Comparison of Digital Analysis and Artificial Intelligence for Cephalometric Tracing

Cephalometric Tracing: A Comparison Between Digital Analysis and Artificial Intelligence

This study aims to evaluate the accuracy and reliability of artificial intelligence (AI)-based cephalometric analysis compared with digital manual tracing. A total of 100 standardized lateral cephalometric radiographs will be analyzed using Delta-Dent software with manual landmark identification and three fully automated AI-based systems (WebCeph, QuantX, and Smartee). Sagittal, vertical, dental, and soft tissue cephalometric parameters will be compared among the different methods. Statistical analysis will assess inter-method agreement and the clinical relevance of any observed discrepancies. The study seeks to determine whether AI-based systems provide measurements comparable to conventional digital tracing and whether they can be considered reliable adjunctive tools in orthodontic diagnosis and treatment planning.

Study Overview

• Status: Recruiting
• Conditions: Cephalometric Analysis; Cephalometry; Artificial Intelligence (AI); Artificial Intelligence (AI) in Diagnosis
• Intervention / Treatment: Diagnostic test: Cephalometric Analysis

Detailed Description

This prospective observational study aims to evaluate the accuracy, reproducibility, and clinical reliability of artificial intelligence (AI)-based cephalometric analysis systems compared with digital manual tracing. Patients whose lateral cephalometric radiographs were previously acquired for orthodontic diagnostic purposes at the Unit of Orthodontics and Paediatric Dentistry, University of Pavia, will be retrospectively selected according to predefined inclusion and exclusion criteria. Written informed consent for the use of clinical records for research purposes will be obtained from all participants or their legal guardians. A total of 100 standardized digital lateral cephalometric radiographs will be included in the study. Each radiograph will be analysed using Delta-Dent software with manual landmark identification and three fully automated AI-based software systems: WebCeph™, QuantX, and Smartee. Cephalometric analyses will be performed without manual adjustment of landmarks in the AI-based systems. Ten cephalometric parameters representative of sagittal, vertical, dental, and soft tissue relationships will be evaluated, including SNA, SNB, ANB, SN-GoGn, L1-GoGn, U1-ANSPNS, nasolabial angle, facial angle, Wits appraisal, and N-Me. Manual digital tracing performed by a single experienced orthodontist will be considered the reference method. Intra-rater reliability will be assessed using intraclass correlation coefficient (ICC). Statistical analysis will be conducted using R software (version 3.1.3; R Foundation for Statistical Computing, Wien, Austria). Descriptive statistics will be calculated for all variables. Normality of data distribution will be assessed using the Kolmogorov-Smirnov test. Comparisons among the different methods will be performed using the Friedman test followed by Dunn's post hoc test. Statistical significance will be predetermined at p < 0.05.

• Study Type: Observational
• Enrollment (Estimated): 100

Contacts and Locations

• Study Contact: Name: Andrea Scribante, DDS, PhD; Phone Number: +39 0382516223; Email: andrea.scribante@unipv.it

Study Locations

• Italy
  • Italy
    • Pavia, Italy, Italy, 27100
      • Recruiting
      • Unit of Orthodontics and Pediatric Dentistry - Section of Dentistry - Department of Clinical, Surgical, Diagnostic and Pediatrics - University of Pavia, Pavia, Lombardy 27100
      • Contact: Andrea Scribante, DDS, PhD; Phone Number: +39 0382516223; Email: andrea.scribante@unipv.it

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Child; Adult; Older Adult
• Accepts Healthy Volunteers: No

• Sampling Method: Non-Probability Sample

Study Population

The study population consists of patients of any age and sex who underwent digital lateral cephalometric radiography as part of routine orthodontic diagnostic records at the Unit of Orthodontics and Paediatric Dentistry, University of Pavia. Radiographs meeting the predefined inclusion and exclusion criteria will be retrospectively selected for analysis.

Description

Inclusion Criteria:

• Availability of digital lateral cephalometric radiographs of adequate diagnostic quality
• Radiographs acquired with patients in centric occlusion and proper head positioning using a cephalostat
• Patients of any age and sex
• Absence of congenital or acquired craniofacial anomalies
• No previous orthodontic treatment
• No previous orthognathic surgical treatment
• Absence of agenesis of incisors or first molars
• Absence of supernumerary teeth overlapping the region of interest

Exclusion Criteria:

• Radiographs presenting artifacts or inadequate visualization of anatomical structures
• History of significant craniofacial trauma
• Radiographs acquired without a cephalostat
• Presence of severe skeletal asymmetries
• Incomplete clinical or radiographic records
• Radiographs unsuitable for manual or AI-based cephalometric landmark identification

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort

Intervention / Treatment

Orthodontic Patients with Lateral Cephalometric Radiographs; This study includes a single observational arm, since all lateral cephalometric radiographs included in the study will undergo the same analysis procedures. Each radiograph will be evaluated using one digital manual tracing method (Delta-Dent) and three fully automated artificial intelligence-based cephalometric analysis systems (WebCeph™, QuantX, and Smartee). No patient allocation, randomization, or therapeutic intervention will be performed.

Diagnostic test: Cephalometric Analysis; All included lateral cephalometric radiographs will undergo cephalometric analysis using both digital manual tracing and artificial intelligence-based automated systems. Manual digital tracing will be performed with Delta-Dent software by a single experienced orthodontist through manual identification of cephalometric landmarks. The same radiographs will subsequently be analysed using three fully automated AI-based software programs (WebCeph™, QuantX, and Smartee) without manual correction of landmark positioning. No therapeutic intervention or modification of patient treatment will be performed, as this is an observational comparative study based exclusively on retrospective analysis of radiographic records.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Agreement between AI-based cephalometric analysis and digital manual tracing	Agreement between cephalometric measurements obtained with AI-based software systems and digital manual tracing will be assessed using the intraclass correlation coefficient (ICC) and differences in angular and linear cephalometric measurements.	Baseline

Secondary Outcome Measures

Outcome Measure	Time Frame
SNA	Baseline
SNB	Baseline
ANB	Baseline
SN-GoGn	Baseline
L1-GoGn	Baseline
U1-ANSPNS	Baseline
Nasolabial angle	Baseline
Facial angle	Baseline
Wits appraisal	Baseline
N-Me	Baseline

Collaborators and Investigators

• Sponsor: University of Pavia

Study record dates

Study Major Dates

• Study Start (Actual): June 1, 2026
• Primary Completion (Estimated): September 30, 2026
• Study Completion (Estimated): September 30, 2026

Study Registration Dates

• First Submitted: June 17, 2026
• First Submitted That Met QC Criteria: June 17, 2026
• First Posted (Actual): June 24, 2026

Study Record Updates

• Last Update Posted (Actual): June 24, 2026
• Last Update Submitted That Met QC Criteria: June 17, 2026
• Last Verified: June 1, 2026

More Information

Terms related to this study

• Keywords: artificial intelligence; landmark identification; cephalometric analysis; lateral cephalogram; automated cephalometric tracing
• Other Study ID Numbers: 2026-CEPHTRACING

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO
• IPD Plan Description: Data will be available upon motivated request to the corresponding authors.

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No