基于自我实施超声的盆底肌训练生物反馈中盆底肌收缩自动评估算法。
Pelvic floor muscle contraction automatic evaluation algorithm for pelvic floor muscle training biofeedback using self-performed ultrasound.
机构信息
Department of Gerontological Nursing / Wound Care Management, Graduate School of Medicine, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo, Japan.
Department of Nursing, Yokohama City University, 3-9, Fukuura, Kanazawa-ku, Yokohama-shi, Kanagawa, Japan.
出版信息
BMC Womens Health. 2024 Apr 4;24(1):219. doi: 10.1186/s12905-024-03041-y.
INTRODUCTION
Non-invasive biofeedback of pelvic floor muscle training (PFMT) is required for continuous training in home care. Therefore, we considered self-performed ultrasound (US) in adult women with a handheld US device applied to the bladder. However, US images are difficult to read and require assistance when using US at home. In this study, we aimed to develop an algorithm for the automatic evaluation of pelvic floor muscle (PFM) contraction using self-performed bladder US videos to verify whether it is possible to automatically determine PFM contraction from US videos.
METHODS
Women aged ≥ 20 years were recruited from the outpatient Urology and Gynecology departments of a general hospital or through snowball sampling. The researcher supported the participants in their self-performed bladder US and videos were obtained several times during PFMT. The US videos obtained were used to develop an automatic evaluation algorithm. Supervised machine learning was then performed using expert PFM contraction classifications as ground truth data. Time-series features were generated from the x- and y-coordinate values of the bladder area including the bladder base. The final model was evaluated for accuracy, area under the curve (AUC), recall, precision, and F1. The contribution of each feature variable to the classification ability of the model was estimated.
RESULTS
The 1144 videos obtained from 56 participants were analyzed. We split the data into training and test sets with 7894 time series features. A light gradient boosting machine model (Light GBM) was selected, and the final model resulted in an accuracy of 0.73, AUC = 0.91, recall = 0.66, precision = 0.73, and F1 = 0.73. Movement of the y-coordinate of the bladder base was shown as the most important.
CONCLUSION
This study showed that automated classification of PFM contraction from self-performed US videos is possible with high accuracy.
简介
在家庭护理中进行连续的骨盆底肌肉训练（PFMT）需要进行非侵入性的生物反馈。因此，我们考虑在成年女性中使用手持式超声设备对膀胱进行自我执行的超声（US）。然而，US 图像难以阅读，并且在家中使用 US 时需要协助。在这项研究中，我们旨在开发一种自动评估使用自我执行的膀胱 US 视频的骨盆底肌肉（PFM）收缩的算法，以验证是否可以从 US 视频中自动确定 PFM 收缩。
方法
从综合医院的泌尿科和妇科门诊或通过雪球抽样招募年龄≥20 岁的女性。研究人员支持参与者进行自我执行的膀胱 US，并且在 PFMT 期间多次获得视频。获得的 US 视频用于开发自动评估算法。然后，使用专家 PFM 收缩分类作为地面实况数据进行监督机器学习。从包括膀胱基底在内的膀胱区域的 x 和 y 坐标值生成时间序列特征。最后，根据准确性、曲线下面积（AUC）、召回率、精确度和 F1 对模型进行评估。估计了每个特征变量对模型分类能力的贡献。
结果
从 56 名参与者中获得了 1144 个视频。我们将数据分为训练集和测试集，其中包含 7894 个时间序列特征。选择了一个轻梯度提升机模型（Light GBM），最终模型的准确性为 0.73，AUC=0.91，召回率=0.66，精确度=0.73，F1=0.73。膀胱基底的 y 坐标运动被证明是最重要的。
结论
这项研究表明，使用自我执行的 US 视频自动分类 PFM 收缩是可能的，并且具有很高的准确性。
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