Clinical Reliability of AI-Based Cephalometric Analysis Using WebCeph: A Comparative Agreement Study

Background/Objectives: Artificial intelligence has accelerated cephalometric analysis by enabling rapid and standardized measurements. However, whether these automated outputs can be considered clinically interchangeable with expert manual tracing remains unresolved, particularly for routinely used analyses such as Steiner. Methods: A comparative experimental study was conducted on 100 lateral cephalometric radiographs analysed using two parallel approaches: expert manual tracing and fully automated analysis with the WebCeph platform. Seven Steiner variables (SNA, SNB, ANB, 1–NA, 1–NB, interincisal angle, and FMA) were evaluated. Paired t-tests were used to assess differences between methods, while agreement was evaluated using intraclass correlation coefficients and Bland–Altman analysis. Particularly low agreement was observed for clinically relevant parameters such as ANB and FMA. Results: Six of the seven variables showed statistically significant differences between methods. Automated measurements systematically tended to overestimate both skeletal and dental parameters. Agreement was inconsistent and frequently poor, with ICC values ranging from 0.01 to 0.60 for clinically relevant variables such as ANB and FMA. Importantly, small or non-significant mean differences did not translate into acceptable agreement. Bland–Altman analysis confirmed the presence of systematic bias and wide limits of agreement, especially for dental measurements. Conclusions: Despite its speed and automation, WebCeph does not achieve clinically acceptable agreement with expert manual tracing across several key cephalometric variables. The observed discrepancies—particularly in parameters critical for sagittal and vertical diagnosis—may compromise clinical interpretation and treatment planning. These findings support the use of AI-based cephalometric analysis as an adjunctive tool rather than a substitute for clinician-guided evaluation.

Authors

Institutions

Publication Details

Journal
Journal of Clinical Medicine
Published
2026-05-28
DOI
https://doi.org/10.3390/jcm15114155
Primary Topic
Dental Radiography and Imaging
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Clinical Reliability of AI-Based Cephalometric Analysis Using WebCeph: A Comparative Agreement Study

Juan Ignacio Aura‐Tormos, Clara Guinot-Barona, María Carmona-Santamaría, Laura Marqués-Martínez et al.
Journal of Clinical Medicine
Dental Radiography and Imaging
article

Clinical Reliability of AI-Based Cephalometric Analysis Using WebCeph: A Comparative Agreement Study

Juan Ignacio Aura‐Tormos, Clara Guinot-Barona, María Carmona-Santamaría, Laura Marqués-Martínez, Angela Bisbal-Puchades, Esther García-Miralles, Ali Azari-Mehr
article en

Abstract

Background/Objectives: Artificial intelligence has accelerated cephalometric analysis by enabling rapid and standardized measurements. However, whether these automated outputs can be considered clinically interchangeable with expert manual tracing remains unresolved, particularly for routinely used analyses such as Steiner. Methods: A comparative experimental study was conducted on 100 lateral cephalometric radiographs analysed using two parallel approaches: expert manual tracing and fully automated analysis with the WebCeph platform. Seven Steiner variables (SNA, SNB, ANB, 1–NA, 1–NB, interincisal angle, and FMA) were evaluated. Paired t-tests were used to assess differences between methods, while agreement was evaluated using intraclass correlation coefficients and Bland–Altman analysis. Particularly low agreement was observed for clinically relevant parameters such as ANB and FMA. Results: Six of the seven variables showed statistically significant differences between methods. Automated measurements systematically tended to overestimate both skeletal and dental parameters. Agreement was inconsistent and frequently poor, with ICC values ranging from 0.01 to 0.60 for clinically relevant variables such as ANB and FMA. Importantly, small or non-significant mean differences did not translate into acceptable agreement. Bland–Altman analysis confirmed the presence of systematic bias and wide limits of agreement, especially for dental measurements. Conclusions: Despite its speed and automation, WebCeph does not achieve clinically acceptable agreement with expert manual tracing across several key cephalometric variables. The observed discrepancies—particularly in parameters critical for sagittal and vertical diagnosis—may compromise clinical interpretation and treatment planning. These findings support the use of AI-based cephalometric analysis as an adjunctive tool rather than a substitute for clinician-guided evaluation.

Journal of Clinical MedicineVol. 15(11)
Universitat de València (ES), Valencia Catholic University Saint Vincent Martyr (ES)
No poverty
Openalex Percentile: Top 5%
Dental Radiography and Imaging
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.