Comparison of Digital Analysis and Artificial Intelligence for Cephalometric Tracing
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- The protocol lists: Cephalometric Analysis.
- Who it may be relevant to
- Registry conditions: Cephalometric Analysis, Cephalometry, Artificial Intelligence (AI), Artificial Intelligence (AI) in Diagnosis. Basic parameters: No limits · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- Italy
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Official title
Cephalometric Tracing: A Comparison Between Digital Analysis and Artificial Intelligence
Overview
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.
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.
Interventions
- 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 la
Primary outcome measures
- Agreement between AI-based cephalometric analysis and digital manual tracing [Time frame: Baseline]
Secondary outcome measures (10)
- SNA [Time frame: Baseline]
- SNB [Time frame: Baseline]
- ANB [Time frame: Baseline]
- SN-GoGn [Time frame: Baseline]
- L1-GoGn [Time frame: Baseline]
- U1-ANSPNS [Time frame: Baseline]
- Nasolabial angle [Time frame: Baseline]
- Facial angle [Time frame: Baseline]
- Wits appraisal [Time frame: Baseline]
- N-Me [Time frame: Baseline]
Eligibility criteria
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
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: No
Study design
- Observational model
- Cohort
Study locations
Italy · 1 center
- Unit of Orthodontics and Pediatric Dentistry - Section of Dentistry - Department of Clinic — Pavia
Identifiers
NCT: NCT07664488 · 2026-CEPHTRACING