AI-Assisted Versus Conventional Orthodontic Diagnosis and Treatment Planning

Overview

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.

This was a prospective paired diagnostic-agreement study conducted at Ben Hafeed Dental Center, Sana'a, Yemen, from March 2024 to March 2026. A total of 120 eligible new orthodontic patients were included. Each participant contributed one standardized, de-identified pretreatment case package. Every case was evaluated using both an artificial intelligence-assisted workflow and a conventional clinician-based workflow, allowing direct within-case comparison. The artificial intelligence-assisted workflow used WebCeph Premium, software version 2.0.3 (AssembleCircle Corp., Seongnam-si, Republic of Korea). The lateral cephalometric radiograph was uploaded to the software for automated anatomical landmark identification and generation of angular and linear cephalometric measurements. The original automated landmarks and measurements were recorded without manual correction. The reviewer interpreted the automated findings together with the remaining standardized pretreatment records and entered the required diagnostic and treatment-planning decisions. For the conventional workflow, two calibrated orthodontists independently reviewed each case using routine clinical, photographic, radiographic, cephalometric, and dental-model assessment. Conventional cephalometric evaluation was performed using manual landmark identification through a local Dolphin Imaging-based workflow. When the two conventional orthodontists disagreed on an endpoint, a third calibrated orthodontist adjudicated the disputed decision. The reference standard was established by an independent panel of five experienced orthodontists. Each expert independently reviewed the de-identified cases while blinded to the artificial intelligence-assisted and conventional workflow results. Reference decisions were established through majority agreement, with additional review and structured adjudication when required. The primary endpoint was skeletal sagittal classification, categorized as Class I, Class II, or Class III. Secondary endpoints included the Index of Orthodontic Treatment Need Dental Health Component, the Index of Orthodontic Treatment Need Aesthetic Component, premolar extraction decision, anchorage-plan category, and primary appliance modality. Workflow duration was measured from the beginning of case assessment until all required decisions had been entered. The study included 71 females and 49 males. The mean participant age was 20.3 years, with a standard deviation of 3.9 years and a range of 14 to 33 years. The protocol was approved by the Yemen Dental Association under approval number 207. Written informed consent was obtained from adult participants. For minors, written parental or legal-guardian permission and participant assent were obtained.