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.

拟议的项目结合了来自大型临床磁共振成像(MRI)数据集的基于机器学习的新方法。这些方法结合了高水平和低水平的成像特征，包括椎间盘的位置、形状和强度。此外，这些方法将圆盘结构建模为马尔可夫链，以实现邻域信息的利用。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、患者病史和体检结果。这些特征的可用性为下腰痛的诊断提供了信息。这些方法产生了不偏不倚和可重复的解释。这项技术能够提供标准化、无偏见和可重复性的MRI解释。许多人受到腰痛的影响，这是与工作相关的残疾背后最常见的原因。它是一种突出的慢性病，对人们的生活造成了重大破坏。据估计，美国每年至少花费500亿美元用于下腰痛患者的诊断和相关康复。此外，相关的个体化治疗和康复费用很高，通常需要获得负责支付医疗费用的保险提供者的特别预先批准才能接受治疗。目前临床上批准的下腰痛诊断最常见的标准是MRI测试，而MRI的诊断解释具有高度的主观性。所有随后的治疗建议都是基于主观报告。这项技术或许能够提供基于标准化方案的MRI解释，将显着影响治疗计划的结果，并最大限度地减少过度治疗。