Influence of exposure protocol, voxel size, and artifact removal algorithm on the trueness of segmentation utilizing an artificial-intelligence-based system

Safa Alrashed; Vinicius Dutra; Tien-Min G Chu; Chao-Chieh Yang; Wei-Shao Lin

doi:10.1111/jopr.13827

Influence of exposure protocol, voxel size, and artifact removal algorithm on the trueness of segmentation utilizing an artificial-intelligence-based system

J Prosthodont. 2024 Feb 2. doi: 10.1111/jopr.13827. Online ahead of print.

Authors

Safa Alrashed¹, Vinicius Dutra², Tien-Min G Chu³, Chao-Chieh Yang^{4

5}, Wei-Shao Lin^{4

5}

Affiliations

¹ Oral Biology PhD program in the College of Dentistry, Division of Restorative and Prosthetic Dentistry, The Ohio State University, Columbus, Ohio, USA.
² Department of Oral Pathology, Medicine, and Radiology, Indiana University School of Dentistry, Indianapolis, Indiana, USA.
³ Department of Biomedical Sciences and Comprehensive Care, Indiana University School of Dentistry, Indianapolis, Indiana, USA.
⁴ Department of Prosthodontics, Indiana University School of Dentistry, Indianapolis, Indiana, USA.
⁵ Advanced Education Program in Prosthodontics, Department of Prosthodontics, Indiana University School of Dentistry, Indianapolis, Indiana, USA.

PMID: 38305665
DOI: 10.1111/jopr.13827

Abstract

Purpose: To evaluate the effects of exposure protocol, voxel sizes, and artifact removal algorithms on the trueness of segmentation in various mandible regions using an artificial intelligence (AI)-based system.

Materials and methods: Eleven dry human mandibles were scanned using a cone beam computed tomography (CBCT) scanner under differing exposure protocols (standard and ultra-low), voxel sizes (0.15 mm, 0.3 mm, and 0.45 mm), and with or without artifact removal algorithm. The resulting datasets were segmented using an AI-based system, exported as 3D models, and compared to reference files derived from a white-light laboratory scanner. Deviation measurement was performed using a computer-aided design (CAD) program and recorded as root mean square (RMS). The RMS values were used as a representation of the trueness of the AI-segmented 3D models. A 4-way ANOVA was used to assess the impact of voxel size, exposure protocol, artifact removal algorithm, and location on RMS values (α = 0.05).

Results: Significant effects were found with voxel size (p < 0.001) and location (p < 0.001), but not with exposure protocol (p = 0.259) or artifact removal algorithm (p = 0.752). Standard exposure groups had significantly lower RMS values than the ultra-low exposure groups in the mandible body with 0.3 mm (p = 0.014) or 0.45 mm (p < 0.001) voxel sizes, the symphysis with a 0.45 mm voxel size (p = 0.011), and the whole mandible with a 0.45 mm voxel size (p = 0.001). Exposure protocol did not affect RMS values at teeth and alveolar bone (p = 0.544), mandible angles (p = 0.380), condyles (p = 0.114), and coronoids (p = 0.806) locations.

Conclusion: This study informs optimal exposure protocol and voxel size choices in CBCT imaging for true AI-based automatic segmentation with minimal radiation. The artifact removal algorithm did not influence the trueness of AI segmentation. When using an ultra-low exposure protocol to minimize patient radiation exposure in AI segmentations, a voxel size of 0.15 mm is recommended, while a voxel size of 0.45 mm should be avoided.

Keywords: AI segmentation; CBCT; cone beam computed tomography; ultra-low dose.