Artificial Intelligence (AI), Artificial Intelligence (AI) in Diagnosis, Cephalometric Analysis, Cephalometry
Conditions
Keywords
artificial intelligence, cephalometric analysis, lateral cephalogram, landmark identification, automated cephalometric tracing
Brief summary
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_TESTCephalometric 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.
Sponsors
University of Pavia
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Healthy volunteers
No
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
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Agreement between AI-based cephalometric analysis and digital manual tracing | Baseline | 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. |
Secondary
| 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 |
Countries
Italy
Contacts
CONTACTAndrea Scribante, DDS, PhD
Outcome results
None listed