This study is designed to evaluate whether artificial intelligence can analyze cephalometric images in orthodontics as a reliable tool for diagnosis and treatment planning. The study will include orthodontic patients who need cephalometric evaluation. Participants will have their X-ray images analyzed using both the AI system and traditional manual methods. The study will compare the results to see how closely the AI measurements match the standard measurements. This information may help patients, families, and health care providers understand how AI can support orthodontic diagnosis and treatment planning.

Cephalometric analysis is a fundamental diagnostic tool in orthodontics. Conventional manual tracing is time-consuming and operator-dependent, while artificial intelligence-based software has been introduced to improve efficiency and consistency.

This observational study will evaluate and compare manual and AI-assisted cephalometric analyses using lateral cephalometric radiographs. Selected angular and linear measurements will be assessed, and the agreement between the two methods will be statistically analyzed to determine accuracy and reliability.

Inclusion Criteria:

• No systemic disease.
• Not receiving medical treatment that could interfere with bone metabolism.
• Good level of oral hygiene.
• No periodontal disease or radiographic evidence of bone loss.

Exclusion Criteria:

• Periodontally compromised patients.
• Presence of systemic diseases.
• Drug dependencies.
• Uncooperative patients.

consists of orthodontic patients aged 12 to 40 years who require orthodontic diagnosis and treatment planning. Participants will have good-quality lateral cephalometric radiographs taken using standardized imaging protocols. The study includes both male and female patients with no previous orthodontic treatment.

The decision to share individual participant data has not yet been finalized. Considerations regarding participant confidentiality, data protection regulations, and ongoing study procedures may affect the ability to share IPD in the future.