基于人体运动分析技术的人工智能盆底优化训练系统的临床应用疗效研究

Clinical efficacy of artificial intelligence-based pelvic floor optimization training system using human body motion analysis technique

摘要

目的 将基于人体运动分析技术的人工智能（AI）盆底优化训练系统应用在盆底康复的患者中，辅助医师对患者进行指导，提高医师工作效能，提升患者自主训练效果。方法 选取2024年9月-2025年12月于复旦大学附属上海市第五人民医院收治的60例膀胱过度活动症（OAB）且伴有急迫性尿失禁（UUI）患者作为研究对象，随机将其分为常规医师指导组（n=30）和AI系统指导组（n=30）。常规医师指导组跟随医师进行传统盆底优化训练，AI系统指导组采用基于人体运动分析技术的AI盆底优化训练系统进行盆底训练。比较两组患者治疗6周后的国际尿失禁咨询委员会尿失禁问卷（ICI-Q-SF）、尿失禁生活质量量表（I-QoL）、膀胱过度活动症症状评分（OABSS）、感知膀胱症状量表（PPBC）评分以及盆底肌功能（前静息阶段、后静息阶段、Ⅰ类肌阶段、Ⅱ类肌阶段及耐力测试阶段的肌电值）、测定尿量、平均尿流率（Qavg）、最大尿流率（Qmax）的变化情况。结果 治疗6周后，两组患者的ICI-Q-SF、I-QoL、OABSS、PPBC评分、Ⅰ类肌阶段肌电值、Qavg及Qmax均较治疗前有显著改善（P＜0.05）；治疗6周后，AI系统指导组I-QoL评分显著高于常规医师指导组[81.82（79.55，84.38）分对比77.84（75.00，79.55）分]，差异具有统计学意义（P＜0.05）。与常规医师指导组相比，AI系统指导组患者前静息阶段、后静息阶段、Ⅰ类肌阶段及耐力测试阶段的肌电值均显著改善，差异具有统计学意义（P＜0.05）。结论 基于人体运动分析技术的AI盆底优化训练系统不仅可改善患者的盆底肌功能、尿流动力学指标及膀胱相关症状，显著提高患者的生活质量，还能够辅助医生精准指导患者完成“贇式盆底训练”，提升自主训练效果与康复效率，减少对专业医师的依赖且安全性高，具有良好的临床应用与推广价值。

Abstract

Objective To apply an artificial intelligence (AI)-based pelvic floor optimization training system using human motion analysis technique in patients undergoing pelvic floor rehabilitation, so as to assist clinicians in providing guidance, improving clinical efficiency, and enhancing the efficacy of patients' independent training. Methods A total of 60 patients with overactive bladder (OAB) complicated with urgent urinary incontinence (UUI) treated in our hospital during Sep. 2024 and Dec. 2025 were enrolled and randomly divided into the routine guidance group (n=30) and AI guidance group (n=30). The routine guidance group received conventional pelvic floor optimization training under clinicians' instruction, while the AI guidance group underwent training with the AI-based pelvic floor optimization system using human motion analysis. After 6 weeks of intervention, outcomes were compared between the two groups, including the international consultation on incontinence questionnaire-short form (ICI-Q-SF), incontinence quality of life scale (I-QoL), overactive bladder symptom score (OABSS), patient's perception of bladder condition (PPBC) score, pelvic floor muscle function (myoelectric values in the pre-resting, post-resting, type Ⅰ muscle, type Ⅱ muscle, and endurance test phases), urine output, average flow rate (Qavg), and maximum flow rate (Qmax). Results After 6 weeks of treatment, the ICI-Q-SF, I-QoL, OABSS, PPBC score, type I muscle myoelectric value, Qavg and Qmax were significantly improved in both groups compared with baseline (P<0.05). After 6 weeks of intervention, the I-QoL score was significantly higher in the AI guidance group than in the routine guidance group [81.82 (79.55, 84.38) vs. 77.84 (75.00, 79.55)], with significant difference (P<0.05). Compared with the routine guidance group, the AI guidance group showed significant improvements in myoelectric values in the pre-resting, post-resting, type I muscle and endurance testing phases (P<0.05). Conclusion The AI-based pelvic floor optimization training system can effectively assist clinicians in guiding patients to complete pelvic floor optimization training, improve rehabilitation efficiency and efficacy of independent exercise, and reduce dependence on specialized clinicians, which exhibits favorable safety.

关键词

膀胱过度活动症 / AI盆底优化训练系统 / 盆底康复 / 人体运动分析

Key words

overactive bladder / AI-based pelvic floor optimization training system / pelvic floor rehabilitation / human motion analysis

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基金资助

区级综合（专科）医院中西医结合专科能力提升项目(QJZXYJK-202402)