Comparison of linear and angular cephalometric measurements in CBCT using semi-automated software and artificial intelligence-generated cephalograms
BMC Oral Health, cilt.26, sa.1, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 26 Sayı: 1
- Basım Tarihi: 2026
- Doi Numarası: 10.1186/s12903-026-08391-7
- Dergi Adı: BMC Oral Health
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, CINAHL, EMBASE, MEDLINE, Directory of Open Access Journals, Natural Science Collection (ProQuest), Biological Science Database (ProQuest), Biomedical Reference Collection: Corporate Edition (EBSCO), Health Research Premium Collection (ProQuest)
- Anahtar Kelimeler: Artificial intelligence, CBCT, Cephalometric measurements
- Ankara Üniversitesi Adresli: Evet
Özet
Background: This study aimed to assess the reliability of cephalometric measurements using CBCTs with two semi-automated software programs (InVivoDental and Romexis) and an artificial intelligence-based platform (Diagnocat). Methods: This cross-sectional reliability study retrospectively analyzed a total of 29 CBCT scans (15 from Northern Cyprus and 14 from Egypt). Eleven measurements were included to assess vertical relationship, and anteroposterior relationship, using two software programs and an AI-based website. Results: Intraobserver reliability, assessed through Bland-Altman plots, demonstrated high consistency. Interobserver agreement showed excellent reliability for most measurements, with ICC values exceeding 0.90 for both InVivoDental and Romexis software. Most measurements did not show significant differences between the two semi-automated programs and the AI-based algorithms. However, the AI system indicated lower measurements for Wits Appraisal and U1-SN angle compared to the two software systems. Conclusion: In conclusion, this study underscores the importance of evaluating the reliability of cephalometric measurements on CBCTs, particularly when utilizing semi-automated software programs and artificial intelligence-based platforms. Our analysis revealed consistent intra-observer repeatability across most measurements. However, it is noteworthy that certain metrics exhibited reduced repeatability when assessed using AI-generated cephalograms, suggesting potential areas for improvement in AI algorithms.