Anthropometry can analyze the size,weight,and proportion of the human body objectively and quantitatively to supplement the visual assessment.Various non-invasive three-dimensional(3D)anthropometric techniques have be...Anthropometry can analyze the size,weight,and proportion of the human body objectively and quantitatively to supplement the visual assessment.Various non-invasive three-dimensional(3D)anthropometric techniques have been applied to assess soft tissues’3D morphology in the clinical practice.Among them,non-invasive stereophotogrammetry and laser scanning techniques are becoming increasingly popular in craniofacial surgery and plastic surgery.They have been applied for craniofacial growth estimation and morphometric investigation,genetic and acquired malformation diagnosis,as well as orthodontic or surgical treatment arrangement and outcome evaluation.However,few studies have been published for assessing the 3D morphology of soft tissues in the periorbital region.This paper reviews the studies involving the application and evaluation of the increasingly popular 3D photogrammetry in the periorbital region.These studies proposed detailed and standardized protocols for three-dimensionally assessing linear,curvilinear,angular,as well as volumetric measurements,and verified its high reliability in the periorbital region(even higher than caliper-derived direct measurements).In the future,reliable and accurate 3D imaging techniques,as well as standardized analyzing protocols,may find applications in following up morphological growth,preoperatively diagnosing and assessing patient periorbital conditions,planning surgical procedures,postoperatively evaluating treatment outcomes of a specific procedure,and comparing the differences in surgical results between various procedures,studies,as well as populations.展开更多
Soft Tissue Tumors(STT)are a form of sarcoma found in tissues that connect,support,and surround body structures.Because of their shallow frequency in the body and their great diversity,they appear to be heterogeneous ...Soft Tissue Tumors(STT)are a form of sarcoma found in tissues that connect,support,and surround body structures.Because of their shallow frequency in the body and their great diversity,they appear to be heterogeneous when observed through Magnetic Resonance Imaging(MRI).They are easily confused with other diseases such as fibroadenoma mammae,lymphadenopathy,and struma nodosa,and these diagnostic errors have a considerable detrimental effect on the medical treatment process of patients.Researchers have proposed several machine learning models to classify tumors,but none have adequately addressed this misdiagnosis problem.Also,similar studies that have proposed models for evaluation of such tumors mostly do not consider the heterogeneity and the size of the data.Therefore,we propose a machine learning-based approach which combines a new technique of preprocessing the data for features transformation,resampling techniques to eliminate the bias and the deviation of instability and performing classifier tests based on the Support Vector Machine(SVM)and Decision Tree(DT)algorithms.The tests carried out on dataset collected in Nur Hidayah Hospital of Yogyakarta in Indonesia show a great improvement compared to previous studies.These results confirm that machine learning methods could provide efficient and effective tools to reinforce the automatic decision-making processes of STT diagnostics.展开更多
基金This study was supported by the Koeln Fortune Program/Faculty of Medicine,University of Cologne,Germany(No.2680148101)the State Scholarship Fund from China Scholarship Council,China(No.201708080141).
文摘Anthropometry can analyze the size,weight,and proportion of the human body objectively and quantitatively to supplement the visual assessment.Various non-invasive three-dimensional(3D)anthropometric techniques have been applied to assess soft tissues’3D morphology in the clinical practice.Among them,non-invasive stereophotogrammetry and laser scanning techniques are becoming increasingly popular in craniofacial surgery and plastic surgery.They have been applied for craniofacial growth estimation and morphometric investigation,genetic and acquired malformation diagnosis,as well as orthodontic or surgical treatment arrangement and outcome evaluation.However,few studies have been published for assessing the 3D morphology of soft tissues in the periorbital region.This paper reviews the studies involving the application and evaluation of the increasingly popular 3D photogrammetry in the periorbital region.These studies proposed detailed and standardized protocols for three-dimensionally assessing linear,curvilinear,angular,as well as volumetric measurements,and verified its high reliability in the periorbital region(even higher than caliper-derived direct measurements).In the future,reliable and accurate 3D imaging techniques,as well as standardized analyzing protocols,may find applications in following up morphological growth,preoperatively diagnosing and assessing patient periorbital conditions,planning surgical procedures,postoperatively evaluating treatment outcomes of a specific procedure,and comparing the differences in surgical results between various procedures,studies,as well as populations.
文摘Soft Tissue Tumors(STT)are a form of sarcoma found in tissues that connect,support,and surround body structures.Because of their shallow frequency in the body and their great diversity,they appear to be heterogeneous when observed through Magnetic Resonance Imaging(MRI).They are easily confused with other diseases such as fibroadenoma mammae,lymphadenopathy,and struma nodosa,and these diagnostic errors have a considerable detrimental effect on the medical treatment process of patients.Researchers have proposed several machine learning models to classify tumors,but none have adequately addressed this misdiagnosis problem.Also,similar studies that have proposed models for evaluation of such tumors mostly do not consider the heterogeneity and the size of the data.Therefore,we propose a machine learning-based approach which combines a new technique of preprocessing the data for features transformation,resampling techniques to eliminate the bias and the deviation of instability and performing classifier tests based on the Support Vector Machine(SVM)and Decision Tree(DT)algorithms.The tests carried out on dataset collected in Nur Hidayah Hospital of Yogyakarta in Indonesia show a great improvement compared to previous studies.These results confirm that machine learning methods could provide efficient and effective tools to reinforce the automatic decision-making processes of STT diagnostics.
