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Corrigendum to "Validation of 2D lateral cephalometric analysis using artificial intelligence-processed low-dose cone beam computed tomography" [Heliyon Volume 10, Issue 21, 15 November 2024, e39445].

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Effectiveness of cone-beam computed tomography-generated cephalograms using artificial intelligence cephalometric

Eun-Ji Chung1,2, Byoung-Eun Yang3,2,4,5, In-Young Park6,2,4

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Cone-beam computed tomography (CBCT) can generate accurate lateral cephalograms for orthodontic analysis. This method, combined with artificial intelligence, is effective for diagnosing and planning orthodontic treatment in pediatric patients.

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Area of Science:

  • Dentistry
  • Orthodontics
  • Radiology

Background:

  • Conventional lateral cephalograms have limitations in accurately representing 3D structures on a 2D plane.
  • Cone-beam computed tomography (CBCT) offers high-resolution 3D imaging with minimal distortion, though at a higher radiation dose than traditional methods.
  • Pediatric patients require careful consideration of radiation exposure and diagnostic accuracy in orthodontic evaluations.

Purpose of the Study:

  • To evaluate the accuracy and consistency of lateral cephalograms generated from CBCT data.
  • To assess the utility of artificial intelligence (AI) in analyzing these CBCT-derived cephalograms.
  • To determine the applicability of CBCT-generated cephalograms for orthodontic diagnosis and treatment planning in children.

Main Methods:

  • Comparison of conventional lateral cephalograms (Group I) with lateral cephalograms generated from CBCT using OnDemand 3D (Group II) and Invivo5 (Group III).
  • Utilized an artificial intelligence analysis program for measurements across all groups.
  • Reconstruction of lateral cephalometric radiographs from Digital Imaging and Communications in Medicine (DICOM) data obtained via CBCT.

Main Results:

  • No significant differences were found in measurements between the conventional lateral cephalograms and those generated from CBCT.
  • The study demonstrated the consistency of measurements derived from CBCT-generated cephalograms.
  • Artificial intelligence analysis proved effective in processing both conventional and CBCT-derived radiographs.

Conclusions:

  • CBCT scans, when reconstructed into lateral cephalograms and analyzed with AI, are efficient for orthodontic analysis in pediatric patients.
  • This approach provides a viable alternative for orthodontic diagnosis and planning, potentially optimizing radiation use.
  • The findings support the integration of CBCT and AI tools in modern orthodontic practices for improved diagnostic capabilities.