El Merouani
Drissi A, Ben Mohimd H, Alassfar A, Ahmadoun D, Zaoui F, Benyahia H. Evaluation
of Accuracy and Reproducibility of an AI-Based Cephalometric Landmark Detection
Software: A Comparative Study with Manual Annotations. J Clin Exp Dent.
2026;18(6):e720-8.
doi:10.4317/jced.64030
https://doi.org/10.4317/jced.64030
___
References
|
1.
Arık SÖ, Ibragimov B, Xing L. Fully automated quantitative cephalometry using
convolutional neural networks. J Med Imaging (Bellingham). 2017;4(1):014501. |
|
|
|
|
|
2.
Kim IH, Kim Y, Han S, Park JW, Kim N. Comparing intra-observer variation and
automated cephalometric landmark identification on lateral cephalograms. Sci
Rep. 2021. |
|
|
|
|
|
3.
Park JH, Hwang HW, Moon JH, Yu Y, Kim H, Her SB, et al. Automated
identification of cephalometric landmarks: Part 1-Comparisons between the
latest deep-learning methods YOLOV3 and SSD. Angle Orthod.
2019;89(6):903-909. |
|
|
|
|
|
4.
Polizzi A, Leonardi R. Automatic cephalometric landmark identification with
artificial intelligence: An umbrella review of systematic reviews. J Dent.
2024;146:105056. |
|
|
|
|
|
5.
Kim YH, Lee C, Ha EG, Choi YJ, Han SS. A fully deep learning model for the
automatic identification of cephalometric landmarks. Imaging Sci Dent.
2021;51(3):299-306. |
|
|
|
|
|
6.
Schwendicke F, Chaurasia A, Arsiwala L, Lee JH, Elhennawy K, Jost-Brinkmann
PG, et al. Deep learning for cephalometric landmark detection: systematic
review and meta-analysis. Clin Oral Investig. 2021;25(7):4299-4309. |
|
|
|
|
|
7.
Hung K, Montalvao C, Tanaka R, Kawai T, Bornstein MM. The use and performance
of artificial intelligence applications in dental and maxillofacial
radiology: a systematic review. Dentomaxillofac Radiol. 2020;49(1):20190107. |
|
|
|
|
|
8.
Hwang HW, Moon JH, Kim MG, Donatelli RE, Lee SJ. Evaluation of automated
cephalometric analysis based on the latest deep learning method. Angle
Orthod. 2021 May 1;91(3):329-335. |
|
|
|
|
|
9.
Yassir YA, Salman AR, Nabbat SA. The accuracy and reliability of WebCeph for
cephalometric analysis. J Taibah Univ Med Sci. 2021;17(1):57-66. |
|
|
|
|
|
10.
World Medical Association. World Medical Association Declaration of Helsinki:
ethical principles for medical research involving human subjects. JAMA.
2013;310(20):2191-4. |
|
|
|
|
|
11.
Durão AR, Pittayapat P, Rockenbach MI, Olszewski R, Ng S, Ferreira AP, et al.
Validity of 2D lateral cephalometry in orthodontics: a systematic review.
Prog Orthod. 2013;14(1):31. |
|
|
|
|
|
12.
Houston WJ. The analysis of errors in orthodontic measurements. Am J Orthod.
1983;83(5):382-90. |
|
|
|
|
|
13.
McClure SR, Sadowsky PL, Ferreira A, Jacobson A. Reliability of digital
versus conventional cephalometric radiology: a comparative evaluation.
American Journal of Orthodontics and Dentofacial Orthopedics,
2005;128(6):721-726. |
|
|
|
|
|
14.
Kim H, Shim E, Park J, Kim YJ, Lee U, Kim Y. Web-based fully automated
cephalometric analysis by deep learning. Comput Methods Programs Biomed. 2020;194:105513. |
|
|
|
|
|
15.
Yang S, Song ES, Lee ES, Kang SR, Yi WJ, Lee SP. Ceph-Net: automatic
detection of cephalometric landmarks on scanned lateral cephalograms from
children and adolescents using an attention-based stacked regression network.
BMC Oral Health. 2023 Oct 27;23(1):803. Erratum in: BMC Oral Health. 2024 Feb
1;24(1):165. |
|
|
|
|
|
16.
Midtgård J, Björk G, Linder-Aronson S. Reproducibility of cephalometric
landmarks and errors of measurements of cephalometric cranial distances. Angle
Orthod. 1974;44(1):56-61. |
|
|
|
|
|
17.
Stabrun AE, Danielsen K. Precision in cephalometric landmark identification.
Eur J Orthod. 1982;4(3):185-96. |
|
|
|
|
|
18.
Trpkova B, Major P, Prasad N, Nebbe B. Cephalometric landmarks identification
and reproducibility: a meta analysis. Am J Orthod Dentofacial Orthop.
1997;112(2):165-70. |
|
|
|
|
|
19.
Rudolph DJ, Sinclair PM, Coggins JM. Automatic computerized radiographic
identification of cephalometric landmarks. Am J Orthod Dentofacial Orthop.
1998;113(2):173-9. |
|
|
|
|
|
20.
Panesar S, Manoharan G, Alshayji AA, et al. Precision and accuracy assessment
of cephalometric analyses performed by deep-learning artificial intelligence.
Applied Sciences. 2023;13. |
|
|
|
|
|
21.
Londoño J, Aboal DG, Peña CG. Accuracy of deep learning models for
cephalometric landmark detection: a systematic review and meta-analysis.
Dentomaxillofacial Radiology. 2023;52(9):20230073. |
|
|
|
|
|
22.
Bao P, Lee JS, Kim S. Evaluation of cephalometric landmark detection methods:
clinical acceptability thresholds. Journal of Dental Research. 2022;101(12):1426. |
|
|
|
|
|
23.
Buscemi N, Hartling L, Vandermeer B, Tjosvold L, Klassen TP. Single data
extraction generated more errors than double data extraction in systematic
reviews. J Clin Epidemiol. 2006;59(7):697-703. |
|