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

NCT07246018 | Completed

• 2 other identifiers: IREC 2023-045CHAIN 22-001-0001
• Study Type: observational
• Target: 40
• Locations: 1 country
• Sites: 1

Brief Summary

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.

Conditions

Cephalometry; Orthodontic; Cephalometric Analysis

Outcome Measures

Primary Outcomes (4)

• Intraclass Correlation Coefficient (ICC) for repeated manual measurements

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

  Baseline (T₀) and 4 weeks later (T₁)

• Intraclass Correlation Coefficient (ICC) for repeated AI measurements

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

  Baseline (T₀) and 4 weeks later (T₁)

• Mean differences between manual and AI-based measurements at T₀

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

  Baseline (T₀)

• Mean differences between manual and AI-based measurements at T₁

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

  4 weeks

Secondary Outcomes (3)

• Angular Measurements

  Baseline (T₀) and 4 weeks (T₁)

• Linear Measurements

  Baseline (T₀) and 4 weeks (T₁)

• Inter-examiner Reliability

  During calibration phase

Study Arms (1)

Orthodontic Patients with Lateral Cephalometric Radiographs

Lateral cephalometric radiographs from 40 orthodontic patients collected between January 2023 and June 2023 from the Orthodontic Specialist Clinic. Each radiograph was analyzed using both manual tracing and AI-based software (WeDoCeph) at two time points (initial and 4 weeks later)

Diagnostic Test: Manual Cephalometric Tracing; Diagnostic Test: AI-Based Cephalometric Analysis (WeDoCeph Software)

Interventions

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.

Orthodontic Patients with Lateral Cephalometric Radiographs

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.

Orthodontic Patients with Lateral Cephalometric Radiographs

Eligibility Criteria

• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

The study population consisted of radiographs from orthodontic patients at various stages of treatment, including both pretreatment (initial diagnostic) and post-treatment radiographs. All radiographs were high-quality digital or digitized lateral cephalograms suitable for landmark identification and measurement. Patients with surgical rigid fixations, orthodontic appliances visible on radiographs, or dental prostheses were excluded to ensure clear visualization of anatomical landmarks. Additionally, radiographs of very poor quality or from patients with diagnosed syndromes or craniofacial deformities were excluded to maintain consistency in anatomical structure assessment. The unit of analysis is the cephalometric radiograph rather than individual patients, as each radiograph represents a single diagnostic assessment.

You may qualify if:

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

You may not qualify if:

• 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

Sponsors & Collaborators

• International Islamic University Malaysia: lead

Study Sites (1)

Orthodontic Specialist Clinic, Kulliyyah of Dentistry

Kuantan, Pahang, 25200, Malaysia

Location

Related Publications (3)

• Alqahtani H. Evaluation of an online website-based platform for cephalometric analysis. J Stomatol Oral Maxillofac Surg. 2020 Feb;121(1):53-57. doi: 10.1016/j.jormas.2019.04.017. Epub 2019 May 3.

  PMID: 31059836 RESULT

• Kazimierczak W, Gawin G, Janiszewska-Olszowska J, Dyszkiewicz-Konwinska M, Nowicki P, Kazimierczak N, Serafin Z, Orhan K. Comparison of Three Commercially Available, AI-Driven Cephalometric Analysis Tools in Orthodontics. J Clin Med. 2024 Jun 26;13(13):3733. doi: 10.3390/jcm13133733.

  PMID: 38999299 RESULT

• Lee JH, Yu HJ, Kim MJ, Kim JW, Choi J. Automated cephalometric landmark detection with confidence regions using Bayesian convolutional neural networks. BMC Oral Health. 2020 Oct 7;20(1):270. doi: 10.1186/s12903-020-01256-7.

  PMID: 33028287 RESULT

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Target Duration: 6 Months
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Assistant Professor Dr.

Study Record Dates

• First Submitted: November 14, 2025
• First Posted: November 24, 2025
• Study Start: January 2, 2023
• Primary Completion: June 30, 2023
• Study Completion: June 30, 2023
• Last Updated: November 24, 2025

Record last verified: 2023-01

Data Sharing

• IPD Sharing: Will not share

Locations

MY (1)