Validity and Reliability of an AI-based Physiotherapy Evaluation System for Oromandibular and Neck-Shoulder Range of Motion in Oral Cancer Patients
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 入组人数
- 20
- 试验地点
- 1
- 主要终点
- Agreement Between AI and Manual Measurements
研究概览
简要总结
This study aims to evaluate the validity and reliability of a novel AI-based physiotherapy evaluation system for measuring oromandibular and neck-shoulder range of motion (ROM). Traditional ROM assessments rely on manual measurements, which may be influenced by rater experience and variability. The proposed AI system uses automated keypoint tracking to provide objective and standardized measurements.
In this cross-sectional study, healthy adult participants will perform standardized ROM tasks. Measurements obtained from the AI system will be compared with those from two independent raters using conventional clinical tools. Repeated measurements will be conducted to assess intra-rater and inter-rater reliability. The agreement between the AI system and human raters will be evaluated to determine the system's clinical applicability.
详细描述
This study is a cross-sectional measurement study designed to evaluate the reliability and concurrent validity of an AI-based physiotherapy evaluation system for assessing oromandibular and neck-shoulder range of motion (ROM). Participants will be healthy adults aged 20 to 70 years who meet predefined inclusion and exclusion criteria. After providing informed consent, participants will perform standardized movements, including mouth opening and cervical and shoulder ROM tasks.
Each participant will undergo three repeated measurements for each movement. ROM will be assessed using three methods: (1) an AI-based system utilizing real-time keypoint tracking and automated angle calculation, (2) manual measurement by Rater 1, and (3) independent manual measurement by Rater 2 using a goniometer or TheraBite ROM scale.
To minimize measurement bias and fatigue effects, the order of the three assessment methods will be randomized for each participant. Raters will be blinded to each other's measurements and to the AI-generated results.
The primary outcomes include inter-rater reliability and intra-rater reliability of the AI system, as well as agreement between AI-based and manual measurements. Reliability will be assessed using intraclass correlation coefficients (ICC), while agreement will be evaluated using Bland-Altman analysis and mean absolute error (MAE).
This study is expected to provide evidence supporting the clinical applicability of AI-based physiotherapy assessment tools, particularly for standardized and scalable musculoskeletal evaluations.
研究设计
- 研究类型
- Observational
- 观察模型
- Case Only
- 时间视角
- Cross Sectional
入排标准
- 年龄范围
- 20 Years 至 70 Years（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •Healthy adults aged 20 to 70 years
- •No trismus
- •No history of head, neck, or shoulder injury or surgery
- •No history of head and neck cancer-related radiotherapy or chemotherapy
排除标准
- •Inability to communicate or follow instructions
- •Any condition that may affect movement performance
研究组 & 干预措施
Healthy group
Healthy adults aged between 20 and 70 years without a history of trismus, head, neck or shoulder injury or surgery, HNC-related radiotherapy or chemoradiotherapy were recruited.
结局指标
主要结局
Agreement Between AI and Manual Measurements
时间窗: Baseline
Agreement between AI-based and manual measurements assessed using Intraclass correlation coefficients (ICC) and Bland-Altman analysis
次要结局
- Mean Absolute Error (MAE)(Baseline)
- Intra-rater reliability of human raters(Baselinte)
- Inter-rater reliability among all raters(Baseline)
- Intra-rater reliability of AI system(Baseline)
- Systematic measurement bias(Baseline)