will provide previously unavailable objective and disease sensitive radiographic outcome measures of RA progression. The result will be a computer-based system with improved disease sensitivity, which will lead to more accurate evaluation and appropriate prescription of therapies. This work will play a major role towards alleviating the effects of this debilitating disease.