De Novo Dissecting the Three‑Dimensional Facial Morphology of 2379 Han Chinese Individuals

Phenotypic diversity,especially that of facial morphology,has not been fully investigated in the Han Chinese,which is the largest ethnic group in the world.In this study,we systematically analyzed a total of 14,838 facial traits representing 15 categories with both a large-scale three-dimensional(3D)manual landmarking database and computer-aided facial segmented phenotyping in 2379 Han Chinese individuals.Our results illustrate that homogeneous and heterogeneous facial morphological traits exist among Han Chinese populations across the three geographical regions:Zhengzhou,Taizhou,and Nanning.We identifed 1560 shared features from extracted phenotypes,which characterized well the basic facial morphology of the Han Chinese.In particular,heterogeneous phenotypes showing population structures corresponded to geographical subpopulations.The greatest facial variation among these geographical populations was the angle of glabella,left subalare,and right cheilion(p=3.4×10^(−161)).Interestingly,we found that Han Chinese populations could be classifed into northern Han,central Han,and southern Han at the phenotypic level,and the facial morphological variation pattern of central Han Chinese was between the typical diferentiation of northern and southern Han Chinese.This result was highly consistent with the results revealed by the genetic data.These fndings provide new insights into the analysis of multidimensional phenotypes as well as a valuable resource for further facial phenotype-genotype association studies in Han Chinese and East Asian populations.

A genome-wide association study of facial morphology identifies novel genetic loci in Han Chinese

The human face is a heritable surface with many complex sensory organs.In recent years,many genetic loci associated with facial features have been reported in different populations,yet there is a lack of studies on the Han Chinese population.Here,we report a genome-wide association study of 3D normal human faces of 2,659 Han Chinese with autosegment phenotypes of facial morphology.We identify singlenucleotide polymorphisms(SNPs)encompassing four genomic regions showing significant associations with different facial regions,including SNPs in DENND1 B associated with the chin,SNPs among PISRT1 associated with eyes,SNPs between DCHS2 and SFRP2 associated with the nose,and SNPs in VPS13 B associated with the nose.We replicate 24 SNPs from previously reported genetic loci in different populations,whose candidate genes are DCHS2,SUPT3 H,HOXD1,SOX9,PAX3,and EDAR.These results provide a more comprehensive understanding of the genetic basis of variation in human facial morphology.

Development and accuracy of artificial intelligence-generated prediction of facial changes in orthodontic treatment:a scoping review

Artificial intelligence(AI)has been utilized in soft-tissue analysis and prediction in orthodontic treatment planning,although its reliability has not been systematically assessed.This scoping review was conducted to outline the development of AI in terms of predicting soft-tissue changes after orthodontic treatment,as well as to comprehensively evaluate its prediction accuracy.Six electronic databases(PubMed,EBSCOhost,Web of Science,Embase,Cochrane Library,and Scopus)were searched up to March 14,2023.Clinical studies investigating the performance of AI-based systems in predicting post-orthodontic soft-tissue alterations were included.The Quality Assessment of Diagnostic Accuracy Studies-2(QUADAS-2)and Joanna Briggs Institute(JBI)appraisal checklist for diagnostic test accuracy studies were applied to assess risk of bias,while the Grading of Recommendation,Assessment,Development,and Evaluation(GRADE)assessment was conducted to evaluate the certainty of outcomes.After screening 2500 studies,four non-randomized clinical trials were finally included for full-text evaluation.We found a low level of evidence indicating an estimated high overall accuracy of AI-generated prediction,whereas the lower lip and chin seemed to be the least predictable regions.Furthermore,the facial morphology simulated by AI via the fusion of multimodality images was considered to be reasonably true.Since all of the included studies that were not randomized clinical trials(non-RCTs)showed a moderate to high risk of bias,more well-designed clinical trials with sufficient sample size are needed in future work.

A Facial Size Automatic Measurement and Analysis Technology

Facial measurement and analysis is an important part of anthropometry,which provides data support for the design of facial protective equipment.To overcome the inconveniences,low efficiency and poor measurement accuracy of facial size parameters measured and analyzed by manual contact,a method of automatic measurement and analysis of face size parameters is proposed.First,the automatic marking method of faces based on deep learning can improve the efficiency ofmeasuring facial parameters.Then,facial parameters,including nose middle width,nose width,face width and eye width,can be measured.Finally,the data set of face size parameters is classified and counted based on fuzzy clustering analysis.Sixty-five groups of Han youth facial data are collected formeasurement and analysis,and compared with the existing algorithms,the facial morphology analysis system presented in this paper has higher measurement accuracy.