Background:Donor nerve selection is a crucial factor in determining clinical outcomes of facial reanimation.Although dual innervation approaches using two neurotizers have shown promise,there is a lack of evidence-bas...Background:Donor nerve selection is a crucial factor in determining clinical outcomes of facial reanimation.Although dual innervation approaches using two neurotizers have shown promise,there is a lack of evidence-based comparison in the literature.Furthermore,no animal model of dual reinnervation has yet been published.This study aimed to establish such a model and verify its technical and anatomical feasibility by performing dual-innervated reanimation approaches in Wistar rats.Methods:Fifteen Wistar rats were divided into four experimental groups and one control group.The sural nerve was exposed and used as a cross-face nerve graft(CFNG),which was then anastomosed to the contralateral buccal branch of the facial nerve through a subcutaneous tunnel on the forehead.The CFNG,the masseteric nerve(MN),and the recipient nerve were coapted in one or two stages.The length and width of the utilized structures were measured under an operating microscope.Return of whisker motion was visually confirmed.Results:Nine out of the eleven rats that underwent surgery survived the procedure.Whisker motion was observed in all experimental animals,indicating successful reinnervation.The mean duration of the surgical procedures did not differ significantly between the experimental groups,ensuring similar conditions for all groups.Conclusions:Our experimental study confirmed that the proposed reanimation model in Wistar rats is anatomically and technically feasible,with a high success rate,and shows good prospects for future experiments.展开更多
Facial morphology,a complex trait influenced by genetics,holds great significance in evolutionary research.However,due to limited fossil evidence,the facial characteristics of Neanderthals and Denisovans have remained...Facial morphology,a complex trait influenced by genetics,holds great significance in evolutionary research.However,due to limited fossil evidence,the facial characteristics of Neanderthals and Denisovans have remained largely unknown.In this study,we conduct a large-scale multi-ethnic meta-analysis of the genome-wide association study(GWAS),including 9674 East Asians and 10,115 Europeans,quantitatively assessing 78 facial traits using 3D facial images.We identify 71 genomic loci associated with facial features,including 21 novel loci.We develop a facial polygenic score(FPS)that enables the prediction of facial features based on genetic information.Interestingly,the distribution of FPSs among populations from diverse continental groups exhibits relevant correlations with observed facial features.Furthermore,we apply the FPS to predict the facial traits of seven Neanderthals and one Denisovan using ancient DNA and align predictions with the fossil records.Our results suggest that Neanderthals and Denisovans likely share similar facial features,such as a wider but shorter nose and a wider endocanthion distance.The decreased mouth width is characterized specifically in Denisovans.The integration of genomic data and facial trait analysis provides valuable insights into the evolutionary history and adaptive changes in human facial morphology.展开更多
In computer vision and artificial intelligence,automatic facial expression-based emotion identification of humans has become a popular research and industry problem.Recent demonstrations and applications in several fi...In computer vision and artificial intelligence,automatic facial expression-based emotion identification of humans has become a popular research and industry problem.Recent demonstrations and applications in several fields,including computer games,smart homes,expression analysis,gesture recognition,surveillance films,depression therapy,patientmonitoring,anxiety,and others,have brought attention to its significant academic and commercial importance.This study emphasizes research that has only employed facial images for face expression recognition(FER),because facial expressions are a basic way that people communicate meaning to each other.The immense achievement of deep learning has resulted in a growing use of its much architecture to enhance efficiency.This review is on machine learning,deep learning,and hybrid methods’use of preprocessing,augmentation techniques,and feature extraction for temporal properties of successive frames of data.The following section gives a brief summary of assessment criteria that are accessible to the public and then compares them with benchmark results the most trustworthy way to assess FER-related research topics statistically.In this review,a brief synopsis of the subject matter may be beneficial for novices in the field of FER as well as seasoned scholars seeking fruitful avenues for further investigation.The information conveys fundamental knowledge and provides a comprehensive understanding of the most recent state-of-the-art research.展开更多
Automated behavior monitoring of macaques offers transformative potential for advancing biomedical research and animal welfare.However,reliably identifying individual macaques in group environments remains a significa...Automated behavior monitoring of macaques offers transformative potential for advancing biomedical research and animal welfare.However,reliably identifying individual macaques in group environments remains a significant challenge.This study introduces ACE-YOLOX,a lightweight facial recognition model tailored for captive macaques.ACE-YOLOX incorporates Efficient Channel Attention(ECA),Complete Intersection over Union loss(CIoU),and Adaptive Spatial Feature Fusion(ASFF)into the YOLOX framework,enhancing prediction accuracy while reducing computational complexity.These integrated approaches enable effective multiscale feature extraction.Using a dataset comprising 179400 labeled facial images from 1196 macaques,ACE-YOLOX surpassed the performance of classical object detection models,demonstrating superior accuracy and real-time processing capabilities.An Android application was also developed to deploy ACE-YOLOX on smartphones,enabling on-device,real-time macaque recognition.Our experimental results highlight the potential of ACE-YOLOX as a non-invasive identification tool,offering an important foundation for future studies in macaque facial expression recognition,cognitive psychology,and social behavior.展开更多
Artificial intelligence,such as deep learning technology,has advanced the study of facial expression recognition since facial expression carries rich emotional information and is significant for many naturalistic situ...Artificial intelligence,such as deep learning technology,has advanced the study of facial expression recognition since facial expression carries rich emotional information and is significant for many naturalistic situations.To pursue a high facial expression recognition accuracy,the network model of deep learning is generally designed to be very deep while the model’s real-time performance is typically constrained and limited.With MobileNetV3,a lightweight model with a good accuracy,a further study is conducted by adding a basic ResNet module to each of its existing modules and an SSH(Single Stage Headless Face Detector)context module to expand the model’s perceptual field.In this article,the enhanced model named Res-MobileNetV3,could alleviate the subpar of real-time performance and compress the size of large network models,which can process information at a rate of up to 33 frames per second.Although the improved model has been verified to be slightly inferior to the current state-of-the-art method in aspect of accuracy rate on the publically available face expression datasets,it can bring a good balance on accuracy,real-time performance,model size and model complexity in practical applications.展开更多
Objective To explore the feasibility of constructing a lung cancer early-warning risk model based on facial image features,providing novel insights into the early screening of lung cancer.Methods This study included p...Objective To explore the feasibility of constructing a lung cancer early-warning risk model based on facial image features,providing novel insights into the early screening of lung cancer.Methods This study included patients with pulmonary nodules diagnosed at the Physical Examination Center of Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine from November 1,2019 to December 31,2024,as well as patients with lung cancer diagnosed in the Oncology Departments of Yueyang Hospital of Integrated Traditional Chinese and Western Medicine and Longhua Hospital during the same period.The facial image information of patients with pulmonary nodules and lung cancer was collected using the TFDA-1 tongue and facial diagnosis instrument,and the facial diagnosis features were extracted from it by deep learning technology.Statistical analysis was conducted on the objective facial diagnosis characteristics of the two groups of participants to explore the differences in their facial image characteristics,and the least absolute shrinkage and selection operator(LASSO)regression was used to screen the characteristic variables.Based on the screened feature variables,four machine learning methods:random forest,logistic regression,support vector machine(SVM),and gradient boosting decision tree(GBDT)were used to establish lung cancer classification models independently.Meanwhile,the model performance was evaluated by indicators such as sensitivity,specificity,F1 score,precision,accuracy,the area under the receiver operating characteristic(ROC)curve(AUC),and the area under the precision-recall curve(AP).Results A total of 1275 patients with pulmonary nodules and 1623 patients with lung cancer were included in this study.After propensity score matching(PSM)to adjust for gender and age,535 patients were finally included in the pulmonary nodule group and the lung cancer group,respectively.There were significant differences in multiple color space metrics(such as R,G,B,V,L,a,b,Cr,H,Y,and Cb)and texture metrics[such as gray-levcl co-occurrence matrix(GLCM)-contrast(CON)and GLCM-inverse different moment(IDM)]between the two groups of individuals with pulmonary nodules and lung cancer(P<0.05).To construct a classification model,LASSO regression was used to select 63 key features from the initial 136 facial features.Based on this feature set,the SVM model demonstrated the best performance after 10-fold stratified cross-validation.The model achieved an average AUC of 0.8729 and average accuracy of 0.7990 on the internal test set.Further validation on an independent test set confirmed the model’s robust performance(AUC=0.8233,accuracy=0.7290),indicating its good generalization ability.Feature importance analysis demonstrated that color space indicators and the whole/lip Cr components(including color-B-0,wholecolor-Cr,and lipcolor-Cr)were the core factors in the model’s classification decisions,while texture indicators[GLCM-angular second moment(ASM)_2,GLCM-IDM_1,GLCM-CON_1,GLCM-entropy(ENT)_2]played an important auxiliary role.Conclusion The facial image features of patients with lung cancer and pulmonary nodules show significant differences in color and texture characteristics in multiple areas.The various models constructed based on facial image features all demonstrate good performance,indicating that facial image features can serve as potential biomarkers for lung cancer risk prediction,providing a non-invasive and feasible new approach for early lung cancer screening.展开更多
The dynamics of student engagement and emotional states significantly influence learning outcomes.Positive emotions resulting from successful task completion stand in contrast to negative affective states that arise f...The dynamics of student engagement and emotional states significantly influence learning outcomes.Positive emotions resulting from successful task completion stand in contrast to negative affective states that arise from learning struggles or failures.Effective transitions to engagement occur upon problem resolution,while unresolved issues lead to frustration and subsequent boredom.This study proposes a Convolutional Neural Networks(CNN)based approach utilizing the Multi⁃source Academic Affective Engagement Dataset(MAAED)to categorize facial expressions into boredom,confusion,frustration,and yawning.This method provides an efficient and objective way to assess student engagement by extracting features from facial images.Recognizing and addressing negative affective states,such as confusion and boredom,is fundamental in creating supportive learning environments.Through automated frame extraction and model comparison,this study demonstrates reduced loss values with improving accuracy,showcasing the effectiveness of this method in objectively evaluating student engagement.Monitoring facial engagement with CNN using the MAAED dataset is essential for gaining insights into human behaviour and improving educational experiences.展开更多
Multimedia semantic communication has been receiving increasing attention due to its significant enhancement of communication efficiency.Semantic coding,which is oriented towards extracting and encoding the key semant...Multimedia semantic communication has been receiving increasing attention due to its significant enhancement of communication efficiency.Semantic coding,which is oriented towards extracting and encoding the key semantics of video for transmission,is a key aspect in the framework of multimedia semantic communication.In this paper,we propose a facial video semantic coding method with low bitrate based on the temporal continuity of video semantics.At the sender’s end,we selectively transmit facial keypoints and deformation information,allocating distinct bitrates to different keypoints across frames.Compressive techniques involving sampling and quantization are employed to reduce the bitrate while retaining facial key semantic information.At the receiver’s end,a GAN-based generative network is utilized for reconstruction,effectively mitigating block artifacts and buffering problems present in traditional codec algorithms under low bitrates.The performance of the proposed approach is validated on multiple datasets,such as VoxCeleb and TalkingHead-1kH,employing metrics such as LPIPS,DISTS,and AKD for assessment.Experimental results demonstrate significant advantages over traditional codec methods,achieving up to approximately 10-fold bitrate reduction in prolonged,stable head pose scenarios across diverse conversational video settings.展开更多
BACKGROUND Aging is an inevitable aspect of human life,characterized by the gradual decline in the function of individual cells and structural components,including bones,muscles,and ligaments.AIM To evaluate the clini...BACKGROUND Aging is an inevitable aspect of human life,characterized by the gradual decline in the function of individual cells and structural components,including bones,muscles,and ligaments.AIM To evaluate the clinical effects of radiofrequency technology in treating facial skin wrinkles and laxity.METHODS This study included 60 female patients,aged 36-58 years(mean age 47.71±1.56 years),who received focused radiofrequency technology treatment for facial wrinkles and laxity in the Department of Medical Cosmetology at our hospital between January 2021 and June 2022.Each patient underwent three treatment sessions,one every two months.Facial photographs were taken before treatment and one week after the final session.A single physician assessed wrinkle severity using a standardized wrinkle severity scale,and patients completed a satisfaction questionnaire one week after the last treatment.RESULTS After three consecutive radiofrequency treatments,performed every two months,patients exhibited significantly reduced wrinkles and skin laxity compared to baseline.One week after the third treatment,the mean facial wrinkle severity score had significantly decreased from 3.00±0.79 to 2.71±0.47(t=2.58,P<0.05).Additionally,88.24%of patients reported noticeable improvements in facial wrinkles and skin laxity.No serious adverse reactions occurred during or follow-ing treatment.CONCLUSION Radiofrequency technology demonstrates significant clinical efficacy in improving facial skin wrinkles and laxity.展开更多
Ten years ago,Drolma-then selling facial masks on the streets of Lhasa-could hardly have imagined that the"Tibetan medicinal facial mask"sheand her partner were developing would one day appear in the central...Ten years ago,Drolma-then selling facial masks on the streets of Lhasa-could hardly have imagined that the"Tibetan medicinal facial mask"sheand her partner were developing would one day appear in the central exhibition hall of an international showcase.展开更多
Autism spectrum disorder(ASD)is a multifaceted neurological developmental condition that manifests in several ways.Nearly all autistic children remain undiagnosed before the age of three.Developmental problems affecti...Autism spectrum disorder(ASD)is a multifaceted neurological developmental condition that manifests in several ways.Nearly all autistic children remain undiagnosed before the age of three.Developmental problems affecting face features are often associated with fundamental brain disorders.The facial evolution of newborns with ASD is quite different from that of typically developing children.Early recognition is very significant to aid families and parents in superstition and denial.Distinguishing facial features from typically developing children is an evident manner to detect children analyzed with ASD.Presently,artificial intelligence(AI)significantly contributes to the emerging computer-aided diagnosis(CAD)of autism and to the evolving interactivemethods that aid in the treatment and reintegration of autistic patients.This study introduces an Ensemble of deep learning models based on the autism spectrum disorder detection in facial images(EDLM-ASDDFI)model.The overarching goal of the EDLM-ASDDFI model is to recognize the difference between facial images of individuals with ASD and normal controls.In the EDLM-ASDDFI method,the primary level of data pre-processing is involved by Gabor filtering(GF).Besides,the EDLM-ASDDFI technique applies the MobileNetV2 model to learn complex features from the pre-processed data.For the ASD detection process,the EDLM-ASDDFI method uses ensemble techniques for classification procedure that encompasses long short-term memory(LSTM),deep belief network(DBN),and hybrid kernel extreme learning machine(HKELM).Finally,the hyperparameter selection of the three deep learning(DL)models can be implemented by the design of the crested porcupine optimizer(CPO)technique.An extensive experiment was conducted to emphasize the improved ASD detection performance of the EDLM-ASDDFI method.The simulation outcomes indicated that the EDLM-ASDDFI technique highlighted betterment over other existing models in terms of numerous performance measures.展开更多
Objective:To explore the clinical application effect of autologous fat granule transplantation in facial depression plastic surgery.Methods:A total of 98 patients with facial depression admitted to the plastic surgery...Objective:To explore the clinical application effect of autologous fat granule transplantation in facial depression plastic surgery.Methods:A total of 98 patients with facial depression admitted to the plastic surgery department of our hospital from January 2021 to December 2023 were selected and divided into observation group(49 cases)and control group(49 cases)according to the random number table method.The observation group was treated with autologous fat granule transplantation,while the control group was treated with hyaluronic acid filling.The total effective rate of treatment,incidence of postoperative complications,improvement indicators of facial morphology(depth of depression,symmetry),and effect maintenance rate after 6 months of follow-up were compared between the two groups.Results:The total effective rate of treatment in the observation group was 93.88%(46/49),which was significantly higher than that in the control group(79.59%,39/49)(P<0.05).The incidence of postoperative complications in the observation group was 6.12%(3/49),which was lower than that in the control group(20.41%,10/49)(P<0.05).One month after surgery,the depth of depression(1.23±0.31 mm)and symmetry(1.02±0.15 points)in the observation group were better than those in the control group(P<0.05).After 6 months of follow-up,the effect maintenance rate in the observation group was 89.80%(44/49),which was significantly higher than that in the control group(67.35%,33/49)(P<0.05).Conclusion:Autologous fat granule transplantation for the treatment of facial depression can significantly improve facial morphology,enhance treatment effect and patient satisfaction,reduce the incidence of complications,and maintain a more durable effect.It is a clinically preferred facial depression plastic surgery solution.展开更多
To overcome the deficiencies of single-modal emotion recognition based on facial expression or bodily posture in natural scenes,a spatial guidance and temporal enhancement(SG-TE)network is proposed for facial-bodily e...To overcome the deficiencies of single-modal emotion recognition based on facial expression or bodily posture in natural scenes,a spatial guidance and temporal enhancement(SG-TE)network is proposed for facial-bodily emotion recognition.First,ResNet50,DNN and spatial ransformer models are used to capture facial texture vectors,bodily skeleton vectors and wholebody geometric vectors,and an intraframe correlation attention guidance(S-CAG)mechanism,which guides the facial texture vector and the bodily skeleton vector by the whole-body geometric vector,is designed to exploit the spatial potential emotional correlation between face and posture.Second,an interframe significant segment enhancement(T-SSE)structure is embedded into a temporal transformer to enhance high emotional intensity frame information and avoid emotional asynchrony.Finally,an adaptive weight assignment(M-AWA)strategy is constructed to realise facial-bodily fusion.The experimental results on the BabyRobot Emotion Dataset(BRED)and Context-Aware Emotion Recognition(CAER)dataset indicate that the proposed network reaches accuracies of 81.61%and 89.39%,which are 9.61%and 9.46%higher than those of the baseline network,respectively.Compared with the state-of-the-art methods,the proposed method achieves 7.73%and 20.57%higher accuracy than single-modal methods based on facial expression or bodily posture,respectively,and 2.16%higher accuracy than the dual-modal methods based on facial-bodily fusion.Therefore,the proposed method,which adaptively fuses the complementary information of face and posture,improves the quality of emotion recognition in real-world scenarios.展开更多
This study investigated the impact of problematic mobile phone use(PMPU)on emotion recognition.The PMPU levels of 150 participants were measured using the standardized SAS-SV scale.Based on the SAS-SV cutoff scores,pa...This study investigated the impact of problematic mobile phone use(PMPU)on emotion recognition.The PMPU levels of 150 participants were measured using the standardized SAS-SV scale.Based on the SAS-SV cutoff scores,participants were divided into PMPU and Control groups.These participants completed two emotion recognition experiments involving facial emotion stimuli that had been manipulated to varying emotional intensities using Morph software.Experiment 1(n=75)assessed differences in facial emotion detection accuracy.Experiment 2(n=75),based on signal detection theory,examined differences in hit and false alarm rates across emotional expressions.The results showed that PMPU users demonstrated higher recognition accuracy rates for disgust faces but lower accuracy for happy faces.This indicates a tendency among PMPU users to prioritize specific negative emotions and may have impaired perception of positive emotions.Practically,incorporating diverse emotional stimuli into PMPU intervention may help alleviate the negative emotional focus bias associated with excessive mobile devices use.展开更多
In the event of the ever-increasing growth of the beauty industry and the burgeoning market for facial masks,high-performance and high-safety mask products have emerged.Among these,light-cured collagen peptide-based h...In the event of the ever-increasing growth of the beauty industry and the burgeoning market for facial masks,high-performance and high-safety mask products have emerged.Among these,light-cured collagen peptide-based hydrogels,which are non-toxic,photocurable natural materials,exhibit significant potential for use in facial masks.We developed a novel collagen peptide-lithium chloride hydrogel-based facial mask.Light-cured collagen peptide hydrogel is a non-toxic,light-activated natural material that holds considerable promise for application in facial masks.Nonetheless,there is a significant lack of effective methodologies for real-time assessment of skin quality currently available in the market.To address this deficiency,we have developed an innovative collagen peptide-lithium chloride hydrogel mask,which is characterized by exceptional transparency(98%within the visible spectrum of 400-800 nm),commendable tensile properties(tensile strength of 428.6±2.1 kPa,with a tensile strength increase of 123.9%),substantial water retention capacity(61%),and favorable antimicrobial efficacy(89%).The incorporation of lithium chloride enhances ionic conduction at the interface between the human body and hydrogel,thereby enabling quantitative evaluation of skin quality through impedance analysis.Our collagen peptide-lithium chloride hydrogel facial mask demonstrated effectiveness in distinguishing various skin types,including D+(severely dry),D(mildly to moderately dry),N(moderate),O(mildly to moderately oily),and O+(severely oily).This study presents significant opportunities for the advancement of hydrogel masks and provides a new application platform for polymer hydrogels.展开更多
Objective: To analyze the therapeutic effect of combining dental arch splint intermaxillary traction with rigid internal fixation for the treatment of facial comminuted fractures. Methods: Sixty patients with facial c...Objective: To analyze the therapeutic effect of combining dental arch splint intermaxillary traction with rigid internal fixation for the treatment of facial comminuted fractures. Methods: Sixty patients with facial comminuted fractures admitted for treatment between July 2023 and December 2024 were selected. Using a random number table method, 30 patients were assigned to the observation group, where moderate traction using a dental arch splint combined with rigid internal fixation was applied. Another 30 patients were assigned to the control group and only received dental arch splint traction treatment. The total effective rate, postoperative recovery indicators, periodontal status, complication rate, and quality of life scores were compared between the two groups. Results: The total effective rate in the observation group was higher than that in the control group. The postoperative recovery indicators and periodontal status in the observation group were superior to those in the control group. The complication rate and quality of life score were lower in the observation group compared to the control group, with P < 0.05. Conclusion: Combining dental arch splint intermaxillary traction with rigid internal fixation can improve the periodontal status and quality of life of patients with facial comminuted fractures, shorten postoperative recovery time, reduce various complications, and enhance surgical efficacy.展开更多
Objective To determine the correlation between traditional Chinese medicine(TCM)inspec-tion of spirit classification and the severity grade of depression based on facial features,offer-ing insights for intelligent int...Objective To determine the correlation between traditional Chinese medicine(TCM)inspec-tion of spirit classification and the severity grade of depression based on facial features,offer-ing insights for intelligent intergrated TCM and western medicine diagnosis of depression.Methods Using the Audio-Visual Emotion Challenge and Workshop(AVEC 2014)public dataset on depression,which conclude 150 interview videos,the samples were classified ac-cording to the TCM inspection of spirit classification:Deshen(得神,presence of spirit),Shaoshen(少神,insufficiency of spirit),and Shenluan(神乱,confusion of spirit).Meanwhile,based on Beck Depression Inventory-II(BDI-II)score for the severity grade of depression,the samples were divided into minimal(0-13,Q1),mild(14-19,Q2),moderate(20-28,Q3),and severe(29-63,Q4).Sixty-eight landmarks were extracted with a ResNet-50 network,and the feature extracion mode was stadardized.Random forest and support vectior machine(SVM)classifiers were used to predict TCM inspection of spirit classification and the severity grade of depression,respectively.A Chi-square test and Apriori association rule mining were then applied to quantify and explore the relationships.Results The analysis revealed a statistically significant and moderately strong association be-tween TCM spirit classification and the severity grade of depression,as confirmed by a Chi-square test(χ^(2)=14.04,P=0.029)with a Cramer’s V effect size of 0.243.Further exploration us-ing association rule mining identified the most compelling rule:“moderate depression(Q3)→Shenluan”.This rule demonstrated a support level of 5%,indicating this specific co-occur-rence was present in 5%of the cohort.Crucially,it achieved a high Confidence of 86%,mean-ing that among patients diagnosed with Q3,86%exhibited the Shenluan pattern according to TCM assessment.The substantial Lift of 2.37 signifies that the observed likelihood of Shenlu-an manifesting in Q3 patients is 2.37 times higher than would be expected by chance if these states were independent-compelling evidence of a highly non-random association.Conse-quently,Shenluan emerges as a distinct and core TCM diagnostic manifestation strongly linked to Q3,forming a clinically significant phenotype within this patient subgroup.展开更多
Bipolar disorder is a serious mental condition that may be caused by any kind of stress or emotional upset experienced by the patient.It affects a large percentage of people globally,who fluctuate between depression a...Bipolar disorder is a serious mental condition that may be caused by any kind of stress or emotional upset experienced by the patient.It affects a large percentage of people globally,who fluctuate between depression and mania,or vice versa.A pleasant or unpleasant mood is more than a reflection of a state of mind.Normally,it is a difficult task to analyze through physical examination due to a large patient-psychiatrist ratio,so automated procedures are the best options to diagnose and verify the severity of bipolar.In this research work,facial microexpressions have been used for bipolar detection using the proposed Convolutional Neural Network(CNN)-based model.Facial Action Coding System(FACS)is used to extract micro-expressions called Action Units(AUs)connected with sad,happy,and angry emotions.Experiments have been conducted on a dataset collected from Bahawal Victoria Hospital,Bahawalpur,Pakistan,Using the Patient Health Questionnaire-15(PHQ-15)to infer a patient’s mental state.The experimental results showed a validation accuracy of 98.99%for the proposed CNN modelwhile classification through extracted featuresUsing SupportVectorMachines(SVM),K-NearestNeighbour(KNN),and Decision Tree(DT)obtained 99.9%,98.7%,and 98.9%accuracy,respectively.Overall,the outcomes demonstrated the stated method’s superiority over the current best practices.展开更多
Facial emotion recognition(FER)has become a focal point of research due to its widespread applications,ranging from human-computer interaction to affective computing.While traditional FER techniques have relied on han...Facial emotion recognition(FER)has become a focal point of research due to its widespread applications,ranging from human-computer interaction to affective computing.While traditional FER techniques have relied on handcrafted features and classification models trained on image or video datasets,recent strides in artificial intelligence and deep learning(DL)have ushered in more sophisticated approaches.The research aims to develop a FER system using a Faster Region Convolutional Neural Network(FRCNN)and design a specialized FRCNN architecture tailored for facial emotion recognition,leveraging its ability to capture spatial hierarchies within localized regions of facial features.The proposed work enhances the accuracy and efficiency of facial emotion recognition.The proposed work comprises twomajor key components:Inception V3-based feature extraction and FRCNN-based emotion categorization.Extensive experimentation on Kaggle datasets validates the effectiveness of the proposed strategy,showcasing the FRCNN approach’s resilience and accuracy in identifying and categorizing facial expressions.The model’s overall performance metrics are compelling,with an accuracy of 98.4%,precision of 97.2%,and recall of 96.31%.This work introduces a perceptive deep learning-based FER method,contributing to the evolving landscape of emotion recognition technologies.The high accuracy and resilience demonstrated by the FRCNN approach underscore its potential for real-world applications.This research advances the field of FER and presents a compelling case for the practicality and efficacy of deep learning models in automating the understanding of facial emotions.展开更多
Pulse rate is one of the important characteristics of traditional Chinese medicine pulse diagnosis,and it is of great significance for determining the nature of cold and heat in diseases.The prediction of pulse rate b...Pulse rate is one of the important characteristics of traditional Chinese medicine pulse diagnosis,and it is of great significance for determining the nature of cold and heat in diseases.The prediction of pulse rate based on facial video is an exciting research field for getting palpation information by observation diagnosis.However,most studies focus on optimizing the algorithm based on a small sample of participants without systematically investigating multiple influencing factors.A total of 209 participants and 2,435 facial videos,based on our self-constructed Multi-Scene Sign Dataset and the public datasets,were used to perform a multi-level and multi-factor comprehensive comparison.The effects of different datasets,blood volume pulse signal extraction algorithms,region of interests,time windows,color spaces,pulse rate calculation methods,and video recording scenes were analyzed.Furthermore,we proposed a blood volume pulse signal quality optimization strategy based on the inverse Fourier transform and an improvement strategy for pulse rate estimation based on signal-to-noise ratio threshold sliding.We found that the effects of video estimation of pulse rate in the Multi-Scene Sign Dataset and Pulse Rate Detection Dataset were better than in other datasets.Compared with Fast independent component analysis and Single Channel algorithms,chrominance-based method and plane-orthogonal-to-skin algorithms have a more vital anti-interference ability and higher robustness.The performances of the five-organs fusion area and the full-face area were better than that of single sub-regions,and the fewer motion artifacts and better lighting can improve the precision of pulse rate estimation.展开更多
基金Grant Agency of Masaryk University,Grant/Award Number:MUNI/11/SUP/01/2020 and MUNI/A/1457/2021。
文摘Background:Donor nerve selection is a crucial factor in determining clinical outcomes of facial reanimation.Although dual innervation approaches using two neurotizers have shown promise,there is a lack of evidence-based comparison in the literature.Furthermore,no animal model of dual reinnervation has yet been published.This study aimed to establish such a model and verify its technical and anatomical feasibility by performing dual-innervated reanimation approaches in Wistar rats.Methods:Fifteen Wistar rats were divided into four experimental groups and one control group.The sural nerve was exposed and used as a cross-face nerve graft(CFNG),which was then anastomosed to the contralateral buccal branch of the facial nerve through a subcutaneous tunnel on the forehead.The CFNG,the masseteric nerve(MN),and the recipient nerve were coapted in one or two stages.The length and width of the utilized structures were measured under an operating microscope.Return of whisker motion was visually confirmed.Results:Nine out of the eleven rats that underwent surgery survived the procedure.Whisker motion was observed in all experimental animals,indicating successful reinnervation.The mean duration of the surgical procedures did not differ significantly between the experimental groups,ensuring similar conditions for all groups.Conclusions:Our experimental study confirmed that the proposed reanimation model in Wistar rats is anatomically and technically feasible,with a high success rate,and shows good prospects for future experiments.
基金funded by the following grants and contracts:Strategic Priority Research Program of the Chinese Academy of Sciences(XDB38020400 to S.W.)the National Natural Science Foundation of China(32325013 to S.W.,32271186 to J.T.,31900408 to M.Z.)+5 种基金the CAS Project for Young Scientists in Basic Research(YSBR-077 to S.W.)Shanghai Science and Technology Commission Excellent Academic Leaders Program(22XD1424700 to S.W.)CAMS Innovation Fund for Medical Sciences(2019-I2M-5-066 to L.J.and J.W.)Shanghai Municipal Science and Technology Major Project(2017SHZDZX01 to L.J.and S.W.)the National Science and Technology Basic Research Project(2015FY111700 to L.J.)the 111 Project(B13016 to L.J.).
文摘Facial morphology,a complex trait influenced by genetics,holds great significance in evolutionary research.However,due to limited fossil evidence,the facial characteristics of Neanderthals and Denisovans have remained largely unknown.In this study,we conduct a large-scale multi-ethnic meta-analysis of the genome-wide association study(GWAS),including 9674 East Asians and 10,115 Europeans,quantitatively assessing 78 facial traits using 3D facial images.We identify 71 genomic loci associated with facial features,including 21 novel loci.We develop a facial polygenic score(FPS)that enables the prediction of facial features based on genetic information.Interestingly,the distribution of FPSs among populations from diverse continental groups exhibits relevant correlations with observed facial features.Furthermore,we apply the FPS to predict the facial traits of seven Neanderthals and one Denisovan using ancient DNA and align predictions with the fossil records.Our results suggest that Neanderthals and Denisovans likely share similar facial features,such as a wider but shorter nose and a wider endocanthion distance.The decreased mouth width is characterized specifically in Denisovans.The integration of genomic data and facial trait analysis provides valuable insights into the evolutionary history and adaptive changes in human facial morphology.
文摘In computer vision and artificial intelligence,automatic facial expression-based emotion identification of humans has become a popular research and industry problem.Recent demonstrations and applications in several fields,including computer games,smart homes,expression analysis,gesture recognition,surveillance films,depression therapy,patientmonitoring,anxiety,and others,have brought attention to its significant academic and commercial importance.This study emphasizes research that has only employed facial images for face expression recognition(FER),because facial expressions are a basic way that people communicate meaning to each other.The immense achievement of deep learning has resulted in a growing use of its much architecture to enhance efficiency.This review is on machine learning,deep learning,and hybrid methods’use of preprocessing,augmentation techniques,and feature extraction for temporal properties of successive frames of data.The following section gives a brief summary of assessment criteria that are accessible to the public and then compares them with benchmark results the most trustworthy way to assess FER-related research topics statistically.In this review,a brief synopsis of the subject matter may be beneficial for novices in the field of FER as well as seasoned scholars seeking fruitful avenues for further investigation.The information conveys fundamental knowledge and provides a comprehensive understanding of the most recent state-of-the-art research.
基金supported by the grants from Yunnan Province(202305AH340006,202305AH340007)CAS Light of West China Program(xbzg-zdsys-202213)。
文摘Automated behavior monitoring of macaques offers transformative potential for advancing biomedical research and animal welfare.However,reliably identifying individual macaques in group environments remains a significant challenge.This study introduces ACE-YOLOX,a lightweight facial recognition model tailored for captive macaques.ACE-YOLOX incorporates Efficient Channel Attention(ECA),Complete Intersection over Union loss(CIoU),and Adaptive Spatial Feature Fusion(ASFF)into the YOLOX framework,enhancing prediction accuracy while reducing computational complexity.These integrated approaches enable effective multiscale feature extraction.Using a dataset comprising 179400 labeled facial images from 1196 macaques,ACE-YOLOX surpassed the performance of classical object detection models,demonstrating superior accuracy and real-time processing capabilities.An Android application was also developed to deploy ACE-YOLOX on smartphones,enabling on-device,real-time macaque recognition.Our experimental results highlight the potential of ACE-YOLOX as a non-invasive identification tool,offering an important foundation for future studies in macaque facial expression recognition,cognitive psychology,and social behavior.
基金supported by China Academy of Railway Sciences Corporation Limited(No.2021YJ127).
文摘Artificial intelligence,such as deep learning technology,has advanced the study of facial expression recognition since facial expression carries rich emotional information and is significant for many naturalistic situations.To pursue a high facial expression recognition accuracy,the network model of deep learning is generally designed to be very deep while the model’s real-time performance is typically constrained and limited.With MobileNetV3,a lightweight model with a good accuracy,a further study is conducted by adding a basic ResNet module to each of its existing modules and an SSH(Single Stage Headless Face Detector)context module to expand the model’s perceptual field.In this article,the enhanced model named Res-MobileNetV3,could alleviate the subpar of real-time performance and compress the size of large network models,which can process information at a rate of up to 33 frames per second.Although the improved model has been verified to be slightly inferior to the current state-of-the-art method in aspect of accuracy rate on the publically available face expression datasets,it can bring a good balance on accuracy,real-time performance,model size and model complexity in practical applications.
基金National Natural Science Foundation of China(82305090)Shanghai Municipal Health Commission(20234Y0168)National Key Research and Development Program of China (2017YFC1703301)。
文摘Objective To explore the feasibility of constructing a lung cancer early-warning risk model based on facial image features,providing novel insights into the early screening of lung cancer.Methods This study included patients with pulmonary nodules diagnosed at the Physical Examination Center of Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine from November 1,2019 to December 31,2024,as well as patients with lung cancer diagnosed in the Oncology Departments of Yueyang Hospital of Integrated Traditional Chinese and Western Medicine and Longhua Hospital during the same period.The facial image information of patients with pulmonary nodules and lung cancer was collected using the TFDA-1 tongue and facial diagnosis instrument,and the facial diagnosis features were extracted from it by deep learning technology.Statistical analysis was conducted on the objective facial diagnosis characteristics of the two groups of participants to explore the differences in their facial image characteristics,and the least absolute shrinkage and selection operator(LASSO)regression was used to screen the characteristic variables.Based on the screened feature variables,four machine learning methods:random forest,logistic regression,support vector machine(SVM),and gradient boosting decision tree(GBDT)were used to establish lung cancer classification models independently.Meanwhile,the model performance was evaluated by indicators such as sensitivity,specificity,F1 score,precision,accuracy,the area under the receiver operating characteristic(ROC)curve(AUC),and the area under the precision-recall curve(AP).Results A total of 1275 patients with pulmonary nodules and 1623 patients with lung cancer were included in this study.After propensity score matching(PSM)to adjust for gender and age,535 patients were finally included in the pulmonary nodule group and the lung cancer group,respectively.There were significant differences in multiple color space metrics(such as R,G,B,V,L,a,b,Cr,H,Y,and Cb)and texture metrics[such as gray-levcl co-occurrence matrix(GLCM)-contrast(CON)and GLCM-inverse different moment(IDM)]between the two groups of individuals with pulmonary nodules and lung cancer(P<0.05).To construct a classification model,LASSO regression was used to select 63 key features from the initial 136 facial features.Based on this feature set,the SVM model demonstrated the best performance after 10-fold stratified cross-validation.The model achieved an average AUC of 0.8729 and average accuracy of 0.7990 on the internal test set.Further validation on an independent test set confirmed the model’s robust performance(AUC=0.8233,accuracy=0.7290),indicating its good generalization ability.Feature importance analysis demonstrated that color space indicators and the whole/lip Cr components(including color-B-0,wholecolor-Cr,and lipcolor-Cr)were the core factors in the model’s classification decisions,while texture indicators[GLCM-angular second moment(ASM)_2,GLCM-IDM_1,GLCM-CON_1,GLCM-entropy(ENT)_2]played an important auxiliary role.Conclusion The facial image features of patients with lung cancer and pulmonary nodules show significant differences in color and texture characteristics in multiple areas.The various models constructed based on facial image features all demonstrate good performance,indicating that facial image features can serve as potential biomarkers for lung cancer risk prediction,providing a non-invasive and feasible new approach for early lung cancer screening.
文摘The dynamics of student engagement and emotional states significantly influence learning outcomes.Positive emotions resulting from successful task completion stand in contrast to negative affective states that arise from learning struggles or failures.Effective transitions to engagement occur upon problem resolution,while unresolved issues lead to frustration and subsequent boredom.This study proposes a Convolutional Neural Networks(CNN)based approach utilizing the Multi⁃source Academic Affective Engagement Dataset(MAAED)to categorize facial expressions into boredom,confusion,frustration,and yawning.This method provides an efficient and objective way to assess student engagement by extracting features from facial images.Recognizing and addressing negative affective states,such as confusion and boredom,is fundamental in creating supportive learning environments.Through automated frame extraction and model comparison,this study demonstrates reduced loss values with improving accuracy,showcasing the effectiveness of this method in objectively evaluating student engagement.Monitoring facial engagement with CNN using the MAAED dataset is essential for gaining insights into human behaviour and improving educational experiences.
基金supported by the National Natural Science Foundation of China (Nos. NSFC 61925105, 62322109, 62171257 and U22B2001)the Xplorer Prize in Information and Electronics technologiesthe Tsinghua University (Department of Electronic Engineering)-Nantong Research Institute for Advanced Communication Technologies Joint Research Center for Space, Air, Ground and Sea Cooperative Communication Network Technology
文摘Multimedia semantic communication has been receiving increasing attention due to its significant enhancement of communication efficiency.Semantic coding,which is oriented towards extracting and encoding the key semantics of video for transmission,is a key aspect in the framework of multimedia semantic communication.In this paper,we propose a facial video semantic coding method with low bitrate based on the temporal continuity of video semantics.At the sender’s end,we selectively transmit facial keypoints and deformation information,allocating distinct bitrates to different keypoints across frames.Compressive techniques involving sampling and quantization are employed to reduce the bitrate while retaining facial key semantic information.At the receiver’s end,a GAN-based generative network is utilized for reconstruction,effectively mitigating block artifacts and buffering problems present in traditional codec algorithms under low bitrates.The performance of the proposed approach is validated on multiple datasets,such as VoxCeleb and TalkingHead-1kH,employing metrics such as LPIPS,DISTS,and AKD for assessment.Experimental results demonstrate significant advantages over traditional codec methods,achieving up to approximately 10-fold bitrate reduction in prolonged,stable head pose scenarios across diverse conversational video settings.
文摘BACKGROUND Aging is an inevitable aspect of human life,characterized by the gradual decline in the function of individual cells and structural components,including bones,muscles,and ligaments.AIM To evaluate the clinical effects of radiofrequency technology in treating facial skin wrinkles and laxity.METHODS This study included 60 female patients,aged 36-58 years(mean age 47.71±1.56 years),who received focused radiofrequency technology treatment for facial wrinkles and laxity in the Department of Medical Cosmetology at our hospital between January 2021 and June 2022.Each patient underwent three treatment sessions,one every two months.Facial photographs were taken before treatment and one week after the final session.A single physician assessed wrinkle severity using a standardized wrinkle severity scale,and patients completed a satisfaction questionnaire one week after the last treatment.RESULTS After three consecutive radiofrequency treatments,performed every two months,patients exhibited significantly reduced wrinkles and skin laxity compared to baseline.One week after the third treatment,the mean facial wrinkle severity score had significantly decreased from 3.00±0.79 to 2.71±0.47(t=2.58,P<0.05).Additionally,88.24%of patients reported noticeable improvements in facial wrinkles and skin laxity.No serious adverse reactions occurred during or follow-ing treatment.CONCLUSION Radiofrequency technology demonstrates significant clinical efficacy in improving facial skin wrinkles and laxity.
文摘Ten years ago,Drolma-then selling facial masks on the streets of Lhasa-could hardly have imagined that the"Tibetan medicinal facial mask"sheand her partner were developing would one day appear in the central exhibition hall of an international showcase.
基金Researchers supporting Project number(RSPD2025R1107),King Saud University,Riyadh,Saudi Arabia.
文摘Autism spectrum disorder(ASD)is a multifaceted neurological developmental condition that manifests in several ways.Nearly all autistic children remain undiagnosed before the age of three.Developmental problems affecting face features are often associated with fundamental brain disorders.The facial evolution of newborns with ASD is quite different from that of typically developing children.Early recognition is very significant to aid families and parents in superstition and denial.Distinguishing facial features from typically developing children is an evident manner to detect children analyzed with ASD.Presently,artificial intelligence(AI)significantly contributes to the emerging computer-aided diagnosis(CAD)of autism and to the evolving interactivemethods that aid in the treatment and reintegration of autistic patients.This study introduces an Ensemble of deep learning models based on the autism spectrum disorder detection in facial images(EDLM-ASDDFI)model.The overarching goal of the EDLM-ASDDFI model is to recognize the difference between facial images of individuals with ASD and normal controls.In the EDLM-ASDDFI method,the primary level of data pre-processing is involved by Gabor filtering(GF).Besides,the EDLM-ASDDFI technique applies the MobileNetV2 model to learn complex features from the pre-processed data.For the ASD detection process,the EDLM-ASDDFI method uses ensemble techniques for classification procedure that encompasses long short-term memory(LSTM),deep belief network(DBN),and hybrid kernel extreme learning machine(HKELM).Finally,the hyperparameter selection of the three deep learning(DL)models can be implemented by the design of the crested porcupine optimizer(CPO)technique.An extensive experiment was conducted to emphasize the improved ASD detection performance of the EDLM-ASDDFI method.The simulation outcomes indicated that the EDLM-ASDDFI technique highlighted betterment over other existing models in terms of numerous performance measures.
文摘Objective:To explore the clinical application effect of autologous fat granule transplantation in facial depression plastic surgery.Methods:A total of 98 patients with facial depression admitted to the plastic surgery department of our hospital from January 2021 to December 2023 were selected and divided into observation group(49 cases)and control group(49 cases)according to the random number table method.The observation group was treated with autologous fat granule transplantation,while the control group was treated with hyaluronic acid filling.The total effective rate of treatment,incidence of postoperative complications,improvement indicators of facial morphology(depth of depression,symmetry),and effect maintenance rate after 6 months of follow-up were compared between the two groups.Results:The total effective rate of treatment in the observation group was 93.88%(46/49),which was significantly higher than that in the control group(79.59%,39/49)(P<0.05).The incidence of postoperative complications in the observation group was 6.12%(3/49),which was lower than that in the control group(20.41%,10/49)(P<0.05).One month after surgery,the depth of depression(1.23±0.31 mm)and symmetry(1.02±0.15 points)in the observation group were better than those in the control group(P<0.05).After 6 months of follow-up,the effect maintenance rate in the observation group was 89.80%(44/49),which was significantly higher than that in the control group(67.35%,33/49)(P<0.05).Conclusion:Autologous fat granule transplantation for the treatment of facial depression can significantly improve facial morphology,enhance treatment effect and patient satisfaction,reduce the incidence of complications,and maintain a more durable effect.It is a clinically preferred facial depression plastic surgery solution.
基金National Natural Science Foundation of China,Grant/Award Number:62176084,Natural Science Foundation of Anhui Province of China,Grant/Award Number:1908085MF195,Natural Science Research Project of the Education Department of Anhui Province of China Grant/Award Numbers:2022AH051038,2023AH050474 and 2023AH050490.
文摘To overcome the deficiencies of single-modal emotion recognition based on facial expression or bodily posture in natural scenes,a spatial guidance and temporal enhancement(SG-TE)network is proposed for facial-bodily emotion recognition.First,ResNet50,DNN and spatial ransformer models are used to capture facial texture vectors,bodily skeleton vectors and wholebody geometric vectors,and an intraframe correlation attention guidance(S-CAG)mechanism,which guides the facial texture vector and the bodily skeleton vector by the whole-body geometric vector,is designed to exploit the spatial potential emotional correlation between face and posture.Second,an interframe significant segment enhancement(T-SSE)structure is embedded into a temporal transformer to enhance high emotional intensity frame information and avoid emotional asynchrony.Finally,an adaptive weight assignment(M-AWA)strategy is constructed to realise facial-bodily fusion.The experimental results on the BabyRobot Emotion Dataset(BRED)and Context-Aware Emotion Recognition(CAER)dataset indicate that the proposed network reaches accuracies of 81.61%and 89.39%,which are 9.61%and 9.46%higher than those of the baseline network,respectively.Compared with the state-of-the-art methods,the proposed method achieves 7.73%and 20.57%higher accuracy than single-modal methods based on facial expression or bodily posture,respectively,and 2.16%higher accuracy than the dual-modal methods based on facial-bodily fusion.Therefore,the proposed method,which adaptively fuses the complementary information of face and posture,improves the quality of emotion recognition in real-world scenarios.
基金supported by the National Social Science Fund of China(Grant Number:20BSH134).
文摘This study investigated the impact of problematic mobile phone use(PMPU)on emotion recognition.The PMPU levels of 150 participants were measured using the standardized SAS-SV scale.Based on the SAS-SV cutoff scores,participants were divided into PMPU and Control groups.These participants completed two emotion recognition experiments involving facial emotion stimuli that had been manipulated to varying emotional intensities using Morph software.Experiment 1(n=75)assessed differences in facial emotion detection accuracy.Experiment 2(n=75),based on signal detection theory,examined differences in hit and false alarm rates across emotional expressions.The results showed that PMPU users demonstrated higher recognition accuracy rates for disgust faces but lower accuracy for happy faces.This indicates a tendency among PMPU users to prioritize specific negative emotions and may have impaired perception of positive emotions.Practically,incorporating diverse emotional stimuli into PMPU intervention may help alleviate the negative emotional focus bias associated with excessive mobile devices use.
基金financially supported by the National Natural Science Foundation of China(Nos.52275290 and 51905222)Natural Science Foundation of Jiangsu Province(No.BK20211068)+2 种基金Research Project of State Key Laboratory of Mechanical System and Vibration(No.MSV202419)Major Program of the National Natural Science Foundation of China for Basic Theory and Key Technology of Tri-Co Robots(No.92248301)the Opening Project of the Key Laboratory of Bionic Engineering(Ministry of Education),Jilin University(No.KF2023006)。
文摘In the event of the ever-increasing growth of the beauty industry and the burgeoning market for facial masks,high-performance and high-safety mask products have emerged.Among these,light-cured collagen peptide-based hydrogels,which are non-toxic,photocurable natural materials,exhibit significant potential for use in facial masks.We developed a novel collagen peptide-lithium chloride hydrogel-based facial mask.Light-cured collagen peptide hydrogel is a non-toxic,light-activated natural material that holds considerable promise for application in facial masks.Nonetheless,there is a significant lack of effective methodologies for real-time assessment of skin quality currently available in the market.To address this deficiency,we have developed an innovative collagen peptide-lithium chloride hydrogel mask,which is characterized by exceptional transparency(98%within the visible spectrum of 400-800 nm),commendable tensile properties(tensile strength of 428.6±2.1 kPa,with a tensile strength increase of 123.9%),substantial water retention capacity(61%),and favorable antimicrobial efficacy(89%).The incorporation of lithium chloride enhances ionic conduction at the interface between the human body and hydrogel,thereby enabling quantitative evaluation of skin quality through impedance analysis.Our collagen peptide-lithium chloride hydrogel facial mask demonstrated effectiveness in distinguishing various skin types,including D+(severely dry),D(mildly to moderately dry),N(moderate),O(mildly to moderately oily),and O+(severely oily).This study presents significant opportunities for the advancement of hydrogel masks and provides a new application platform for polymer hydrogels.
基金Special Support Program for Scientific and Technological Talent“Application and Impact of Dental Arch Splint Intermaxillary Traction Combined with Rigid Internal Fixation on Oral Health in Patients with Facial Fractures”(DX2023BR18)。
文摘Objective: To analyze the therapeutic effect of combining dental arch splint intermaxillary traction with rigid internal fixation for the treatment of facial comminuted fractures. Methods: Sixty patients with facial comminuted fractures admitted for treatment between July 2023 and December 2024 were selected. Using a random number table method, 30 patients were assigned to the observation group, where moderate traction using a dental arch splint combined with rigid internal fixation was applied. Another 30 patients were assigned to the control group and only received dental arch splint traction treatment. The total effective rate, postoperative recovery indicators, periodontal status, complication rate, and quality of life scores were compared between the two groups. Results: The total effective rate in the observation group was higher than that in the control group. The postoperative recovery indicators and periodontal status in the observation group were superior to those in the control group. The complication rate and quality of life score were lower in the observation group compared to the control group, with P < 0.05. Conclusion: Combining dental arch splint intermaxillary traction with rigid internal fixation can improve the periodontal status and quality of life of patients with facial comminuted fractures, shorten postoperative recovery time, reduce various complications, and enhance surgical efficacy.
基金Research and Development Plan of Key Areas of Hunan Science and Technology Department (2022SK2044)Clinical Research Center for Depressive Disorder in Hunan Province (2021SK4022)。
文摘Objective To determine the correlation between traditional Chinese medicine(TCM)inspec-tion of spirit classification and the severity grade of depression based on facial features,offer-ing insights for intelligent intergrated TCM and western medicine diagnosis of depression.Methods Using the Audio-Visual Emotion Challenge and Workshop(AVEC 2014)public dataset on depression,which conclude 150 interview videos,the samples were classified ac-cording to the TCM inspection of spirit classification:Deshen(得神,presence of spirit),Shaoshen(少神,insufficiency of spirit),and Shenluan(神乱,confusion of spirit).Meanwhile,based on Beck Depression Inventory-II(BDI-II)score for the severity grade of depression,the samples were divided into minimal(0-13,Q1),mild(14-19,Q2),moderate(20-28,Q3),and severe(29-63,Q4).Sixty-eight landmarks were extracted with a ResNet-50 network,and the feature extracion mode was stadardized.Random forest and support vectior machine(SVM)classifiers were used to predict TCM inspection of spirit classification and the severity grade of depression,respectively.A Chi-square test and Apriori association rule mining were then applied to quantify and explore the relationships.Results The analysis revealed a statistically significant and moderately strong association be-tween TCM spirit classification and the severity grade of depression,as confirmed by a Chi-square test(χ^(2)=14.04,P=0.029)with a Cramer’s V effect size of 0.243.Further exploration us-ing association rule mining identified the most compelling rule:“moderate depression(Q3)→Shenluan”.This rule demonstrated a support level of 5%,indicating this specific co-occur-rence was present in 5%of the cohort.Crucially,it achieved a high Confidence of 86%,mean-ing that among patients diagnosed with Q3,86%exhibited the Shenluan pattern according to TCM assessment.The substantial Lift of 2.37 signifies that the observed likelihood of Shenlu-an manifesting in Q3 patients is 2.37 times higher than would be expected by chance if these states were independent-compelling evidence of a highly non-random association.Conse-quently,Shenluan emerges as a distinct and core TCM diagnostic manifestation strongly linked to Q3,forming a clinically significant phenotype within this patient subgroup.
文摘Bipolar disorder is a serious mental condition that may be caused by any kind of stress or emotional upset experienced by the patient.It affects a large percentage of people globally,who fluctuate between depression and mania,or vice versa.A pleasant or unpleasant mood is more than a reflection of a state of mind.Normally,it is a difficult task to analyze through physical examination due to a large patient-psychiatrist ratio,so automated procedures are the best options to diagnose and verify the severity of bipolar.In this research work,facial microexpressions have been used for bipolar detection using the proposed Convolutional Neural Network(CNN)-based model.Facial Action Coding System(FACS)is used to extract micro-expressions called Action Units(AUs)connected with sad,happy,and angry emotions.Experiments have been conducted on a dataset collected from Bahawal Victoria Hospital,Bahawalpur,Pakistan,Using the Patient Health Questionnaire-15(PHQ-15)to infer a patient’s mental state.The experimental results showed a validation accuracy of 98.99%for the proposed CNN modelwhile classification through extracted featuresUsing SupportVectorMachines(SVM),K-NearestNeighbour(KNN),and Decision Tree(DT)obtained 99.9%,98.7%,and 98.9%accuracy,respectively.Overall,the outcomes demonstrated the stated method’s superiority over the current best practices.
文摘Facial emotion recognition(FER)has become a focal point of research due to its widespread applications,ranging from human-computer interaction to affective computing.While traditional FER techniques have relied on handcrafted features and classification models trained on image or video datasets,recent strides in artificial intelligence and deep learning(DL)have ushered in more sophisticated approaches.The research aims to develop a FER system using a Faster Region Convolutional Neural Network(FRCNN)and design a specialized FRCNN architecture tailored for facial emotion recognition,leveraging its ability to capture spatial hierarchies within localized regions of facial features.The proposed work enhances the accuracy and efficiency of facial emotion recognition.The proposed work comprises twomajor key components:Inception V3-based feature extraction and FRCNN-based emotion categorization.Extensive experimentation on Kaggle datasets validates the effectiveness of the proposed strategy,showcasing the FRCNN approach’s resilience and accuracy in identifying and categorizing facial expressions.The model’s overall performance metrics are compelling,with an accuracy of 98.4%,precision of 97.2%,and recall of 96.31%.This work introduces a perceptive deep learning-based FER method,contributing to the evolving landscape of emotion recognition technologies.The high accuracy and resilience demonstrated by the FRCNN approach underscore its potential for real-world applications.This research advances the field of FER and presents a compelling case for the practicality and efficacy of deep learning models in automating the understanding of facial emotions.
基金supported by the Key Research Program of the Chinese Academy of Sciences(grant number ZDRW-ZS-2021-1-2).
文摘Pulse rate is one of the important characteristics of traditional Chinese medicine pulse diagnosis,and it is of great significance for determining the nature of cold and heat in diseases.The prediction of pulse rate based on facial video is an exciting research field for getting palpation information by observation diagnosis.However,most studies focus on optimizing the algorithm based on a small sample of participants without systematically investigating multiple influencing factors.A total of 209 participants and 2,435 facial videos,based on our self-constructed Multi-Scene Sign Dataset and the public datasets,were used to perform a multi-level and multi-factor comprehensive comparison.The effects of different datasets,blood volume pulse signal extraction algorithms,region of interests,time windows,color spaces,pulse rate calculation methods,and video recording scenes were analyzed.Furthermore,we proposed a blood volume pulse signal quality optimization strategy based on the inverse Fourier transform and an improvement strategy for pulse rate estimation based on signal-to-noise ratio threshold sliding.We found that the effects of video estimation of pulse rate in the Multi-Scene Sign Dataset and Pulse Rate Detection Dataset were better than in other datasets.Compared with Fast independent component analysis and Single Channel algorithms,chrominance-based method and plane-orthogonal-to-skin algorithms have a more vital anti-interference ability and higher robustness.The performances of the five-organs fusion area and the full-face area were better than that of single sub-regions,and the fewer motion artifacts and better lighting can improve the precision of pulse rate estimation.
