Affiliation:
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
Abstract
AbstractPurposeTo 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 methodsEleven 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).ResultsSignificant 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.ConclusionThis 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.