BioMedical Engineering OnLine最新文献

{"title":"Health disorders in menopausal women: microbiome alterations, associated problems, and possible treatments.","authors":"Feiyun Lin, Lin Ma, Zhumei Sheng","doi":"10.1186/s12938-025-01415-3","DOIUrl":"10.1186/s12938-025-01415-3","url":null,"abstract":"<p><p>Perimenopause marks a critical transition in women's lives, characterized by declining estrogen levels that trigger profound physiological and psychological changes, impacting quality of life and increasing susceptibility to age-related degenerative diseases. This review systematically examines the intricate relationships among menopause, disease associations, microbiome alterations, and intervention strategies. Estrogen fluctuations disrupt the microbial balance in the vagina, intestine, urethra, and oral cavity, contributing to microecological imbalance and heightened disease risk. Menopause is closely linked to a spectrum of health issues, including reproductive system disorders (e.g., uterine fibroids, ovarian cancer microbiota changes), metabolic syndromes (obesity, type 2 diabetes), cardiovascular diseases (influenced by gut microbiota and dietary patterns), osteoporosis, and mental health disturbances. Current interventions-ranging from dietary modifications (cocoa polyphenols, dietary fiber, soy isoflavones) and menopausal hormone therapy (MHT) to probiotic supplementation, plant extracts (soybean, black cohosh, red clover), and traditional therapies-exhibit distinct advantages and limitations. Technological advancements in microbiome analysis, tissue processing, and cell isolation have revolutionized diagnostic and therapeutic approaches, while immune function, socioeconomic factors, and lifestyle choices significantly modulate health outcomes. Future research should prioritize exploring synergistic intervention strategies, developing personalized health management programs, and unraveling the mechanistic links between the microbiome and menopause-related diseases. This comprehensive synthesis aims to advance evidence-based strategies for improving the health and quality of life of menopausal women.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"24 1","pages":"84"},"PeriodicalIF":2.9,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12235801/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144582970","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing chronic obstructive pulmonary disease risk based on exhalation and cough sounds.","authors":"Geyi Wen, Chenshuo Wang, Wei Zhao, Jinliang Meng, Yanyan Xu, Ruiqi Wang, Zijing Zeng","doi":"10.1186/s12938-025-01420-6","DOIUrl":"10.1186/s12938-025-01420-6","url":null,"abstract":"<p><strong>Background and objective: </strong>Chronic obstructive pulmonary disease (COPD), a progressively worsening respiratory condition, severely impacts patient quality of life. Early risk assessment can improve treatment outcomes and lessen healthcare burdens. However, current early assessment methods are limited. This study seeks to develop innovative approaches for the early detection and evaluation of COPD.</p><p><strong>Methods: </strong>This study employed a cross-sectional design. Initially, we created a dedicated recording application deployed on smartphones to gather audio data from participants. Following this, each individual completed pulmonary function tests and participated in questionnaire surveys. COPD risk was defined as a pre-bronchodilator FEV₁/FVC ratio < 0.7 combined with a history of exposure to risk factors like smoking or biomass fuel. Ultimately, we assessed the feasibility of utilizing smartphones to capture exhalation and cough sounds for the identification of COPD risks through the application of machine learning algorithms.</p><p><strong>Results: </strong>We gathered valid data from 530 adults, of whom 171 met the criteria for being at risk of COPD. Utilizing the XGBoost algorithm, we achieved a precision of 0.98 and a recall of 0.89.</p><p><strong>Conclusions: </strong>Our study demonstrates that cough audio signals provide valuable insights for identifying COPD risk, effectively complementing exhalation signals in assessments. This approach is not only feasible and practical for real-world applications, but also offers an affordable and accessible solution, especially beneficial in resource-limited settings.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"24 1","pages":"82"},"PeriodicalIF":2.9,"publicationDate":"2025-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12228398/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144567031","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Combined therapy with contralateral controlled functional electrical stimulation and transcranial direct current stimulation for early post-stroke hand dysfunction.","authors":"Zibo Liu, Lichun Wang, Mushao Hou, Sha Li, Hongli Zhang, Hongling Li","doi":"10.1186/s12938-025-01417-1","DOIUrl":"10.1186/s12938-025-01417-1","url":null,"abstract":"<p><strong>Objectives: </strong>To explore the clinical efficacy of contralateral controlled functional electrical stimulation (CCFES) combined with transcranial direct current stimulation (tDCS) in the treatment of patients with early post-stroke hand dysfunction.</p><p><strong>Methods: </strong>Ninety patients with definitively diagnosed early post-stroke hand dysfunction were selected and divided into the control group (30 cases), experimental group 1 (EG1; 30 cases) and experimental group 2 (EG2; 30 cases) according to the random number table method. The control group received traditional therapy; experimental group 1 received tDCS in addition to standard care; experimental group 2 received both tDCS and CCFES in addition to standard treatments. Before and after treatment, all three groups were evaluated using the Fugl-Meyer assessment for upper extremity (FMA-UE), the functional test for the hemiplegic upper extremity-Hong Kong version (FTHUE-HK), the Modified Barthel Index (MBI), the Brunnstrom stages of hand, the Motor Assessment Scale (MAS) and surface electromyography (sEMG).</p><p><strong>Results: </strong>Before treatment, there were no significant differences in baseline characteristics among the three groups (P > 0.05). After treatment, significant improvements were observed in FMA-UE score, FTHUE-HK grading, MBI score, Brunnstrom hand staging, MAS score and sEMG compared with pre-treatment values (P < 0.05). Specifically, EG1 showed greater improvements than the control group (P < 0.05), whereas EG2 demonstrated better outcomes than both EG1 and the control group (P < 0.05).</p><p><strong>Conclusions: </strong>Contralateral controlled functional electrical stimulation combined with tDCS substantially improves hand function in patients with early stage stroke, with better outcomes than tDCS therapy alone.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"24 1","pages":"81"},"PeriodicalIF":2.9,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12228401/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144564329","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting the perioperative transfusion risk of proximal femoral antirotation nailing (PFNA) for elderly patients with intertrochanteric fractures: a new predictive nomogram.","authors":"Donglei Wei, Yage Jiang, Xingcan Long, Nanchang Huang, Jianhui Xiang, Jianwen Cheng, Jinmin Zhao","doi":"10.1186/s12938-025-01419-z","DOIUrl":"10.1186/s12938-025-01419-z","url":null,"abstract":"<p><strong>Background: </strong>Proximal femoral antirotation nailing (PFNA) for treating elderly patients with intertrochanteric fractures (EIFs) is often associated with substantial hidden blood loss. Perioperative blood transfusion to restore the lost blood has no effect on postoperative mortality and it increases the risk of postoperative infection. The goal of this study was to develop and validate a nomogram for predicting the risk of perioperative transfusion and intervening ahead of time to reduce the risk in EIF patients receiving PFNA.</p><p><strong>Methods: </strong>This study retrospectively examined and collected risk factors associated with transfusion in EIF patients treated with PFNA. Random forest with least absolute shrinkage and selection operator (LASSO) regression analysis was used to select characteristic variables and construct nomograms with the screening variables. The predictive model's discriminatory efficacy and calibration efficacy were assessed by receiver operating characteristic (ROC) curves, C-index, and calibration curves, respectively. Clinical usefulness was assessed by decision curve analysis (DCA).</p><p><strong>Results: </strong>The final nomogram consisted of five predictor variables: lower preoperative haemoglobin (HGB), age, preoperative urea, preoperative albumin, and surgical position. The nomogram showed good discriminatory and calibration efficacy with an area under the curve (AUC) value of 0.865 and a calibration curve highly approximating the ideal curve. In internal validation, the C-index of the model was calculated to be 0.823, indicating that the model exhibited superior predictive power.</p><p><strong>Conclusions: </strong>The nomogram constructed from preoperative HGB, age, urea, albumin, and surgical position can be used to predict more accurately the risk of perioperative transfusion in EIF patients treated with PFNA. Validation of the accuracy of this predictive model requires multicenter, prospective, and larger populations.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"24 1","pages":"80"},"PeriodicalIF":2.9,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12220181/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144538032","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A hybrid multi-instance learning-based identification of gastric adenocarcinoma differentiation on whole-slide images.","authors":"Mudan Zhang, Xinhuan Sun, Wuchao Li, Yin Cao, Chen Liu, Guilan Tu, Jian Wang, Rongpin Wang","doi":"10.1186/s12938-025-01407-3","DOIUrl":"10.1186/s12938-025-01407-3","url":null,"abstract":"<p><strong>Objective: </strong>To investigate the potential of a hybrid multi-instance learning model (TGMIL) combining Transformer and graph attention networks for classifying gastric adenocarcinoma differentiation on whole-slide images (WSIs) without manual annotation.</p><p><strong>Methods and materials: </strong>A hybrid multi-instance learning model is proposed based on the Transformer and the graph attention network, called TGMIL, to classify the differentiation of gastric adenocarcinoma. A total of 613 WSIs from patients with gastric adenocarcinoma were retrospectively collected from two different hospitals. According to the differentiation of gastric adenocarcinoma, the data were divided into four groups: normal group (n = 254), well differentiation group (n = 166), moderately differentiation group (n = 75), and poorly differentiation group (n = 118). The gold standard of differentiation classification was blindly established by two gastrointestinal pathologists. The WSIs were randomly split into a training dataset consisting of 494 images and a testing dataset consisting of 119 images. Within the training set, the WSI count of the normal, well, moderately, and poorly differential groups was 203, 131, 62, and 98 individuals, respectively. Within the test set, the corresponding WSI count was 51, 35, 13, and 20 individuals.</p><p><strong>Results: </strong>The TGMIL model developed for the differential prediction task exhibited remarkable efficiency when considering sensitivity, specificity, and the area under the curve (AUC) values. We also conducted a comparative analysis to assess the efficiency of five other models, namely MIL, CLAM_SB, CLAM_MB, DSMIL, and TransMIL, in classifying the differentiation of gastric cancer. The TGMIL model achieved a sensitivity of 73.33% and a specificity of 91.11%, with an AUC value of 0.86.</p><p><strong>Conclusions: </strong>The hybrid multi-instance learning model TGMIL could accurately classify the differentiation of gastric adenocarcinoma using WSI without the need for labor-intensive and time-consuming manual annotations, which will improve the efficiency and objectivity of diagnosis.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"24 1","pages":"79"},"PeriodicalIF":2.9,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12199488/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144494093","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Performance of deep-learning models incorporating knee alignment information for predicting ground reaction force during walking.","authors":"Tommy Sugiarto, Yi-Jia Lin, Hsiao-Liang Tsai, Chi-Tien Sun, Wei-Chun Hsu","doi":"10.1186/s12938-025-01409-1","DOIUrl":"10.1186/s12938-025-01409-1","url":null,"abstract":"<p><strong>Background: </strong>Wearable sensors combined with deep-learning models are increasingly being used to predict biomechanical variables. Researchers have focused on either simple neural networks or complex pretrained models with multiple layers. In addition, studies have rarely integrated knee alignment information or the side affected by injury as features to improve model predictions. In this study, we compared the performance of selected model architectures, including complex pretrained models, in predicting three-dimensional (3D) ground reaction force (GRF) data during level walking by using data obtained from motion capture systems and wearable accelerometers.</p><p><strong>Results: </strong>Ten deep-learning models for predicting the 3D GRF were developed using motion capture and accelerometer data with or without subject-specific features. Incorporating subject-specific features improved prediction accuracy for all models except the long short-term memory (LSTM) model. A two-dimensional (2D)-CNN-LSTM hybrid model achieved the best results. Established models, such as ResNet50 and Inception, performed better when trained with pretrained ImageNet weights and subject-specific features, underscoring the value of pretrained knowledge and subject-specific information for improving accuracy. However, these models did not outperform the custom hybrid models in predicting time-series 3D GRF data, indicating that larger models do not necessarily perform better for time-series applications but do always have greater computational demands.</p><p><strong>Conclusion: </strong>Incorporating subject-specific features, such as alignment information, enhanced the accuracy of GRF predictions during walking. Complex pretrained models were outperformed by custom hybrid models for time-series 3D GRF prediction during walking. Custom models with lower computational demands and using alignment features are a more efficient and effective choice for applications requiring accurate and resource-efficient predictions.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"24 1","pages":"78"},"PeriodicalIF":2.9,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12186330/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144473978","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Emerging technologies in airway management: a narrative review of intubation robotics and anatomical structure recognition algorithms.","authors":"Weixiong Chen, Yu Tian, Yingjie Wang, Lili Feng, Mannan Abdul, Shuangshuang Li, Wenxian Li, Yuan Han","doi":"10.1186/s12938-025-01408-2","DOIUrl":"10.1186/s12938-025-01408-2","url":null,"abstract":"<p><p>In recent years, the medical field has seen significant advancements in the field of robotics and artificial intelligence (AI). However, many healthcare professionals still find these technologies unfamiliar and complex, especially regarding their use during airway management. This review covers the current capabilities of robots and AI in tracheal intubation (TI), providing new insights that advocate for the broader adoption of these technologies to improve airway management. A literature review on robotics and AI in TI was conducted through searches in the PubMed, Web of Science, and IEEE Xplore databases. Drawing on a classification framework derived from expert opinions and existing literature, these studies are categorized into six key stages. Most of these technologies remain in the testing and validation phases, with only a few having reached commercialization. The primary goal of these robotic and AI systems is to enhance the success rate and operational efficiency of intubation while mitigating the persistent shortage of medical resources and supporting telemedicine. However, ongoing attention is required to address challenges such as high costs, a shortage of interdisciplinary talent, and ethical concerns related to medical bias and data security. Robots and AI are beginning to play a significant role in TI. Although many of these technologies remain in the theoretical stage of clinical application, their potential to enhance clinical practice is substantial, provided they are implemented as complementary tools that support rather than substitute the expertise of healthcare professionals. AI-powered robots show great potential as assistive tools for optimizing intubation maneuvers, whereas clinical decision-making (e.g., determining the necessity of intubation) remains under the supervision of physicians.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"24 1","pages":"77"},"PeriodicalIF":2.9,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12186397/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144473924","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Research on automatic assessment of the severity of unilateral vocal cord paralysis based on Mel-spectrogram and convolutional neural networks.","authors":"Shuaichi Ma, Wenwen Liao, Yi Zhang, Fan Zhang, Yimiao Wang, Zhiyan Lu, Chen Zhao, Jianbo Yu, Peijie He","doi":"10.1186/s12938-025-01401-9","DOIUrl":"10.1186/s12938-025-01401-9","url":null,"abstract":"<p><strong>Background: </strong>This study aims to develop an AI-powered platform using Mel-spectrogram analysis and convolutional neural networks (CNN) to automate the severity assessment of unilateral vocal fold paralysis (UVCP) through voice analysis, providing an objective basis for individualized clinical treatment plans.</p><p><strong>Methods: </strong>To accurately identify the severity of UVCP, this study developed the CNN model TripleConvNet. Voice samples were collected from 131 healthy individuals and 292 confirmed UVCP patients from the Eye and ENT Hospital of Fudan University. Based on vocal fold compensation function, the patients were divided into three groups: decompensated (84 cases), partially compensated (98 cases), and fully compensated (110 cases). Using Mel-spectrograms and their first- and second-order differential features as inputs, the TripleConvNet model classified patients by severity and was systematically evaluated for its performance in UVCP severity grading tasks.</p><p><strong>Results: </strong>TripleConvNet achieved a classification accuracy of 74.3% in distinguishing between healthy voices and the UVCP decompensated, partially compensated, and fully compensated groups.</p><p><strong>Conclusion: </strong>This study demonstrates the potential of deep learning-based non-invasive voice analysis for precise grading of UVCP severity. The proposed method offers a promising clinical tool to assist physicians in disease assessment and personalized treatment planning.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"24 1","pages":"76"},"PeriodicalIF":2.9,"publicationDate":"2025-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12181906/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144339809","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prediction of risk factors of plastic bronchitis in children with severe Mycoplasma pneumoniae pneumonia.","authors":"Shiyin Mu, Jia Zhai, Yongsheng Guo, Bing Huang, Yingxue Zou","doi":"10.1186/s12938-025-01410-8","DOIUrl":"10.1186/s12938-025-01410-8","url":null,"abstract":"<p><strong>Background: </strong>Plastic bronchitis (PB) is a rare but potentially life-threatening condition that requires particular attention in pediatric patients, specifically those presenting with severe Mycoplasma pneumoniae pneumonia (SMPP). This study aimed to identify risk factors associated with PB in children with SMPP and develop a comprehensive risk factor scoring system.</p><p><strong>Methods: </strong>A retrospective analysis was conducted on SMPP patients who underwent bronchoscopy between January 2018 and October 2023. Based on bronchoscopic and pathological examination results, patients were categorized into PB (n = 142) and non-PB (n = 274) groups. Clinical manifestations, laboratory data, and imaging findings were analyzed. Risk factors for PB in SMPP children were identified through univariate and multivariate logistic regression analyses. A nomogram model incorporating independent risk factors was developed, and a PB risk factor scoring system was established. Model validation was performed through a prospective validation study.</p><p><strong>Results: </strong>Among 416 SMPP children (197 males, 219 females), mean age at disease onset was 6.9 ± 2.9 years and 6.6 ± 2.8 years in the PB and Non-PB groups, respectively. Multivariate logistic regression analysis identified eight independent predictors of PB in SMPP children: dyspnea, decreased breath sounds, neutrophil-to-lymphocyte ratio (NLR), lactate dehydrogenase (LDH), mean platelet volume to platelet ratio (MPV/PLT), pleural effusion, ≥ 2/3 lobe consolidation, and atelectasis. The nomogram prediction model demonstrated excellent discriminative ability (AUC = 0.92, 95% CI 0.892-0.948, P < 0.005) and strong calibration between predicted and observed outcomes. In the prospective validation cohort (n = 565), the scoring system effectively stratified patients into risk categories: high-risk (71.62% PB incidence), intermediate-risk (59.79%), and low-risk (5.33%), with statistically significant inter-group differences (P < 0.001). The PB group exhibited significantly longer hospitalization durations, extended glucocorticoid treatment periods, higher proportions of glucocorticoid therapy utilization, and a greater frequency of bronchoscopy interventions (≥ 2 sessions) compared to the Non-PB group (all P < 0.05).</p><p><strong>Conclusions: </strong>In this study, we developed and validated a nomogram to PB in children with SMPP. This model serves as a clinically practical tool for early PB identification, enabling physicians to initiate timely interventions and optimize disease management strategies.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"24 1","pages":"75"},"PeriodicalIF":2.9,"publicationDate":"2025-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12181858/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144339808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A comprehensive review of heart rate measurement using remote photoplethysmography and deep learning.","authors":"Uday Debnath, Sungho Kim","doi":"10.1186/s12938-025-01405-5","DOIUrl":"10.1186/s12938-025-01405-5","url":null,"abstract":"<p><p>With the widespread availability of consumer-grade cameras, interest in heart rate (HR) measurement using remote photoplethysmography (rPPG) has grown significantly. rPPG is a noninvasive optical technique that uses camera to measure heart rate by analyzing light reflectance due to blood flow changes beneath the skin from any parts of the body, mostly facial regions. However, it faces challenges such as motion artifacts and sensitivity to varying lighting conditions. The rapid advancement of deep learning techniques in recent years has driven numerous studies to integrate these models with rPPG for HR detection in remote health monitoring systems. This study provides a comprehensive review of both conventional approaches and recent developments in rPPG and deep learning algorithms. A comparative analysis highlighted the superior accuracy of deep learning methods over conventional techniques in non-contact HR estimation. Based on a review of 145 articles encompassing different methodologies, signal processing strategies, and deep learning algorithms, our study identifies existing research gaps and explores future research opportunities for real-world applications.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"24 1","pages":"73"},"PeriodicalIF":2.9,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12181896/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144336302","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}