Medical & Biological Engineering & Computing最新文献_第4页

Pan-Ret: a semi-supervised framework for scalable detection of pan-retinal diseases. Pan-Ret：可扩展的泛视网膜疾病检测半监督框架。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2024-12-14 DOI: 10.1007/s11517-024-03250-5

Rohan Banerjee, Rakhshanda Mujib, Prayas Sanyal, Tapabrata Chakraborti, Sanjoy Kumar Saha

{"title":"Pan-Ret: a semi-supervised framework for scalable detection of pan-retinal diseases.","authors":"Rohan Banerjee, Rakhshanda Mujib, Prayas Sanyal, Tapabrata Chakraborti, Sanjoy Kumar Saha","doi":"10.1007/s11517-024-03250-5","DOIUrl":"https://doi.org/10.1007/s11517-024-03250-5","url":null,"abstract":"It has been shown in recent years that a range of optical diseases have early manifestation in retinal fundus images. It is becoming increasingly important to separate the regions of interest (RoI) upfront in the automated classification pipeline in order to ensure the alignment of the disease diagnosis with clinically relevant visual features. In this work, we introduce Pan-Ret, a semi-supervised framework which starts with locating the abnormalities in the biomedically relevant RoIs of a retinal image in an \"annotation-agnostic\" fashion. It does so by leveraging an anomaly detection setup using parallel autoencoders that are trained only on healthy population initially. Then, the anomalous images are separated based on the RoIs using a fully interpretable classifier like support vector machine (SVM). Experimental results show that the proposed approach yields an overall F1-score of 0.95 and 0.96 in detecting abnormalities on two different public datasets covering a diverse range of retinal diseases including diabetic retinopathy, hypertensive retinopathy, glaucoma, age-related macular degeneration, and several more in a staged manner. Thus, the work presents a milestone towards a pan-retinal disease diagnostic pipeline that can not only cater to the current set of disease classes, but has the capacity of adding further classes down the line. This is due to an anomaly detection style one-class learning setup of the deep autoencoder piece of the proposed pipeline, thus improving the generalizability of this approach compared to usual fully supervised competitors. This is also expected to increase the practical translational potential of Pan-Ret in a real-life scalable clinical setting.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142822598","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

EchoSegDiff: a diffusion-based model for left ventricular segmentation in echocardiography. EchoSegDiff：基于扩散的超声心动图左心室分割模型。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2024-12-14 DOI: 10.1007/s11517-024-03255-0

Huijuan Tian, Lei Zhang, Xuetong Fu, Hongyang Zhang, Yuanquan Wang, Shoujun Zhou, Jin Wei

{"title":"EchoSegDiff: a diffusion-based model for left ventricular segmentation in echocardiography.","authors":"Huijuan Tian, Lei Zhang, Xuetong Fu, Hongyang Zhang, Yuanquan Wang, Shoujun Zhou, Jin Wei","doi":"10.1007/s11517-024-03255-0","DOIUrl":"https://doi.org/10.1007/s11517-024-03255-0","url":null,"abstract":"Echocardiography is a primary tool for cardiac diagnosis. Accurate delineation of the left ventricle is a prerequisite for echocardiography-based clinical decision-making. In this work, we propose an echocardiographic left ventricular segmentation method based on the diffusion probability model, which is named EchoSegDiff. The EchoSegDiff takes an encoder-decoder structure in the reverse diffusion process. A diffusion encoder residual block (DEResblock) based on the atrous pyramid squeeze attention (APSA) block is coined as the main module of the encoder, so that the EchoSegDiff can catch multiscale features effectively. A novel feature fusion module (FFM) is further proposed, which can adaptively fuse the features from encoder and decoder to reduce semantic gap between encoder and decoder. The proposed EchoSegDiff is validated on two publicly available echocardiography datasets. In terms of left ventricular segmentation performance, it outperforms other state-of-the-art networks. The segmentation accuracy on the two datasets reached 93.69% and 89.95%, respectively. This demonstrates the excellent potential of EchoSegDiff in the task of left ventricular segmentation in echocardiography.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142822570","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CMFNet: a cross-dimensional modal fusion network for accurate vessel segmentation based on OCTA data. CMFNet：基于 OCTA 数据进行精确血管分割的跨维模态融合网络。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2024-12-13 DOI: 10.1007/s11517-024-03256-z

Siqi Wang, Xiaosheng Yu, Hao Wu, Ying Wang, Chengdong Wu

{"title":"CMFNet: a cross-dimensional modal fusion network for accurate vessel segmentation based on OCTA data.","authors":"Siqi Wang, Xiaosheng Yu, Hao Wu, Ying Wang, Chengdong Wu","doi":"10.1007/s11517-024-03256-z","DOIUrl":"https://doi.org/10.1007/s11517-024-03256-z","url":null,"abstract":"Optical coherence tomography angiography (OCTA) is a novel non-invasive retinal vessel imaging technique that can display high-resolution 3D vessel structures. The quantitative analysis of retinal vessel morphology plays an important role in the automatic screening and diagnosis of fundus diseases. The existing segmentation methods struggle to effectively use the 3D volume data and 2D projection maps of OCTA images simultaneously, which leads to problems such as discontinuous microvessel segmentation results and deviation of morphological estimation. To enhance diagnostic support for fundus diseases, we propose a cross-dimensional modal fusion network (CMFNet) using both 3D volume data and 2D projection maps for accurate OCTA vessel segmentation. Firstly, we use different encoders to generate 2D projection features and 3D volume data features from projection maps and volume data, respectively. Secondly, we design an attentional cross-feature projection learning module to purify 3D volume data features and learn its projection features along the depth direction. Then, we develop a cross-dimensional hierarchical fusion module to effectively fuse coded features learned from the volume data and projection maps. In addition, we extract high-level semantic weight information and map it to the cross-dimensional hierarchical fusion process to enhance fusion performance. To validate the efficacy of our proposed method, we conducted experimental evaluations using the publicly available dataset: OCTA-500. The experimental results show that our method achieves state-of-the-art performance.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142819936","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An arbitrary waveform neurostimulator for preclinical studies: design and verification.

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2024-12-12 DOI: 10.1007/s11517-024-03241-6

Hipolito Guzman-Miranda, Alejandro Barriga-Rivera

{"title":"An arbitrary waveform neurostimulator for preclinical studies: design and verification.","authors":"Hipolito Guzman-Miranda, Alejandro Barriga-Rivera","doi":"10.1007/s11517-024-03241-6","DOIUrl":"https://doi.org/10.1007/s11517-024-03241-6","url":null,"abstract":"Neural electrostimulation has enabled different therapies to treat a number of health problems. For example, the cochlear implant allows for recovering the hearing function and deep brain electrostimulation has been proved to reduce tremor in Parkinson's disease. Other approaches such as retinal prostheses are progressing rapidly, as researchers continue to investigate new strategies to activate targeted neurons more precisely. The use of arbitrary current waveform electrosimulation is a promising technique that allows exploiting the differences that exist among different neural types to enable preferential activation. This work presents a two-channel arbitrary waveform neurostimulator designed for visual prosthetics research. A field programmable gate array (FPGA) was employed to control and generate voltage waveforms via digital-to-analog converters. Voltage waveforms were then electrically isolated and converted to current waveforms using a modified Howland amplifier. Shorting of the electrodes was provided using multiplexers. The FPGA gateware was verified to a high level of confidence using a transaction-level modeled testbench, achieving a line coverage of 91.4%. The complete system was tested in saline using silver electrodes with diameters from 200 to 1000 µm. The bandwidth obtained was 30 kHz with voltage compliance ± 15 V. The neurostimulator can be easily scaled up using the provided in/out trigger ports and adapted to other applications with minor modifications.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142814710","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Predicting 30-day reoperation following primary total knee arthroplasty: machine learning model outperforms the ACS risk calculator.

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2024-12-09 DOI: 10.1007/s11517-024-03258-x

Tony Lin-Wei Chen, Anirudh Buddhiraju, Blake M Bacevich, Henry Hojoon Seo, Michelle Riyo Shimizu, Young-Min Kwon

{"title":"Predicting 30-day reoperation following primary total knee arthroplasty: machine learning model outperforms the ACS risk calculator.","authors":"Tony Lin-Wei Chen, Anirudh Buddhiraju, Blake M Bacevich, Henry Hojoon Seo, Michelle Riyo Shimizu, Young-Min Kwon","doi":"10.1007/s11517-024-03258-x","DOIUrl":"https://doi.org/10.1007/s11517-024-03258-x","url":null,"abstract":"The ACS risk calculator (ARC) has proven less effective in predicting patient-specific risk of early reoperation after primary total knee arthroplasty (TKA), compromising care quality and cost efficiency. This study compared the performance of a machine learning (ML) model and ARC in predicting 30-day reoperation after primary TKA using a national-scale dataset. Data of 366,151 TKAs were acquired from the ACS-NSQIP database. A random forest model was derived using ARC build-in parameters from the training dataset via techniques of hyperparameter optimization and cross-validation. The predictive performance of random forest and ARC was evaluated by metrics of discrimination, calibration, and clinical utility using the testing dataset. The ML model demonstrated good discrimination and calibration (AUC: 0.72, slope: 1.18, intercept: - 0.14, Brier score: 0.012), outperforming ARC in all metrics (AUC: 0.51, slope: - 0.01, intercept: 0.01, Brier score: 0.135) including clinical utility measured by decision curve analyses. Age (> 67 years) and BMI (> 34 kg/m2) were the important predictors of reoperation. This study suggests the superiority of ML models in identifying individualized 30-day reoperation risk following TKA. ML models may be an adjunct prediction tool in enhancing patient-specific risk stratification and postoperative care management.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142802873","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Mesothelin expression prediction in pancreatic cancer based on multimodal stochastic configuration networks.

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2024-12-06 DOI: 10.1007/s11517-024-03253-2

Junjie Li, Xuanle Li, Yingge Chen, Yunling Wang, Binjie Wang, Xuefeng Zhang, Na Zhang

{"title":"Mesothelin expression prediction in pancreatic cancer based on multimodal stochastic configuration networks.","authors":"Junjie Li, Xuanle Li, Yingge Chen, Yunling Wang, Binjie Wang, Xuefeng Zhang, Na Zhang","doi":"10.1007/s11517-024-03253-2","DOIUrl":"https://doi.org/10.1007/s11517-024-03253-2","url":null,"abstract":"Predicting tumor biomarkers with high precision is essential for improving the diagnostic accuracy and developing more effective treatment strategies. This paper proposes a machine learning model that utilizes CT images and biopsy whole slide images (WSI) to classify mesothelin expression levels in pancreatic cancer. By combining multimodal learning and stochastic configuration networks, a radiopathomics mesothelin-prediction system named RPMSNet is developed. The system extracts radiomic and pathomic features from CT images and WSI, respectively, and sends them into stochastic configuration networks for the final prediction. Compared to traditional radiomics or pathomics, this system has the capability to capture more comprehensive image features, providing a multidimensional insight into tissue characteristics. The experiments and analyses demonstrate the accuracy and effectiveness of the system, with an area under the curve of 81.03%, an accuracy of 73.67%, a sensitivity of 71.54%, a precision of 76.78%, and a F1-score of 72.61%, surpassing both single-modality and dual-modality models. RPMSNet highlights its potential for early diagnosis and personalized treatment in precision medicine.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142787489","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Development and feasibility study of a piezoresistive pressure sensor-based automated system for monitoring and controlling gastric pressure in endoscopy.

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2024-12-03 DOI: 10.1007/s11517-024-03254-1

Sukgyu Koh, Sungwan Kim

{"title":"Development and feasibility study of a piezoresistive pressure sensor-based automated system for monitoring and controlling gastric pressure in endoscopy.","authors":"Sukgyu Koh, Sungwan Kim","doi":"10.1007/s11517-024-03254-1","DOIUrl":"https://doi.org/10.1007/s11517-024-03254-1","url":null,"abstract":"Maintaining precise intragastric pressure during gastrointestinal endoscopy is critical for patient safety and diagnostic accuracy, yet current methods relying on manual adjustments pose risks of improper insufflation. This study aimed to develop an automated gastric pressure control system for flexible endoscopy, addressing these challenges with a piezoresistive pressure sensor that can be integrated into a 7.3 mm diameter flexible endoscope. The system, incorporating air and suction pumps controlled by a microcontroller, was calibrated in an acrylic chamber and validated through comprehensive testing in both an endoscopy simulator and a porcine specimen. Testing scenarios included normal breathing, coughing, belching, and combined events, assessing accuracy, stability, and real-time pressure regulation under conditions mimicking physiological responses. Results demonstrated high accuracy (R2 = 0.9999), minimal bias (0.23 mmHg), and strong agreement with reference standards, confirming effective pressure management. Simulated clinical scenarios in simulator and porcine specimen further showed the system's ability to maintain target pressure with minimal errors, indicating robustness under dynamic conditions. These findings suggest that the automated pressure control system significantly improves safety and procedural efficiency in endoscopy, with potential applicability to other minimally invasive procedures. Further animal model testing is recommended to validate the clinical performance under realistic physiological conditions.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142774125","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deployment and validation of a smart bed architecture for untethered patients with wireless biomonitoring stickers. 利用无线生物监测贴纸，部署和验证用于无牵挂病人的智能床结构。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2024-12-01 Epub Date: 2024-07-22 DOI: 10.1007/s11517-024-03155-3

Tânia Nunes, Luís Gaspar, José N Faria, David Portugal, Telmo Lopes, Pedro Fernandes, Mahmoud Tavakoli

{"title":"Deployment and validation of a smart bed architecture for untethered patients with wireless biomonitoring stickers.","authors":"Tânia Nunes, Luís Gaspar, José N Faria, David Portugal, Telmo Lopes, Pedro Fernandes, Mahmoud Tavakoli","doi":"10.1007/s11517-024-03155-3","DOIUrl":"10.1007/s11517-024-03155-3","url":null,"abstract":"Conventional patient monitoring in healthcare has limitations such as delayed identification of deteriorating conditions, disruptions to patient routines, and discomfort due to extensive wiring for bed-bound patients. To address these, we have recently developed an innovative IoT-based healthcare system for real-time wireless patient monitoring. This system includes a flexible epidermal patch that collects vital signs using low power electronics and transmits the data to IoT nodes in hospital beds. The nodes connect to a smart gateway that aggregates the information and interfaces with the hospital information system (HIS), facilitating the exchange of electronic health records (EHR) and enhancing access to patient vital signs for healthcare professionals. Our study validates the proposed smart bed architecture in a clinical setting, assessing its ability to meet healthcare personnel needs, patient comfort, and data transmission reliability. Technical performance assessment involves analyzing key performance indicators for communication across various interfaces, including the wearable device and the smart box, and the link between the gateway and the HIS. Also, a comparative analysis is conducted on data from our architecture and traditional hospital equipment. Usability evaluation involves questionnaires completed by patients and healthcare professionals. Results demonstrate the robustness of the architecture proposed, exhibiting reliable and efficient information flow, while offering significant improvements in patient monitoring over conventional wired methods, including unrestricted mobility and improved comfort to enhance healthcare delivery.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"3815-3840"},"PeriodicalIF":2.6,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11639216/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141735514","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}

引用次数: 0

Motion artifacts in capacitive ECG monitoring systems: a review of existing models and reduction techniques. 电容式心电图监测系统中的运动伪影：现有模型和减少技术综述。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2024-12-01 Epub Date: 2024-07-20 DOI: 10.1007/s11517-024-03165-1

Matin Khalili, Hamid GholamHosseini, Andrew Lowe, Matthew M Y Kuo

{"title":"Motion artifacts in capacitive ECG monitoring systems: a review of existing models and reduction techniques.","authors":"Matin Khalili, Hamid GholamHosseini, Andrew Lowe, Matthew M Y Kuo","doi":"10.1007/s11517-024-03165-1","DOIUrl":"10.1007/s11517-024-03165-1","url":null,"abstract":"Current research focuses on improving electrocardiogram (ECG) monitoring systems to enable real-time and long-term usage, with a specific focus on facilitating remote monitoring of ECG data. This advancement is crucial for improving cardiovascular health by facilitating early detection and management of cardiovascular disease (CVD). To efficiently meet these demands, user-friendly and comfortable ECG sensors that surpass wet electrodes are essential. This has led to increased interest in ECG capacitive electrodes, which facilitate signal detection without requiring gel preparation or direct conductive contact with the body. This feature makes them suitable for wearables or integrated measurement devices. However, ongoing research is essential as the signals they measure often lack sufficient clinical accuracy due to susceptibility to interferences, particularly Motion Artifacts (MAs). While our primary focus is on studying MAs, we also address other limitations crucial for designing a high Signal-to-Noise Ratio (SNR) circuit and effectively mitigating MAs. The literature on the origins and models of MAs in capacitive electrodes is insufficient, which we aim to address alongside discussing mitigation methods. We bring attention to digital signal processing approaches, especially those using reference signals like Electrode-Tissue Impedance (ETI), as highly promising. Finally, we discuss its challenges, proposed solutions, and offer insights into future research directions.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"3599-3622"},"PeriodicalIF":2.6,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11568998/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141731576","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}

引用次数: 0

Smartphone videos-driven musculoskeletal multibody dynamics modelling workflow to estimate the lower limb joint contact forces and ground reaction forces. 智能手机视频驱动的肌肉骨骼多体动力学建模工作流程，用于估算下肢关节接触力和地面反作用力。

IF 4.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2024-12-01 Epub Date: 2024-07-24 DOI: 10.1007/s11517-024-03171-3

Yinghu Peng, Wei Wang, Lin Wang, Hao Zhou, Zhenxian Chen, Qida Zhang, Guanglin Li

{"title":"Smartphone videos-driven musculoskeletal multibody dynamics modelling workflow to estimate the lower limb joint contact forces and ground reaction forces.","authors":"Yinghu Peng, Wei Wang, Lin Wang, Hao Zhou, Zhenxian Chen, Qida Zhang, Guanglin Li","doi":"10.1007/s11517-024-03171-3","DOIUrl":"10.1007/s11517-024-03171-3","url":null,"abstract":"The estimation of joint contact forces in musculoskeletal multibody dynamics models typically requires the use of expensive and time-consuming technologies, such as reflective marker-based motion capture (Mocap) system. In this study, we aim to propose a more accessible and cost-effective solution that utilizes the dual smartphone videos (SPV)-driven musculoskeletal multibody dynamics modeling workflow to estimate the lower limb mechanics. Twelve participants were recruited to collect marker trajectory data, force plate data, and motion videos during walking and running. The smartphone videos were initially analyzed using the OpenCap platform to identify key joint points and anatomical markers. The markers were used as inputs for the musculoskeletal multibody dynamics model to calculate the lower limb joint kinematics, joint contact forces, and ground reaction forces, which were then evaluated by the Mocap-based workflow. The root mean square error (RMSE), mean absolute deviation (MAD), and Pearson correlation coefficient (ρ) were adopted to evaluate the results. Excellent or strong Pearson correlations were observed in most lower limb joint angles (ρ = 0.74 ~ 0.94). The averaged MADs and RMSEs for the joint angles were 1.93 ~ 6.56° and 2.14 ~ 7.08°, respectively. Excellent or strong Pearson correlations were observed in most lower limb joint contact forces and ground reaction forces (ρ = 0.78 ~ 0.92). The averaged MADs and RMSEs for the joint lower limb joint contact forces were 0.18 ~ 1.07 bodyweight (BW) and 0.28 ~ 1.32 BW, respectively. Overall, the proposed smartphone video-driven musculoskeletal multibody dynamics simulation workflow demonstrated reliable accuracy in predicting lower limb mechanics and ground reaction forces, which has the potential to expedite gait dynamics analysis in a clinical setting.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"3841-3853"},"PeriodicalIF":4.6,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141753224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0