首页|University of Adelaide Reports Findings in Artificial Intelligence (Relationship between anterior occlusion, arch dimension, and mandibular movement during spee ch articulation: A three-dimensional analysis)
University of Adelaide Reports Findings in Artificial Intelligence (Relationship between anterior occlusion, arch dimension, and mandibular movement during spee ch articulation: A three-dimensional analysis)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Artificial Intelligenc e is the subject of a report. According to news originating from Adelaide, Austr alia, by NewsRx correspondents, research stated, “Studies correlating occlusal m orphology from 3-dimensional intraoral scans with both soft and hard tissue dyna mic landmark tracking within the same participant population are lacking. The pu rpose of this clinical study was to use 3-dimensional intraoral scanning, comput er-aided design, electrognathography, and artificial intelligence to investigate the relationships between anterior occlusion and arch parameters with hard and soft tissue displacements during speech production.” Our news journalists obtained a quote from the research from the University of A delaide, “An artificial intelligence (AI) driven software program and electrogna thography was used to record the phonetic activities in 62 participants for soft tissue (ST) and hard tissue (HT) displacement. Soft tissue displacement was qua ntified by the mean difference between subnasale and soft tissue pogonion peaks during phonetic expressions, and hard tissue displacement was directly measured with an electrognathograph. Intercanine and intermolar distances, arch perimeter s, and horizontal and vertical overlap were measured from the intraoral scan dat a. ST and HT displacements were successfully estimated for fricative (ST=7.16 ±4 .51 mm, HT=11.86 ±4.02 mm), sibilant (ST=5.11 ±3.49 mm, HT=8.24 ±3.31 mm), lingu odental (ST=5.72 ±4.46 mm, HT=10.01 ±3.16 mm), and bilabial (ST=5.56 ±4.64 mm, H T=11.69 ±4.28 mm) phonetics. Vertical overlap correlated positively with hard ti ssue movement during all speech expressions except bilabial phonetics (r=.30 to. 41, P<.05). Maxillary and mandibular arch perimeters showed negative correlations with soft tissue displacement during linguodental and bil abial speech (r=-.25 to -.41, P<.05) but were significantly correlated with hard tissue movement during all speech assessments (r=-.28 to - .44, P<.05). Maxillary intermolar distances negatively corr elated with hard tissue phonetic expressions (r=- .24 to -.30, P<.05). Participant age positively correlated with soft tissue displacement during all speech patterns (r=.28 to.33, P<.05) and with weight i ncrease (r=.27, P=.033), and hard tissue displacement (r=.25, P=.048) during max imum mouth opening significantly correlated with linguodental phonetics.”
AdelaideAustraliaAustralia and New Z ealandArtificial IntelligenceEmerging TechnologiesMachine Learning