TY - JOUR AU - Koyama, Takafumi AU - Matsui, Ryota AU - Yamamoto, Akiko AU - Yamada, Eriku AU - Norose, Mio AU - Ibara, Takuya AU - Kaburagi, Hidetoshi AU - Nimura, Akimoto AU - Sugiura, yuuta AU - Saito, Hideo AU - Okawa, Atsushi AU - Fujita, Koji PY - 2022 DA - 2022/10/3 TI -手指运动的高维分析和非接触式脊髓病变筛查诊断病例对照研究乔- JMIR生物医学Eng SP - e41327六世- 7 - 2 KW -颈脊髓病KW -脊髓病KW -脊髓疾病KW -脊髓失调KW -神经系统障碍KW -神经系统疾病KW -笨拙KW -筛选KW - 10秒手柄和发布测试KW -机器学习KW -腕管综合症KW -跳跃运动KW -临床信息学千瓦系统验证KW -扫描系统KW -传感器KW -模型KW -诊断KW -诊断千瓦高维分析KW -运动传感器KW -运动检测KW -高维数据分析KW -高维统计AB -背景:颈脊髓病(CM)会导致手部笨拙等症状，通常需要手术治疗。CM的筛查和早期诊断很重要，因为有些患者不知道自己的早期症状，只有在病情变得严重后才去看外科医生。10秒的握放测试通常用于检查CM的存在。该检测方法简单，但如果能客观评价CM特有的运动变化，则对筛查更有用。此前的一项研究利用非接触式传感器Leap Motion分析了10秒握放测试中的手指运动，并开发了一种利用机器学习的高灵敏度和特异性诊断CM的系统。然而，以往的研究存在局限性，系统记录的参数很少，不能将CM与其他手部疾病区分开来。目的:本研究旨在建立一种具有更高敏感性和特异性的CM诊断系统，并将CM与常见的手部疾病腕管综合征(carpal tunnel syndrome, CTS)区分开来。然后，我们用改进的Leap Motion来验证系统，该系统可以记录每个手指的关节。 Methods: In total, 31, 27, and 29 participants were recruited into the CM, CTS, and control groups, respectively. We developed a system using Leap Motion that recorded 229 parameters of finger movements while participants gripped and released their fingers as rapidly as possible. A support vector machine was used for machine learning to develop the binary classification model and calculated the sensitivity, specificity, and area under the curve (AUC). We developed two models, one to diagnose CM among the CM and control groups (CM/control model), and the other to diagnose CM among the CM and non-CM groups (CM/non-CM model). Results: The CM/control model indexes were as follows: sensitivity 74.2%, specificity 89.7%, and AUC 0.82. The CM/non-CM model indexes were as follows: sensitivity 71%, specificity 72.87%, and AUC 0.74. Conclusions: We developed a screening system capable of diagnosing CM with higher sensitivity and specificity. This system can differentiate patients with CM from patients with CTS as well as healthy patients and has the potential to screen for CM in a variety of patients. SN - 2561-3278 UR - https://biomedeng.www.mybigtv.com/2022/2/e41327 UR - https://doi.org/10.2196/41327 DO - 10.2196/41327 ID - info:doi/10.2196/41327 ER -