I-Corps: Standardized MRI Interpretation for Low Back Pain Diagnosis

I-Corps：用于腰痛诊断的标准化 MRI 解读

• 批准号: 1338960
• 负责人: Vipin Chaudhary
• 金额: $ 5万
• 依托单位: SUNY at Buffalo
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-05-01 至 2013-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1338960&HistoricalAwards=false
• 关键词: Corps; Standardized; MRI; Interpretation; Low

The proposed project incorporates novel machine learning based methods from a large clinical Magnetic Resonance Imaging (MRI) dataset. These methods incorporate high- and low-level imaging features including intervertebral disc location, shape, and intensity. Moreover, these methods model the disc structure as a Markov chain to enable neighborhood information utilization. In addition to the imaging features, these methods utilize patient meta-data including patient age, height, weight, pain and disability score, patient history, and physical exam findings. The availability of these features provides information for the diagnosis of low back pain. These methods generate unbiased and reproducible interpretation. This technology is capable of providing standardized, unbiased, and reproducible MRI interpretation. Many people are affected by low back pain and it is the most common reason behind job-related disability. It is a prominent chronic disease that causes major disruption in people's lives. An annual estimate of at least $50 billion is spent in the United States on diagnosis and related rehabilitation of low back pain patients. Moreover, the associated individualized treatment and rehabilitation cost is significant and often requires special pre-approval to undergo treatment from the insurance providers responsible for paying for health care costs. The most common current clinically approved standard for low back pain diagnosis is MRI testing and the diagnostic interpretation of MRIs is highly subjective. All subsequent therapeutic recommendations are based on the subjective report. This technology may be able to provide the MRI interpretation based on a standardized protocol will significantly impact the treatment plan outcome and minimize overtreatment.

提出的项目结合了来自大型临床磁共振成像（MRI）数据集的新型基于机器学习的方法。这些方法结合了高级和低级成像功能，包括椎间盘位置，形状和强度。此外，这些方法将圆盘结构建模为马尔可夫链，以启用邻里信息利用。除了成像功能外，这些方法还利用患者元数据，包括患者年龄，身高，体重，疼痛和残疾评分，患者病史和身体检查结果。这些功能的可用性提供了诊断下背痛的信息。这些方法产生了公正和可重复的解释。该技术能够提供标准化，无偏见和可再现的MRI解释。许多人受到下腰痛的影响，这是与工作有关的残疾背后最常见的原因。这是一种杰出的慢性疾病，会导致人们的生活严重破坏。在美国，每年至少估算500亿美元，用于诊断和相关的腰痛患者的康复。此外，相关的个性化治疗和康复成本很大，通常需要特别预先批准才能接受负责支付医疗保健费用的保险提供者的治疗。当前最常见的临床认可的腰痛诊断标准是MRI测试，MRI的诊断解释是高度主观的。随后的所有治疗建议均基于主观报告。该技术可能能够基于标准化协议提供MRI解释，将显着影响治疗计划的结果并最大程度地减少过度处理。