Oral CancerNCT07509619
AI-based Physiotherapy Evaluation System for Range of Motion in Oral Cancer Patients
An observational study of Oral Cancer and AI (Artificial Intelligence), run by National Taiwan University Hospital at 1 site in Taiwan.
Trial summary
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. Read more about this study
- Who is it for?
- Ages 20 to 70 yearsHealthy volunteers can take part
- Conditions the study is for
- Oral Cancer, AI (Artificial Intelligence)
- How many people
- 20 participants
- Where
- 1 siteTaiwan
- Study dates
- 7 Apr 2026to 31 Dec 2027
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 20 to 70 years
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: The study population consists of healthy adult volunteers aged 20 to 70 years recruited through non-probability sampling. Participants without a history of trismus, head and neck cancer, or musculoskeletal conditions affecting the head, neck, or shoulder regions are eligible. All participants are capable of following instructions and performing standardized movement tasks. This population is selected to establish baseline measurement performance and to evaluate the reliability and validity of the AI-based physiotherapy evaluation system under controlled conditions.
Who may be able to join
- 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
Who cannot join
- Inability to communicate or follow instructions
- Any condition that may affect movement performance
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Taiwan1 site, 1 recruiting
- Recruiting
School and Graduate Institute of Physical Therapy, College of Medicine, National Taiwan University
Taipei, 100
Yueh-Hsia Chen, Ph.D
About this study
Official title: Validity and Reliability of an AI-based Physiotherapy Evaluation System for Oromandibular and Neck-Shoulder Range of Motion in Oral Cancer Patients
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.
Read the detailed description
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.
- Conditions
- Oral Cancer, AI (Artificial Intelligence)
- Keywords
- Oral cancer, Artificial intelligence, Maximum interincisal opening, Physiotherapy, Range of motion, Neck and shoulder
- Medical subject headings
- Mouth Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| 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. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Agreement Between AI and Manual Measurements
Agreement between AI-based and manual measurements assessed using Intraclass correlation coefficients (ICC) and Bland-Altman analysis
Measured: Baseline
Secondary outcomes (5)
Mean Absolute Error (MAE)
Average absolute difference between AI measurements and manual measurements
Measured: Baseline
Intra-rater reliability of human raters
Consistency of manual measurements by Rater 1 and Rater 2 across repeated trials using intraclass correlation coefficients (ICC)
Measured: Baselinte
Inter-rater reliability among all raters
Agreement among measurements obtained from the AI system, Rater 1, and Rater 2 will be assessed using intraclass correlation coefficients (ICC)
Measured: Baseline
Intra-rater reliability of AI system
Consistency of AI-based measurements across three repeated trials using intraclass correlation coefficients (ICC)
Measured: Baseline
Systematic measurement bias
Mean difference between AI-based and manual measurements
Measured: Baseline
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted3 Apr 2026
- Study start7 Apr 2026
- Primary completion31 May 2026 (estimated)
- Study completion31 Dec 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (2)Papers the sponsor has linked to this study.
- Other trials for Oral CancerStudies of the same condition, open and under way.
- More trials run by National Taiwan University HospitalThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case only
- Time perspective
- Cross-sectional
- Enrolment
- 20 participants (estimated)
Sponsor and investigators
- Lead sponsor
- National Taiwan University Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Yueh-Hsia Chen, Ph.D., Principal investigator, School and Graduate Institute of Physical Therapy, College of Medicine, National Taiwan University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07509619
- Sponsor's protocol ID
- 202603023RIND
- Status last verified
- Mar 2026
- First submitted
- 29 Mar 2026
- Last updated
- 13 Apr 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: Individual participant data (IPD) will not be shared due to privacy considerations and institutional regulations. Although the AI system processes de-identified keypoint data, the dataset may still contain information that could potentially be re-identified. Data sharing may be considered upon reasonable request and subject to institutional review and data protection policies.
Publications
Deb 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.
PubMed 35139022Agarwal 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.
PubMed 27900243
Study at a glance
From the registry record, last updated by the sponsor on 13 Apr 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Validity and Reliability of an AI-based Physiotherapy Evaluation System for Oromandibular and Neck-Shoulder Range of Motion in Oral Cancer Patients
- ClinicalTrials.gov ID
- NCT07509619
- Acronym / sponsor ID
- 202603023RIND
- Conditions
- Oral Cancer, AI (Artificial Intelligence)
- Keywords
- Oral cancer, Artificial intelligence, Maximum interincisal opening, Physiotherapy, Range of motion, Neck and shoulder
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 20 participants (estimated)
- Lead sponsor
- National Taiwan University HospitalUniversity, hospital or other
- Study start
- 7 Apr 2026 (actual)
- Primary completion
- 31 May 2026 (estimated)
- Study completion
- 31 Dec 2027 (estimated)
- First posted
- 3 Apr 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed description
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.
Study design
- Observational model
- Case only
- Time perspective
- Cross-sectional
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| 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. |
Who may qualify
- Ages
- 20 to 70 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- The study population consists of healthy adult volunteers aged 20 to 70 years recruited through non-probability sampling. Participants without a history of trismus, head and neck cancer, or musculoskeletal conditions affecting the head, neck, or shoulder regions are eligible. All participants are capable of following instructions and performing standardized movement tasks. This population is selected to establish baseline measurement performance and to evaluate the reliability and validity of the AI-based physiotherapy evaluation system under controlled conditions.
Key inclusion criteria (4 listed)
- 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
Key exclusion criteria (2 listed)
- Inability to communicate or follow instructions
- Any condition that may affect movement performance
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Agreement Between AI and Manual Measurements
Time frame: Baseline
Agreement between AI-based and manual measurements assessed using Intraclass correlation coefficients (ICC) and Bland-Altman analysis
5 secondary outcomes
Mean Absolute Error (MAE)
Time frame: Baseline
Intra-rater reliability of human raters
Time frame: Baselinte
Inter-rater reliability among all raters
Time frame: Baseline
Intra-rater reliability of AI system
Time frame: Baseline
Systematic measurement bias
Time frame: Baseline
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 13 Apr 2026.
- Background: PubMed 35139022Deb 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.
- Background: PubMed 27900243Agarwal 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.
- Individual participant dataSharing planned: No. Individual participant data (IPD) will not be shared due to privacy considerations and institutional regulations. Although the AI system processes de-identified keypoint data, the dataset may still contain information that could potentially be re-identified. Data sharing may be considered upon reasonable request and subject to institutional review and data protection policies.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Yueh-Hsia Chen, Ph.D. principal investigator
School and Graduate Institute of Physical Therapy, College of Medicine, National Taiwan University
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Oral Cancer and AI (Artificial Intelligence), run by National Taiwan University Hospital at 1 site in Taiwan.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 13 Apr 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Taiwan. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Yueh-Hsia Chen, Ph.D. (+886-2-33668133, yuehhsiachen@ntu.edu.tw), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Agreement Between AI and Manual Measurements (time frame: Baseline). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07509619, which also lists 2 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 13 Apr 2026.