Clinical trial NCT06629038: Estimating Jaw, Neck, and Shoulder Range of Motion Using an AI Model
Official title: A Novel Technique for Estimating Maximal Jaw Movement, Neck and Shoulder Joint Range of Motion Using an Artificial Intelligence Model
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Oral Cancer
Keywords: oral cancer
shoulder
range of motion
artificial intelligence
pose estimation
maximal mouth opening
Lead sponsor: National Taiwan University Hospital
Sponsor class: OTHER
First posted: 2024-10-08
Enrollment: 40
Sex: ALL
Minimum age (years): 20
Maximum age (years): 65
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: OTHER
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: primary care clinic, community, university

Brief summary: This observational study aims to develop an AI-based system for tracking mandibular and shoulder movements using deep learning techniques. It will compare AI-generated pose estimations with gold standard measurements to assess accuracy, particularly in patients with functional impairments from oral cancer treatment, such as trismus, spinal accessory nerve dysfunction, neck dystonia, and radiation fibrosis.

Detailed description: Due to the involvement of various structures, patients with oral cancer may experience functional impairments after treatment, such as trismus, spinal accessory nerve dysfunction, neck dystonia, radiation fibrosis, and fatigue. This observational study aims to develop an AI-based system for tracking mandibular and shoulder movements using deep learning techniques. AI-generated pose estimations will be compared with gold standard measurements: maximal mouth opening will be compared with caliper measurements, and Therabilte scale, while shoulder abduction range of motion will be compared with universal goniometer measurements. We will recruit 20 healthy adults and 20 oral cancer patients. Data on maximal mouth opening and shoulder abduction will be collected through video recordings, calipers, Therabilte scale, and universal goniometers. The videos will be analyzed using deep learning to estimate mouth opening and shoulder abduction angles. These estimates will then be compared with the gold standard measurements. The Intraclass Correlation Coefficient (ICC), Mean Absolute Error (MAE), and Coefficient of Variation (CV) will be used as performance indicators to assess and compare the reliability, accuracy, and consistency of the models.

Eligibility criteria: Inclusion Criteria:

* Healthy adults without a history of head, neck or shoulder injury or surgery, and without HNC-related radiotherapy or chemoradiotherapy
* Oral cancer patients with trismus, clinical signs of neck or shoulder joint impairment after oral cancer surgery or radiotherapy
* Age between 20 and 65 years

Exclusion Criteria:

* Could not communicate
* Had any disorder that could influence movement performance

Primary outcomes:
measure: maximal mouth opening
timeFrame: The date of enrollment
measure: maximal lateral excursion of mandible
timeFrame: The date of enrollment
measure: range of motion of shoulder abduction
timeFrame: The date of enrollment
measure: range of motion of neck
timeFrame: The date of enrollment
Secondary outcomes:
Other outcomes:
Locations:
city: Taipei
country: Taiwan
facility: School and Graduate Institute of Physical Therapy, College of Medicine, National Taiwan University
Study officials:
affiliation: School and Graduate Institute of Physical Therapy, College of Medicine, National Taiwan University
name: Yueh-Hsia Chen, PhD
role: PRINCIPAL_INVESTIGATOR
References:
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