Lee Sunghoon Ivan, Park Eunjeong, Huang Alex, Mortazavi Bobak, Garst Jordan Hayward, Jahanforouz Nima, Espinal Marie, Siero Tiffany, Pollack Sophie, Afridi Marwa, Daneshvar Meelod, Ghias Saif, Lu Daniel C, Sarrafzadeh Majid
Department of Physical Medicine & Rehabilitation, Harvard Medical School, Charlestown, MA 02129, USA; Spaulding Rehabilitation Hospital, Charlestown, MA 02129, USA; Computer Science Department, UCLA, Los Angeles, CA 90095, USA; Wireless Health Institute, UCLA, Los Angeles, CA 90095, USA.
Computer Science Department, UCLA, Los Angeles, CA 90095, USA; Wireless Health Institute, UCLA, Los Angeles, CA 90095, USA.
Med Eng Phys. 2016 May;38(5):442-9. doi: 10.1016/j.medengphy.2016.02.004. Epub 2016 Mar 9.
Lumbar spinal stenosis (LSS) is a condition associated with the degeneration of spinal disks in the lower back. A significant majority of the elderly population experiences LSS, and the number is expected to grow. The primary objective of medical treatment for LSS patients has focused on improving functional outcomes (e.g., walking ability) and thus, an accurate, objective, and inexpensive method to evaluate patients' functional levels is in great need. This paper aims to quantify the functional level of LSS patients by analyzing their clinical information and their walking ability from a 10 m self-paced walking test using a pair of sensorized shoes. Machine learning algorithms were used to estimate the Oswestry Disability Index, a clinically well-established functional outcome, from a total of 29 LSS patients. The estimated ODI scores showed a significant correlation to the reported ODI scores with a Pearson correlation coefficient (r) of 0.81 and p<3.5×10(-11). It was further shown that the data extracted from the sensorized shoes contribute most to the reported estimation results, and that the contribution of the clinical information was minimal. This study enables new research and clinical opportunities for monitoring the functional level of LSS patients in hospital and ambulatory settings.
腰椎管狭窄症（LSS）是一种与下背部椎间盘退变相关的病症。绝大多数老年人群患有LSS，且这一数字预计还会增长。LSS患者的医学治疗主要目标集中在改善功能结果（如行走能力），因此，迫切需要一种准确、客观且廉价的方法来评估患者的功能水平。本文旨在通过分析LSS患者的临床信息以及他们在使用一双传感鞋进行的10米自定步速行走测试中的行走能力，来量化其功能水平。使用机器学习算法从总共29名LSS患者中估计Oswestry功能障碍指数，这是一种临床公认的功能结果。估计的ODI分数与报告的ODI分数显示出显著相关性，皮尔逊相关系数（r）为0.81，p<3.5×10(-11)。进一步表明，从传感鞋提取的数据对报告的估计结果贡献最大，而临床信息的贡献最小。本研究为在医院和门诊环境中监测LSS患者的功能水平带来了新的研究和临床机遇。
