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Machine Learning for Evaluating and Diagnosing Temporomandibular Disorders

StatusRecruiting
PhaseNot applicable
Started
View on ClinicalTrials.gov ↗
Trial flagged as At Risk
Primary completion moved at least 30 days later Jun 25, 2027 → Jul 25, 2027
See other at-risk trials from En Chu Kong Hospital

Record History

See every version on ClinicalTrials.gov ↗

  1. Recruitment opened Show 6 more changesShow less
    • Primary completion pushed from Jun 25, 2027 to Jul 25, 2027
    • Study sites: 0 to 1
    • Start date changed from estimate of Oct 5, 2026 to Oct 9, 2026
    • Eligibility criteria expanded, about 15 words
    • Secondary endpoints: details revised at 2 of 2 entries
    • Study description revised
  2. Re-verified, no change to tracked fieldsv2
  3. Study First Submitted
  • Stopped: an ongoing trial was terminated, suspended or withdrawn.
  • Notable: the trial’s plan changed such as a big move in its timeline or size, a new primary endpoint, sponsor or phase, or recruiting opened or resumed.
  • Minor: routine progress and housekeeping, such as sites added or enrollment closed.
This study aims to improve clinical efficiency and effectiveness in treating Temporomandibular Disorders (TMD). The researchers will integrate machine learning technology with clinical examination data, CBCT, and MRI to identify the core underlying issues of each patient. Based on this precise, AI-assisted diagnosis, the study will implement a tailored treatment framework. The final goal is to develop predictive algorithms that can determine MRI necessity while ensuring that clinical interventions are highly targeted, efficient, and successful.
Trial Details
NCT Number NCT07771296
Lead Sponsor En Chu Kong Hospital
Collaborators: National Yang Ming Chiao Tung University
Conditions
(from sponsor)		 Temporomandibular Disorders (TMD), TMJ Disorder, TMJ Pain
Related terms Temporomandibular Joint Disorders
Enrollment 180 participants
Start Date
Primary Completion (estimated)
Study Completion (estimated)
Updated on ClinicalTrials.gov