首页|Study Data from Indiana University Update Understanding of Artificial Intelligence (Influence of Exposure Protocol, Voxel Size, and Artifact Removal Algorithm On the Trueness of Segmentation Utilizing an Artificial-intelligence-based System)
Study Data from Indiana University Update Understanding of Artificial Intelligence (Influence of Exposure Protocol, Voxel Size, and Artifact Removal Algorithm On the Trueness of Segmentation Utilizing an Artificial-intelligence-based System)
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Investigators discuss new findings in Artificial Intelligence. According to news originating from Indianapolis, Indiana, by NewsRx correspondents, research stated, "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. 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." Financial supporters for this research include Analyst Programmer at the UITS RT Advanced Visualization Lab, Indiana University Information Technology Services. Our news journalists obtained a quote from the research from Indiana University, "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 (alpha = 0.05). 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. 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."
IndianapolisIndianaUnited StatesNorth and Central AmericaAlgorithmsArtificial IntelligenceEmerging TechnologiesMachine LearningIndiana University
NETL
NSTL
Wiley
