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

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

COMPLETED

Total Enrollment

40 participants

Study Classification

OBSERVATIONAL

Study Start Date

2023-01-02

Study Completion Date

2023-06-30

Brief Summary

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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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Detailed Description

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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.

Conditions

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Cephalometry Orthodontic Cephalometric Analysis

Study Design

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Observational Model Type

COHORT

Study Time Perspective

PROSPECTIVE

Study Groups

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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)

Manual Cephalometric Tracing

Intervention Type 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)

Intervention Type 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.

Interventions

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Manual Cephalometric Tracing

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.

Intervention Type DIAGNOSTIC_TEST

AI-Based Cephalometric Analysis (WeDoCeph Software)

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.

Intervention Type DIAGNOSTIC_TEST

Eligibility Criteria

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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

Eligible Sex

ALL

Accepts Healthy Volunteers

No