Farook Taseef Hasan, Ramees Lameesa, Dudley James
PhD Candidate, Adelaide Dental School, The University of Adelaide, Adelaide, Australia.
Research Assistant, Adelaide Dental School, The University of Adelaide, Adelaide, Australia.
J Prosthet Dent. 2025 Mar;133(3):881-887. doi: 10.1016/j.prosdent.2024.08.001. Epub 2024 Sep 5.
Studies correlating occlusal morphology from 3-dimensional intraoral scans with both soft and hard tissue dynamic landmark tracking within the same participant population are lacking.
The purpose of this clinical study was to use 3-dimensional intraoral scanning, computer-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.
An artificial intelligence (AI) driven software program and electrognathography was used to record the phonetic activities in 62 participants for soft tissue (ST) and hard tissue (HT) displacement. Soft tissue displacement was quantified 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 perimeters, and horizontal and vertical overlap were measured from the intraoral scan data.
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), linguodental (ST=5.72 ±4.46 mm, HT=10.01 ±3.16 mm), and bilabial (ST=5.56 ±4.64 mm, HT=11.69 ±4.28 mm) phonetics. Vertical overlap correlated positively with hard tissue movement during all speech expressions except bilabial phonetics (ρ=.30 to.41, P<.05). Maxillary and mandibular arch perimeters showed negative correlations with soft tissue displacement during linguodental and bilabial speech (ρ=-.25 to -.41, P<.05) but were significantly correlated with hard tissue movement during all speech assessments (ρ=-.28 to -.44, P<.05). Maxillary intermolar distances negatively correlated with hard tissue phonetic expressions (ρ=-.24 to -.30, P<.05). Participant age positively correlated with soft tissue displacement during all speech patterns (ρ=.28 to.33, P<.05) and with weight increase (ρ=.27, P=.033), and hard tissue displacement (ρ=.25, P=.048) during maximum mouth opening significantly correlated with linguodental phonetics.
Within the study population, vertical overlap, maxillary intermolar distance, and dental arch perimeters correlated significantly with mandibular displacement during phonetic expression.
目前尚缺乏在同一受试人群中，将三维口腔内扫描所得的咬合形态与软硬组织动态标志点追踪相关联的研究。
本临床研究旨在运用三维口腔内扫描、计算机辅助设计、电颚计及人工智能技术，探究语音产生过程中前牙咬合与牙弓参数和软硬组织位移之间的关系。
使用一款由人工智能驱动的软件程序和电颚计，记录62名受试者在语音活动中的软组织（ST）和硬组织（HT）位移情况。软组织位移通过语音表达过程中鼻下点与软组织颏前点峰值之间的平均差值进行量化，硬组织位移则直接用电颚计测量。从口腔内扫描数据中测量尖牙间和磨牙间距离、牙弓周长以及水平和垂直覆盖情况。
对于擦音（ST = 7.16 ± 4.51毫米，HT = 11.86 ± 4.02毫米）、咝音（ST = 5.11 ± 3.49毫米，HT = 8.24 ± 3.31毫米）、舌齿音（ST = 5.72 ± 4.46毫米，HT = 10.01 ± 3.16毫米）和双唇音（ST = 5.56 ± 4.64毫米，HT = 11.69 ± 4.28毫米），成功估算出了软组织和硬组织的位移。除双唇音外，在所有语音表达中，垂直覆盖与硬组织运动呈正相关（ρ = 0.30至0.41，P < 0.05）。在舌齿音和双唇音语音过程中，上颌和下颌牙弓周长与软组织位移呈负相关（ρ = -0.25至-0.41，P < 0.05），但在所有语音评估中与硬组织运动显著相关（ρ = -
