Estimating Jaw, Neck, and Shoulder Range of Motion Using an AI Model

Phase

N/A

Condition

Oral Cancer

Treatment

observation alone

Clinical Study ID

NCT06629038

202403103RINA

Ages 20-65
All Genders
Accepts Healthy Volunteers

Study 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.

Eligibility Criteria

Inclusion

Inclusion Criteria:

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

Exclusion

Exclusion Criteria:

Could not communicate
Had any disorder that could influence movement performance

Study Design

observation alone

December 05, 2024

August 31, 2025

Study 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.

Connect with a study center

School and Graduate Institute of Physical Therapy, College of Medicine, National Taiwan University
Taipei, 100
Taiwan
Active - Recruiting

Not the study for you?

Let us help you find the best match. Sign up as a volunteer and receive email notifications when clinical trials are posted in the medical category of interest to you.