Objective To construct a precise model for identifying traditional Chinese medicine(TCM)constitutions;thereby offering optimized guidance for clinical diagnosis and treatment plan-ning;and ultimately enhancing medical...Objective To construct a precise model for identifying traditional Chinese medicine(TCM)constitutions;thereby offering optimized guidance for clinical diagnosis and treatment plan-ning;and ultimately enhancing medical efficiency and treatment outcomes.Methods First;TCM full-body inspection data acquisition equipment was employed to col-lect full-body standing images of healthy people;from which the constitutions were labelled and defined in accordance with the Constitution in Chinese Medicine Questionnaire(CCMQ);and a dataset encompassing labelled constitutions was constructed.Second;heat-suppres-sion valve(HSV)color space and improved local binary patterns(LBP)algorithm were lever-aged for the extraction of features such as facial complexion and body shape.In addition;a dual-branch deep network was employed to collect deep features from the full-body standing images.Last;the random forest(RF)algorithm was utilized to learn the extracted multifea-tures;which were subsequently employed to establish a TCM constitution identification mod-el.Accuracy;precision;and F1 score were the three measures selected to assess the perfor-mance of the model.Results It was found that the accuracy;precision;and F1 score of the proposed model based on multifeatures for identifying TCM constitutions were 0.842;0.868;and 0.790;respectively.In comparison with the identification models that encompass a single feature;either a single facial complexion feature;a body shape feature;or deep features;the accuracy of the model that incorporating all the aforementioned features was elevated by 0.105;0.105;and 0.079;the precision increased by 0.164;0.164;and 0.211;and the F1 score rose by 0.071;0.071;and 0.084;respectively.Conclusion The research findings affirmed the viability of the proposed model;which incor-porated multifeatures;including the facial complexion feature;the body shape feature;and the deep feature.In addition;by employing the proposed model;the objectification and intel-ligence of identifying constitutions in TCM practices could be optimized.展开更多
目的:定量评价红蓝光对痤疮患者面部皮肤的作用效应。方法:随机选取2010年6月～2011年6月在笔者激光美容中心进行治疗的痤疮患者150例,应用红蓝光交替照射,2次/周,共4周,于治疗前、4次治疗后和8次治疗后用VISIA皮肤图像分析仪和SOFT5.5...目的:定量评价红蓝光对痤疮患者面部皮肤的作用效应。方法:随机选取2010年6月～2011年6月在笔者激光美容中心进行治疗的痤疮患者150例,应用红蓝光交替照射,2次/周,共4周,于治疗前、4次治疗后和8次治疗后用VISIA皮肤图像分析仪和SOFT5.5皮肤性质测试仪进行定量分析和评价。结果:VI SI A数据显示色素斑、皱纹、纹理和毛孔治疗前后未见明显差异性(P>0.05);与治疗前比较4次治疗后和8次治疗后紫质均有明显差异性(P<0.05),但治疗后4次和8次之间未见明显差异性(P>0.05)。SOFT数据显示水分和弹性治疗前后未见明显差异性(P>0.05);与治疗前比较4次治疗后油脂有明显差异性(P<0.05),与治疗前比较8次治疗后pH和油脂均有明显差异性(P<0.05),但治疗后4次和8次之间未见明显差异性(P>0.05)。结论:红蓝光对痤疮患者皮肤作用效应主要表现为油脂分泌减少,短期改变皮肤pH,但不会影响皮肤色素及水分。展开更多
基金National Key Research and Development Program of China(2022YFC3502302)National Natural Science Foundation of China(82074580)Graduate Research Innovation Program of Jiangsu Province(KYCX23_2078).
文摘Objective To construct a precise model for identifying traditional Chinese medicine(TCM)constitutions;thereby offering optimized guidance for clinical diagnosis and treatment plan-ning;and ultimately enhancing medical efficiency and treatment outcomes.Methods First;TCM full-body inspection data acquisition equipment was employed to col-lect full-body standing images of healthy people;from which the constitutions were labelled and defined in accordance with the Constitution in Chinese Medicine Questionnaire(CCMQ);and a dataset encompassing labelled constitutions was constructed.Second;heat-suppres-sion valve(HSV)color space and improved local binary patterns(LBP)algorithm were lever-aged for the extraction of features such as facial complexion and body shape.In addition;a dual-branch deep network was employed to collect deep features from the full-body standing images.Last;the random forest(RF)algorithm was utilized to learn the extracted multifea-tures;which were subsequently employed to establish a TCM constitution identification mod-el.Accuracy;precision;and F1 score were the three measures selected to assess the perfor-mance of the model.Results It was found that the accuracy;precision;and F1 score of the proposed model based on multifeatures for identifying TCM constitutions were 0.842;0.868;and 0.790;respectively.In comparison with the identification models that encompass a single feature;either a single facial complexion feature;a body shape feature;or deep features;the accuracy of the model that incorporating all the aforementioned features was elevated by 0.105;0.105;and 0.079;the precision increased by 0.164;0.164;and 0.211;and the F1 score rose by 0.071;0.071;and 0.084;respectively.Conclusion The research findings affirmed the viability of the proposed model;which incor-porated multifeatures;including the facial complexion feature;the body shape feature;and the deep feature.In addition;by employing the proposed model;the objectification and intel-ligence of identifying constitutions in TCM practices could be optimized.
文摘目的:定量评价红蓝光对痤疮患者面部皮肤的作用效应。方法:随机选取2010年6月～2011年6月在笔者激光美容中心进行治疗的痤疮患者150例,应用红蓝光交替照射,2次/周,共4周,于治疗前、4次治疗后和8次治疗后用VISIA皮肤图像分析仪和SOFT5.5皮肤性质测试仪进行定量分析和评价。结果:VI SI A数据显示色素斑、皱纹、纹理和毛孔治疗前后未见明显差异性(P>0.05);与治疗前比较4次治疗后和8次治疗后紫质均有明显差异性(P<0.05),但治疗后4次和8次之间未见明显差异性(P>0.05)。SOFT数据显示水分和弹性治疗前后未见明显差异性(P>0.05);与治疗前比较4次治疗后油脂有明显差异性(P<0.05),与治疗前比较8次治疗后pH和油脂均有明显差异性(P<0.05),但治疗后4次和8次之间未见明显差异性(P>0.05)。结论:红蓝光对痤疮患者皮肤作用效应主要表现为油脂分泌减少,短期改变皮肤pH,但不会影响皮肤色素及水分。
