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Journals / Adıyaman Üniversitesi Sağlık Bilimleri Dergisi / 2026 / Cilt: 12 Sayı: 1

Comparison of contemporary cephalometric methods for different malocclusions

Farklı maloklüzyon tipleri için güncel sefalometrik yöntemlerin karşılaştırılması

Elif Albayrak(SÜLEYMAN DEMİREL ÜNİVERSİTESİ)0000-0002-5082-9031
Ömer Faruk Sarı(ANKARA MEDİPOL ÜNİVERSİTESİ)0000-0003-0086-8232
Muhammed Hilmi Buyukcavus(ANTALYA BİLİM ÜNİVERSİTESİ)0000-0003-2184-1549
Pages
10–18
Publication Date
2026-04-23
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

WebCeph and Nemoceph provide reliable alternatives to traditional cephalometric analysis and offer notable advantages in terms of time and clinical efficiency.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Aim: This study aimed to evaluate the reliability of two widely used web-based cephalometric analysis programs, WebCeph and Nemoceph, in comparison with the conventional manual method.
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Key Findings & Data:Materials and Methods: A total of 150 lateral cephalometric radiographs from patients with Class I, II, and III malocclusions were analyzed.
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Conclusion & Impact:WebCeph and Nemoceph provide reliable alternatives to traditional cephalometric analysis and offer notable advantages in terms of time and clinical efficiency.
🔬Design:Araştırma Makalesi
📍Scope:Comprehensive Sample
⚙️Variables:Sefalometri · Dijital Analiz · WebCeph

Abstract

Aim: This study aimed to evaluate the reliability of two widely used web-based cephalometric analysis programs, WebCeph and Nemoceph, in comparison with the conventional manual method. Materials and Methods: A total of 150 lateral cephalometric radiographs from patients with Class I, II, and III malocclusions were analyzed. Eighteen cephalometric parameters were measured by two researchers using manual, WebCeph, and Nemoceph methods. Intra- and inter-method reliability was assessed using Intragroup Correlation Coefficient (ICC), paired t-tests, ANOVA, and post-hoc analyses. Results: High correlation was observed among the three methods for skeletal and dental measurements. However, soft tissue measurements showed lower consistency across methods. Conclusion: There were no clinically significant differences between manual and digital methods. WebCeph and Nemoceph provide reliable alternatives to traditional cephalometric analysis and offer notable advantages in terms of time and clinical efficiency.

Özet

Amaç: Bu çalışma, ortodontik tanıda yaygın olarak kullanılan iki web tabanlı sefalometrik analiz programı olan WebCeph ve Nemoceph'in güvenilirliğini geleneksel manuel yöntemle karşılaştırmayı amaçlamaktadır. Gereç ve Yöntem: Sınıf I, II ve III maloklüzyona sahip 150 hastaya ait lateral sefalometrik radyografiler analiz edilmiştir. On sekiz sefalometrik parametre, iki araştırmacı tarafından manuel, WebCeph ve Nemoceph yöntemleri kullanılarak ölçülmüştür. Yöntemler arası ve araştırmacılar arası güvenilirlik İntra Grup Korelasyon Katsayısı (ICC), eşleştirilmiş t-testi, ANOVA ve post-hoc testlerle değerlendirilmiştir. Bulgular: İskeletsel ve dentoalveolar ölçümler arasında üç yöntem arasında yüksek düzeyde korelasyon gözlenmiştir. Ancak, yumuşak doku ölçümlerinde yöntemler arasında daha düşük tutarlılık bulunmuştur. Sonuç: Manuel ve dijital yöntemler arasında klinik olarak anlamlı bir fark bulunmamıştır. WebCeph ve Nemoceph, geleneksel sefalometrik analizlere güvenilir bir alternatif sunmakta ve klinik uygulamalarda zaman ve verimlilik açısından önemli avantajlar sağlamaktadır.

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Cite This Article

Albayrak, E., Sarı, Ö. F., Buyukcavus, M. H. (2026). Comparison of contemporary cephalometric methods for different malocclusions. Adıyaman Üniversitesi Sağlık Bilimleri Dergisi, Cilt: 12 Sayı: 1, 10–18. https://doi.org/10.30569/adiyamansaglik.1746447

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References (25)

  1. Cobourne, Martyn T., and Andrew T. DiBiase. Handbook of Orthodontics E-Book: Handbook of Orthodontics E-Book. Elsevier Health Sciences, 2024:81
  2. Chartrand G, Cheng PM, Vorontsov E, Drozdzal M, Turcotte S, Pal CJ, Kadoury S, Tang A. Deep Learning: A Primer for Radiologists. Radiographics. 2017;37(7):2113-2131.
  3. Cheng PM, Montagnon E, Yamashita R, et al. Deep Learning: An Update for Radiologists. Radiographics. 2021;41(5):1427-1445.
  4. Naragond A, Kenganal S, Sagarkar R, Kumar NS, Sugaradday. Diagnostic limitations of cephalometrics in ortho- dontics:A review. J Dent Med Sci. 2012; 3:30-5.
  5. Ye H, Cheng Z, Ungvijanpunya N, Chen W, Cao L, Gou Y. Is automatic cephalometric software using artificial intelligence better than orthodontist experts in landmark identification?. BMC Oral Health. 2023;23(1):467.
  6. Sayinsu K, Isik F, Trakyali G, Arun Tülin. An evaluation of the errors in cephalometric measurements on scanned cephalometric images and conventional tracings. Eur J Orthod 2007;29: 105-8.
  7. Celik E, Polat-Ozsoy O, Toygar Memikoglu TU. Compa- rison of cephalometric measurements with digital versus conventional cephalometric analysis. Eur J Orthod. 2009;31(3):241-6.
  8. Mosleh MA, Baba MS, Malek S, Almaktari RA. Ceph-X: development and evaluation of 2D cephalometric system. BMC Bioinformatics. 2016;17:499.
  9. Park JH, Hwang HW, Moon JH, Yu Y, Kim H, 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.
  10. Hwang HW, Park JH, Moon JH, Yu Y, Kim H, et al. Au- tomated identification of cephalometric landmarks: Part 2-Might it be better than human?. Angle Orthod. 2020;90(1):69-76.
  11. Goracci C, Ferrari M. Reproducibility of measurements in tablet-assisted, PC-aided, and manual cephalometric analysis. Angle Orthod. 2014;84(3):437-42.
  12. Cavdar K, Ciger S, Zeynepos AA. Comparison of conventional and computerized cephalometric methods. Clin Dent Res.2011;35(1):33-40.
  13. Jeon S, Lee KC. Comparison of cephalometric measu- rements between conventional and automatic cephalometric analysis using convolutional neural network. Prog Orthod. 2021;22(1):14.
  14. 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.
  15. Tikku T, Khanna R, Maurya RP, Srivastava K, Bhushan R. Comparative evaluation of cephalometric measurements of monitor-displayed images by NemoCeph software and its hard copy by manual tracing. J Oral Biol Craniofac Res. 2014;4(1):35-41.
  16. Hoogeveen RC, Sanderink GC, Berkhout WE. Effect of head position on cephalometric evaluation of the soft-tissue facial profile. Dentomaxillofac Radiol. 2013;42(6):20120423.
  17. Reyes LT, Knorst JK, Ortiz FR, Ardenghi TM. Scope and challenges of machine learning-based diagnosis and prognosis in clinical dentistry: A literature review. J Clin Transl Res. 2021;7(4):523-539.
  18. Farooq MU, Khan MA, Imran S, Sameera A, Qureshi A, Ahmed SA, et al. Assess- ing the Reliability of Digitalized Cephalometric Analysis in Comparison with Manual Cephalometric Analysis. J Clin Diagn Res. 2016;10(10):20–3.
  19. Erkan M, Gurel HG, Nur M, Demirel B. Reliability of four different computer- ized cephalometric analysis programs. European Journal of Orthodontics. 2012;34(3):318–21.
  20. Kunz F, Stellzig-Eisenhauer A, Zeman F, Boldt J. Artificial intelligence in orthodontics: evaluation of a fully automated cephalometric analysis using a customized convolutional neural network. J Orofac Orthop. 2020;81(1):52–68.
  21. Kazimierczak N, Kazimierczak W, Serafin Z, et al. Skeletal facial asymmetry: reliability of manual and artificial intelligence-driven analysis. Dentomaxillofac Radiol. 2024;53(1):52-59.
  22. Aksakallı S, Yılancı H, Görükmez E, Ramoğlu Sİ. Reliability assessment of orthodontic apps for cephalometrics. Turk J Orthod 2016;29: 98-102.
  23. Baig N, Gyasudeen KS, Bhattacharjee T, Chaudhry J, Prasad S. Comparative evaluation of commercially available AI-based cephalometric tracing programs. BMC Oral Health. 2024;24(1):1241.
  24. Ter Horst R, van Weert H, Loonen T, et al. Three-dimensional virtual planning in mandibular advancement surgery: Soft tissue prediction based on deep learning. J Craniomaxillofac Surg. 2021;49(9):775-782.
  25. Bao H, Zhang K, Yu C, et al. Evaluating the accuracy of automated cephalometric analysis based on artificial intelligence. BMC Oral Health. 2023;23(1):191.