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AI-Assisted Versus Conventional Orthodontic Diagnosis and Treatment Planning

NCT07690774 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 120

Last updated 2026-07-08

No results posted yet for this study

Summary

This study compared an artificial intelligence-assisted orthodontic diagnostic workflow using WebCeph with a conventional clinician-based workflow.

The study included 120 new orthodontic patients treated at Ben Hafeed Dental Center in Sana'a, Yemen, between March 2024 and March 2026. For each participant, the researchers prepared a de-identified package of routine pretreatment records, including clinical findings, extraoral and intraoral photographs, panoramic and lateral cephalometric radiographs, and dental models or digital scans.

The same case package was independently assessed using both the artificial intelligence-assisted and conventional workflows. An independent panel of five expert orthodontists reviewed the cases and established the reference standard.

The main outcome was agreement with the expert panel for skeletal sagittal classification as Class I, Class II, or Class III. Other outcomes included orthodontic treatment-need grades, premolar extraction decisions, anchorage planning, appliance selection, and the time required to complete each diagnostic workflow.

Participation did not alter the routine orthodontic care provided to patients, and final clinical decisions remained the responsibility of the treating orthodontist.

Conditions

  • Malocclusion, Angle Class I
  • Malocclusion, Angle Class II
  • Malocclusion, Angle Class III

Sponsors & Collaborators

  • Ben Hafeed Dental Center

    lead OTHER

Eligibility

Min Age
12 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2024-03-03
Primary Completion
2026-03-31
Completion
2026-03-31

Countries

  • Yemen

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07690774 on ClinicalTrials.gov