Objective To identify the key features of facial and tongue images associated with anemia in female populations,establish anemia risk-screening models,and evaluate their performance.Methods A total of 533 female parti...Objective To identify the key features of facial and tongue images associated with anemia in female populations,establish anemia risk-screening models,and evaluate their performance.Methods A total of 533 female participants(anemic and healthy)were recruited from Shuguang Hospital.Facial and tongue images were collected using the TFDA-1 tongue and face diagnosis instrument.Color and texture features from various parts of facial and tongue images were extracted using Face Diagnosis Analysis System(FDAS)and Tongue Diagnosis Analysis System version 2.0(TDAS v2.0).Least Absolute Shrinkage and Selection Operator(LASSO)regression was used for feature selection.Ten machine learning models and one deep learning model(ResNet50V2+Conv1D)were developed and evaluated.Results Anemic women showed lower a-values,higher L-and b-values across all age groups.Texture features analysis showed that women aged 30–39 with anemia had higher angular second moment(ASM)and lower entropy(ENT)values in facial images,while those aged 40–49 had lower contrast(CON),ENT,and MEAN values in tongue images but higher ASM.Anemic women exhibited age-related trends similar to healthy women,with decreasing L-values and increasing a-,b-,and ASM-values.LASSO identified 19 key features from 62.Among classifiers,the Artificial Neural Network(ANN)model achieved the best performance[area under the curve(AUC):0.849,accuracy:0.781].The ResNet50V2 model achieved comparable results[AUC:0.846,accuracy:0.818].Conclusion Differences in facial and tongue images suggest that color and texture features can serve as potential TCM phenotype and auxiliary diagnostic indicators for female anemia.展开更多
基金Funding This research was funded by funding from the National Natural Science Foundation of China(No.82305090,No.82104738)Key Discipline Construction Project of High-level Traditional Chinese Medicine of the National Administration of Traditional Chinese Medicine-Traditional Chinese Medical Diagnostics(ZYYZDXK-2023069)+5 种基金Shanghai Municipal Health Commission Project(No.20234Y0168,No.2024QN018)Shanghai Science and Technology Commission Rising Star Cultivation Project(No.22YF1448900)Capacity Building of Local Colleges and Universities under the Shanghai Municipal Science and Technology Commission(21010504400)General Program of China Postdoctoral Science Foundation(2023M732337)Shanghai“Super Postdoctoral”Incentive Plan(2022509)Science and Technology Development Project of Shanghai University of Traditional Chinese Medicine(23KFL005).
文摘Objective To identify the key features of facial and tongue images associated with anemia in female populations,establish anemia risk-screening models,and evaluate their performance.Methods A total of 533 female participants(anemic and healthy)were recruited from Shuguang Hospital.Facial and tongue images were collected using the TFDA-1 tongue and face diagnosis instrument.Color and texture features from various parts of facial and tongue images were extracted using Face Diagnosis Analysis System(FDAS)and Tongue Diagnosis Analysis System version 2.0(TDAS v2.0).Least Absolute Shrinkage and Selection Operator(LASSO)regression was used for feature selection.Ten machine learning models and one deep learning model(ResNet50V2+Conv1D)were developed and evaluated.Results Anemic women showed lower a-values,higher L-and b-values across all age groups.Texture features analysis showed that women aged 30–39 with anemia had higher angular second moment(ASM)and lower entropy(ENT)values in facial images,while those aged 40–49 had lower contrast(CON),ENT,and MEAN values in tongue images but higher ASM.Anemic women exhibited age-related trends similar to healthy women,with decreasing L-values and increasing a-,b-,and ASM-values.LASSO identified 19 key features from 62.Among classifiers,the Artificial Neural Network(ANN)model achieved the best performance[area under the curve(AUC):0.849,accuracy:0.781].The ResNet50V2 model achieved comparable results[AUC:0.846,accuracy:0.818].Conclusion Differences in facial and tongue images suggest that color and texture features can serve as potential TCM phenotype and auxiliary diagnostic indicators for female anemia.
