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Completed

NCT Number: NCT07246018

Accuracy and Reliability of Artificial Intelligence Cephalometric Analysis Software Compared to Manual Tracing

This study compares the accuracy and reliability of artificial intelligence (AI) software for analyzing dental X-rays to the traditional manual tracing method used by dentists.

Lateral cephalometric radiographs are special X-rays of the head used in orthodontics (teeth straightening) to measure jawbone positions, tooth angles, and facial proportions. Traditionally, orthodontists manually trace these X-rays using pencil and paper to identify key landmarks and make measurements. This manual method is time-consuming and can vary between different practitioners or even when the same practitioner measures twice.

AI-based software can automatically identify these landmarks and perform measurements instantly. This study examined 40 dental X-rays to determine if the AI software (WeDoCeph) is as accurate and more reliable than manual tracing.

Each X-ray was measured twice - once manually by a trained examiner and once by AI software - at two different times (4 weeks apart). The researchers compared 15 different measurements, including 8 angles and 7 distances, to assess accuracy and reliability.

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Key information

Conditions

Cephalometry Cephalometric Analysis Orthodontic

Sex eligibility

All sexes

Study type

Observational

Primary location

Orthodontic Specialist Clinic, Kulliyyah of Dentistry

Kuantan, Pahang, 25200, Malaysia

About this study

Lateral cephalometric analysis is essential for orthodontic diagnosis and treatment planning. The traditional manual tracing method involves identifying anatomical landmarks on radiographs using pencil, ruler, and protractor, which is subjective, time-consuming, and prone to intra- and inter-observer variability.

This diagnostic accuracy study evaluated the WeDoCeph AI-based cephalometric analysis software against conventional manual tracing. The study used a comparative repeated-measures design where each radiograph was analysed by both methods at two time points (T₀ and T₁, separated by 4 weeks) to assess both accuracy and reliability.

Sample size calculation was based on 95% power and a 0.05 significance level, resulting in 40 lateral cephalometric radiographs. All measurements included angular parameters (SNA, SNB, ANB, FMPA, MMPA, UIA, LIA, IIA) and linear parameters (A-N perpendicular, POG-N perpendicular, ANS-Me, SN, UFH, MxPI, MnPI).

Paired T-Test will be employed as the statistical analysis method for comparisons and Intraclass Correlation Coefficient (ICC) for reliability assessment. The study aimed to determine whether AI-based cephalometric analysis provides sufficient accuracy and superior reliability for clinical application in orthodontic practice.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • Pretreatment/post-treatment lateral cephalometric radiographs
  • High-quality cephalograms with visible anatomical landmarks

Exclusion criteria

  • Patients with surgical rigid fixations, orthodontic appliances and dental prostheses visible on radiographs
  • Very poor quality/diagnostically unacceptable radiographs
  • Patients with syndromes or with craniofacial deformities

Treatment and study plan

Manual Cephalometric Tracing

Diagnostic Test

Conventional manual cephalometric analysis performed by trained examiner using traditional tracing technique. Lateral cephalometric radiographs are hand-traced in a darkened room using a view box for transillumination. A 25cm x 18cm radiographic film is used as the base, with a 21cm x 16cm matte acetate tracing paper taped over it. Hard and soft tissue cephalometric landmarks are manually identified and traced using a 0.3mm 2HB pencil. Angular measurements are obtained using a protractor, and linear measurements using a ruler. All 15 cephalometric measurements (8 angular: SNA, SNB, ANB, FMPA, MMPA, UIA, LIA, IIA; and 7 linear: A-N perpendicular, POG-N perpendicular, ANS-Me, SN, UFH, MxPI, MnPI) are calculated manually. Each radiograph is traced and analyzed twice at 4-week intervals by the same examiner to assess intra-examiner reliability.

AI-Based Cephalometric Analysis (WeDoCeph Software)

Diagnostic Test

Automated cephalometric analysis using WeDoCeph artificial intelligence-based software. Digital lateral cephalometric radiographs are imported as high-quality JPEG images into the software platform. The AI system automatically identifies and traces cephalometric landmarks using deep learning algorithms, then instantly generates all measurements based on the predefined parameters. The same 15 cephalometric measurements obtained in manual tracing (8 angular: SNA, SNB, ANB, FMPA, MMPA, UIA, LIA, IIA; and 7 linear: A-N perpendicular, POG-N perpendicular, ANS-Me, SN, UFH, MxPI, MnPI) are automatically calculated by the software. Each radiograph is analyzed twice at 4-week intervals using the previously uploaded digital images to assess reproducibility and consistency of the AI system. No manual landmark identification or measurement calculation is required.

Primary outcomes

  1. Intraclass Correlation Coefficient (ICC) for repeated manual measurements

    Time frame: Baseline (T₀) and 4 weeks later (T₁)

    ICC calculated for all 15 cephalometric measurements (8 angular and 7 linear) performed manually at two time points to assess intra-examiner reliability

  2. Intraclass Correlation Coefficient (ICC) for repeated AI measurements

    Time frame: Baseline (T₀) and 4 weeks later (T₁)

    ICC calculated for all 15 cephalometric measurements performed by WeDoCeph software at two time points to assess consistency

  3. Mean differences between manual and AI-based measurements at T₀

    Time frame: Baseline (T₀)

    Paired T-Test comparison of all 15 measurements between manual tracing and AI analysis at initial time point

  4. Mean differences between manual and AI-based measurements at T₁

    Time frame: 4 weeks

    Paired T-Test comparison of all 15 measurements between manual tracing and AI analysis at 4-week time point

Secondary outcomes

  1. Angular Measurements

    Time frame: Baseline (T₀) and 4 weeks (T₁)

    Comparison of angular cephalometric measurements between methods

  2. Linear Measurements

    Time frame: Baseline (T₀) and 4 weeks (T₁)

    Comparison of linear cephalometric measurements between methods

  3. Inter-examiner Reliability

    Time frame: During calibration phase

    10% of radiographs were analyzed by three examiners to ensure inter-examiner agreement

Sponsors and collaborators

Lead sponsor

International Islamic University Malaysia

Other

Registry information

Official study title

The Accuracy and Reliability of Orthodontic Cephalometry Analysis Using Web-Based Artificial Intelligence Program

Important dates

Study start
2023
Primary completion
2023
Study completion
2023
First posted
Nov 24, 2025
Registry last updated
Nov 24, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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