Agreement Between WebCeph and Manual Cephalometric Analysis: Cleft and Non‐Cleft Patients Comparison: A Retrospective Method‐Comparison Study - Aylar Afshari; Zahra Mohandes; Shabnam Ajami (Health Science Reports, 2026)
ABSTRACT Background and Aims Cephalometric analysis is essential in orthodontic diagnosis, but landmark identification is particularly challenging in cleft lip and palate (CLP). Manual tracing remains, however, time‐consuming and operator‐dependent, the gold standard. AI‐based systems such as WebCeph may improve efficiency, though agreement with manual tracing remains unclear. This study evaluated agreement between WebCeph and manual tracing in complete unilateral CLP (CUCLP) patients, comparing discrepancies with non‐cleft controls. Methods This retrospective method‐comparison study included lateral cephalograms of 45 children with repaired CUCLP and 30 age‐matched controls (8–12 years). Seven angular measurements based on 14 landmarks were analyzed using manual tracing and WebCeph. Agreement was assessed using the Intraclass correlation coefficient (ICC), group differences were tested with parametric or non‐parametric tests, measurement error was calculated using Dahlberg's formula. Results Of 75 cephalograms (mean age 8.85 ± 1.43 years), WebCeph failed in 17 cases (22.6%), with similar failure rates in cleft (22.2%) and non‐cleft (23.3%) groups, leaving 58 radiographs for analysis. ICC ranged from 0.332 to 0.845: good for SNB, and ANB; moderate for SNA and U1–SN; and poor for gonial angle, IMPA, and nasolabial angle. Significant differences occurred for gonial angle, nasolabial angle, U1–SN, and IMPA in cleft patients and for SNA in controls ( p < 0.05), with no significant between‐group difference in disagreement magnitude ( p > 0.05). Conclusion WebCeph showed variable agreement with manual tracing, from moderate to good for skeletal measurements (SNA, SNB, ANB) to poor for gonial angle, IMPA, and nasolabial angle. Similar discrepancy patterns, failure rates, and disagreement magnitudes across cleft and non‐cleft groups suggest algorithmic limitations as a contributing factor, though a meaningful influence of cleft‐related anatomy cannot be excluded given the sample size. AI‐based cephalometry should be used as an adjunct rather than a replacement for manual tracing in clinical decision‐making.