Clinical trial · Observational
AI-based Physiotherapy Evaluation System for Range of Motion in Oral Cancer Patients
Validity and Reliability of an AI-based Physiotherapy Evaluation System for Oromandibular and Neck-Shoulder Range of Motion in Oral Cancer Patients
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
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.
Conditions
Conditions (2)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| AI (Artificial Intelligence) | — | UNRESOLVED | — |
| Oral Cancer | Malignant Lip and Oral Cavity Neoplasm | CURATED_BROADER | 0.80 |
Interventions
Interventions (0)
Data not yet available
Design
Arms and outcomes
Arms (1)
- label
- Healthy group
- description
- 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.
Primary outcomes (1)
- measure
- Agreement Between AI and Manual Measurements
- timeFrame
- Baseline
- description
- Agreement between AI-based and manual measurements assessed using Intraclass correlation coefficients (ICC) and Bland-Altman analysis
Secondary outcomes (5)
- measure
- Mean Absolute Error (MAE)
- timeFrame
- Baseline
- description
- Average absolute difference between AI measurements and manual measurements
- measure
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 20 Years
- Maximum age
- 70 Years
Show eligibility criteria text
Inclusion Criteria: * 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 Exclusion Criteria: * Inability to communicate or follow instructions * Any condition that may affect movement performance
References
Publications (2)
- BACKGROUNDDeb S, Islam MF, Rahman S, Rahman S. Graph Convolutional Networks for Assessment of Physical Rehabilitation Exercises. IEEE Trans Neural Syst Rehabil Eng. 2022;30:410-419. doi: 10.1109/TNSRE.2022.3150392. Epub 2022 Feb 23. PMID 35139022
- BACKGROUNDAgarwal P, Shiva Kumar HR, Rai KK. Trismus in oral cancer patients undergoing surgery and radiotherapy. J Oral Biol Craniofac Res. 2016 Nov;6(Suppl 1):S9-S13. doi: 10.1016/j.jobcr.2016.10.004. Epub 2016 Oct 22. PMID 27900243