BioMedical Engineering OnLine最新文献

{"title":"Modeling of the native knee with kinematic data derived from experiments using the VIVO™ joint simulator: a feasibility study.","authors":"Paul Henke, Johanna Meier, Leo Ruehrmund, Saskia A Brendle, Sven Krueger, Thomas M Grupp, Christoph Lutter, Christoph Woernle, Rainer Bader, Maeruan Kebbach","doi":"10.1186/s12938-024-01279-z","DOIUrl":"10.1186/s12938-024-01279-z","url":null,"abstract":"<p><strong>Background: </strong>Despite advances in total knee arthroplasty, many patients are still unsatisfied with the functional outcome. Multibody simulations enable a more efficient exploration of independent variables compared to experimental studies. However, to what extent numerical models can fully reproduce knee joint kinematics is still unclear. Hence, models must be validated with different test scenarios before being applied to biomechanical questions.</p><p><strong>Methods: </strong>In our feasibility study, we analyzed a human knee specimen on a six degree of freedom joint simulator, applying a passive flexion and different laxity tests with sequential states of ligament resection while recording the joint kinematics. Simultaneously, we generated a subject-specific multibody model of the native tibiofemoral joint considering ligaments and contact between articulating cartilage surfaces.</p><p><strong>Results: </strong>Our experimental data on the sequential states of ligament resection aligned well with the literature. The model-based knee joint kinematics during passive flexion showed good agreement with the experiment, with root-mean-square errors of less than 1.61 mm for translations and 2.1° for knee joint rotations. During laxity tests, the experiment measured up to 8 mm of anteroposterior laxity, while the numerical model allowed less than 3 mm.</p><p><strong>Conclusion: </strong>Although the multibody model showed good agreement to the experimental kinematics during passive flexion, the validation showed that ligament parameters used in this feasibility study are too stiff to replicate experimental laxity tests correctly. Hence, more precise subject-specific ligament parameters have to be identified in the future through model optimization.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"23 1","pages":"85"},"PeriodicalIF":2.9,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11342559/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142046241","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":"Super-resolution reconstruction for early cervical cancer magnetic resonance imaging based on deep learning.","authors":"Chunxia Chen, Liu Xiong, Yongping Lin, Ming Li, Zhiyu Song, Jialin Su, Wenting Cao","doi":"10.1186/s12938-024-01281-5","DOIUrl":"10.1186/s12938-024-01281-5","url":null,"abstract":"<p><p>This study aims to develop a super-resolution (SR) algorithm tailored specifically for enhancing the image quality and resolution of early cervical cancer (CC) magnetic resonance imaging (MRI) images. The proposed method is subjected to both qualitative and quantitative analyses, thoroughly investigating its performance across various upscaling factors and assessing its impact on medical image segmentation tasks. The innovative SR algorithm employed for reconstructing early CC MRI images integrates complex architectures and deep convolutional kernels. Training is conducted on matched pairs of input images through a multi-input model. The research findings highlight the significant advantages of the proposed SR method on two distinct datasets at different upscaling factors. Specifically, at a 2× upscaling factor, the sagittal test set outperforms the state-of-the-art methods in the PSNR index evaluation, second only to the hybrid attention transformer, while the axial test set outperforms the state-of-the-art methods in both PSNR and SSIM index evaluation. At a 4× upscaling factor, both the sagittal test set and the axial test set achieve the best results in the evaluation of PNSR and SSIM indicators. This method not only effectively enhances image quality, but also exhibits superior performance in medical segmentation tasks, thereby providing a more reliable foundation for clinical diagnosis and image analysis.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"23 1","pages":"84"},"PeriodicalIF":2.9,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11342621/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142035147","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":"Predictors of flatfoot in 11-12-year olds: a longitudinal cohort study.","authors":"Tomoko Yamashita, Mitsuru Sato, Shingo Ata, Kazuhiko Yamashita","doi":"10.1186/s12938-024-01282-4","DOIUrl":"10.1186/s12938-024-01282-4","url":null,"abstract":"<p><strong>Background: </strong>The structures around the navicular bones, which constitute the medial longitudinal arch, develop by 10 years of age. While navicular bone height is often emphasized in the assessment of flatfoot, three-dimensional (3D) evaluations, including those of structural parameters during inversion, have rarely been investigated. If the development of flatfoot during the growth process could be predicted, appropriate interventions could be implemented. Therefore, in this longitudinal cohort study, we developed a system, utilizing smartphones, to measure the 3D structure of the foot, performed a longitudinal analysis of changes in midfoot structures in 124 children aged 9-12 years, and identified factors influencing the height of the navicular bone. The foot skeletal structure was measured using a 3D system.</p><p><strong>Results: </strong>Over 2 years, foot length and instep height increased during development, while navicular height decreased. The 25th percentile of the instep height ratio and navicular height ratio at ages 9-10 years did not exceed those at ages 11-12 years, with percentages of 17.9% and 71.6%, respectively, for boys, and 15.8% and 49.1%, respectively, for girls. As the quartiles of the second toe-heel-navicular angle (SHN angle) increased at ages 9-10 years, the axis of the bone distance (ABD) and SHN angles at ages 11-12 years also increased, resulting in a decrease in the navicular height ratio. A significant inverse correlation was found between changes in SHN angle and navicular height ratio. These findings indicate that the navicular bone rotation of the midfoot is a predictor of the descent of the navicular bone.</p><p><strong>Conclusions: </strong>This study revealed that some children exhibit decreases in navicular bone height with growth. As a distinct feature, the inversion of the navicular bone promotes flattening of the midfoot. Thus, this study provides insights into changes in midfoot development in children and provides an effective evaluation index.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"23 1","pages":"83"},"PeriodicalIF":2.9,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11337572/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142016281","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":"Evaluation of left ventricular systolic function in patients with iron deficiency anemia based on non-invasive left ventricular pressure-strain loops.","authors":"Xiuxiu Cui, Meng Jing, Liyuan Ren, Xuanning Hou, Qingfei Song, Kefeng Li, Xiaoyan Wang","doi":"10.1186/s12938-024-01276-2","DOIUrl":"10.1186/s12938-024-01276-2","url":null,"abstract":"<p><strong>Background: </strong>Iron deficiency anemia (IDA) is a common health problem worldwide. The objective of this study was to noninvasively and quantitatively evaluate early changes in left ventricular systolic function in patients with IDA using the left ventricular press-strain loop (LV-PSL).</p><p><strong>Methods: </strong>Sixty-two patients with IDA were selected and divided into two groups based on hemoglobin (Hb) concentration: Group B with Hb > 9 g/dL and group C with 6 g/dL < Hb < 9 g/dL. Thirty-three healthy individuals were used as the control (Group A). The global longitudinal strain (GLS), global work index (GWI), global constructive work (GCW), global waste work (GWW), global work efficiency (GWE) were derived using LV-PSL analysis. Receiver operating characteristic (ROC) curves were constructed for MW parameters to detect abnormal left ventricular systolic function in IDA patients.</p><p><strong>Results: </strong>Compared to group A, GWI and GCW were reduced in group B (both P < 0.01). Compared with groups B and A, GLS, GWI, GCW and GWE, and E/A were all diminished, and GWW, LVEDV, LVESV, and E/mean e' were all increased in group C (all P < 0.01). GLS was positively correlated with GWI, GCW, and GWE (r = 0.679, 0.681, and 0.447, all P < 0.01), and negatively associated with GWW (r = - 0.411, all P < 0.01). For GWI, area under the ROC curve (AUROC) was 0.783. The optimal GWI threshold for detecting abnormal LV systolic function in IDA was1763 mmHg%, with sensitivity of 0.71 and specificity of 0.78.</p><p><strong>Conclusions: </strong>LV-PSL allows noninvasive quantitative assessment of early impaired LV systolic function in IDA patients with preserved LV ejection fraction, and GWI has high sensitivity and specificity compared with other parameters.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"23 1","pages":"82"},"PeriodicalIF":2.9,"publicationDate":"2024-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11330064/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141995226","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 nomogram and risk stratification system for predicting survival in T1-2N0-1 breast cancer patients with liver metastasis in females: a population-based study","authors":"Kaiyue Wang, Lu Shen, Yiding Chen, Zhe Tang","doi":"10.1186/s12938-024-01274-4","DOIUrl":"https://doi.org/10.1186/s12938-024-01274-4","url":null,"abstract":"Liver was one of the most common distant metastatic sites in breast cancer. Patients with distant metastasis were identified as American Joint Committee on Cancer (AJCC) stage IV indicating poor prognosis. However, few studies have predicted the survival in females with T1-2N0-1 breast cancer who developed liver metastasis. This study aimed to explore the clinical features of these patients and establish a nomogram to predict their overall survival. 1923 patients were randomly divided into training (n = 1154) and validation (n = 769) cohorts. Univariate and multivariate analysis showed that age, marital status, race, estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor-2 (HER2), chemotherapy, surgery and bone metastasis, brain metastasis were considered the independent prognostic indicators. We developed a nomogram according to these ten parameters. The consistency index (c-index) was 0.72 (95% confidence interval CI 0.70–0.74) in the training cohort, 0.72 (95% CI 0.69–0.74) in the validation cohort. Calibration plots indicated that the nomogram-predicted survival was consistent with the recorded 1-, 3- and 5-year prognoses. Decision curve analysis curves in both the training and validation cohorts demonstrated that the nomogram showed better prediction than the AJCC TNM (8th) staging system. Kaplan Meier curve based on the risk stratification system showed that the low-risk group had a better prognosis than the high-risk group (P < 0.001). A predictive nomogram and risk stratification system were constructed to assess prognosis in T1-2N0-1 breast cancer patients with liver metastasis in females. The risk model established in this study had good predictive performance and could provide personalized clinical decision-making for future clinical work.","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"52 1","pages":""},"PeriodicalIF":3.9,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141939174","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}

{"title":"The application of machine learning methods for predicting the progression of adolescent idiopathic scoliosis: a systematic review.","authors":"Lening Li, Man-Sang Wong","doi":"10.1186/s12938-024-01272-6","DOIUrl":"10.1186/s12938-024-01272-6","url":null,"abstract":"<p><p>Predicting curve progression during the initial visit is pivotal in the disease management of patients with adolescent idiopathic scoliosis (AIS)-identifying patients at high risk of progression is essential for timely and proactive interventions. Both radiological and clinical factors have been investigated as predictors of curve progression. With the evolution of machine learning technologies, the integration of multidimensional information now enables precise predictions of curve progression. This review focuses on the application of machine learning methods to predict AIS curve progression, analyzing 15 selected studies that utilize various machine learning models and the risk factors employed for predictions. Key findings indicate that machine learning models can provide higher precision in predictions compared to traditional methods, and their implementation could lead to more personalized patient management. However, due to the model interpretability and data complexity, more comprehensive and multi-center studies are needed to transition from research to clinical practice.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"23 1","pages":"80"},"PeriodicalIF":2.9,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11308564/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141905819","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":"Analysis of angiographic findings and short-term recurrence factors in patients presenting with hemoptysis.","authors":"Wei Fan, Huling Su, Yaowen Chang, Wenhui Wang","doi":"10.1186/s12938-024-01270-8","DOIUrl":"10.1186/s12938-024-01270-8","url":null,"abstract":"<p><strong>Objectives: </strong>The abnormal anatomical alterations of blood vessels during DSA angiography in patients with hematological disorders were retrospectively examined, and the influencing factors of short-term (≤ 6 months) recurrent hemoptysis were statistically analyzed, and the consistency between admission diagnosis and intraoperative diagnosis was evaluated.</p><p><strong>Methods: </strong>The intraoperative angiography data of patients who underwent selective bronchial artery embolization for hemoptysis in our hospital from January 2022 to December 2022 were reviewed. They were divided into the observation group and the control group based on whether there was recurrent hemoptysis. The Logistic regression model and forest map were employed to analyze the factors influencing the recurrence rate.</p><p><strong>Results: </strong>A total of 104 patients were encompassed in this study (12 cases of tuberculosis, 35 cases of infection, 4 cases of lung cancer, 8 cases of bronchiectasis, 22 cases of arteriovenous fistula, 16 cases of aneurysm, and 7 cases of pulmonary hypertension). The coincidence rate of preoperative and intraoperative diagnoses was 73.1%. Pulmonary arteriovenous fistula and aneurysm were the predominant types of diseases that were misdiagnosed. The short-term recurrence rate was 16.3%, mainly attributed to the reopening of responsible vessels related to embolization, angiography leakage, and leaky embolization of specific types of vessels. The recurrence rate of only patients with arteriovenous fistula and aneurysm accounted for 47% of the total recurrence rate. The right bronchial artery, right internal thoracic artery, right thyroid neck trunk, and age were the independent factors influencing the recurrence of hemoptysis (p < 0.05).</p><p><strong>Conclusions: </strong>The main reason for angiographic leakage and embolization leakage in cases of hemoptysis is the lack of understanding of the anatomic variations of the vessels responsible. Careful examination of the specific types and locations of the vessels is the principal approach to reducing secondary operations.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"23 1","pages":"79"},"PeriodicalIF":2.9,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11304557/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141900879","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":"Long-term inhaling ultrafine zinc particles increases cardiac wall stresses elevated by myocardial infarction.","authors":"Songyu Wang, Haifang Wang, Li Li, Pei Niu, Zhongjie Yin, Yunlong Huo","doi":"10.1186/s12938-024-01275-3","DOIUrl":"10.1186/s12938-024-01275-3","url":null,"abstract":"<p><p>The analysis of cardiac wall mechanics is of importance for understanding coronary heart diseases (CHD). The inhalation of ultrafine particles could deteriorate CHD. The aim of the study is to investigate the effects of cardiac wall mechanics on rats of myocardial infarction (MI) after long-term inhalation of ultrafine Zn particles. Cardiac wall stresses and strains were computed, based on echocardiographic and hemodynamic measurements. It was found that MI resulted in the significantly elevated stresses and the reduced strains. The short-term inhalation of ultrafine Zn particles decreased stresses and increased strains in MI rats, but the long-term inhalation had the opposite effects. Hence, the short-term inhalation of ultrafine Zn particles could alleviate the MI-induced LV dysfunction while the long-term inhalation impaired it.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"23 1","pages":"78"},"PeriodicalIF":2.9,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11299344/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141892803","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":"Improving cardiovascular risk prediction with machine learning: a focus on perivascular adipose tissue characteristics.","authors":"Cong He, Fangye Wu, Linfeng Fu, Lingting Kong, Zefeng Lu, Yingpeng Qi, Hongwei Xu","doi":"10.1186/s12938-024-01273-5","DOIUrl":"10.1186/s12938-024-01273-5","url":null,"abstract":"<p><strong>Background: </strong>Timely prevention of major adverse cardiovascular events (MACEs) is imperative for reducing cardiovascular diseases-related mortality. Perivascular adipose tissue (PVAT), the adipose tissue surrounding coronary arteries, has attracted increased amounts of attention. Developing a model for predicting the incidence of MACE utilizing machine learning (ML) integrating clinical and PVAT features may facilitate targeted preventive interventions and improve patient outcomes.</p><p><strong>Methods: </strong>From January 2017 to December 2019, we analyzed a cohort of 1077 individuals who underwent coronary CT scanning at our facility. Clinical features were collected alongside imaging features, such as coronary artery calcium (CAC) scores and perivascular adipose tissue (PVAT) characteristics. Logistic regression (LR), Framingham Risk Score, and ML algorithms were employed for MACE prediction.</p><p><strong>Results: </strong>We screened seven critical features to improve the practicability of the model. MACE patients tended to be older, smokers, and hypertensive. Imaging biomarkers such as CAC scores and PVAT characteristics differed significantly between patients with and without a 3-year MACE risk in a population that did not exhibit disparities in laboratory results. The ensemble model, which leverages multiple ML algorithms, demonstrated superior predictive performance compared with the other models. Finally, the ensemble model was used for risk stratification prediction to explore its clinical application value.</p><p><strong>Conclusions: </strong>The developed ensemble model effectively predicted MACE incidence based on clinical and imaging features, highlighting the potential of ML algorithms in cardiovascular risk prediction and personalized medicine. Early identification of high-risk patients may facilitate targeted preventive interventions and improve patient outcomes.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"23 1","pages":"77"},"PeriodicalIF":2.9,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11299393/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141888417","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":"Attention-enhanced dilated convolution for Parkinson's disease detection using transcranial sonography.","authors":"Shuang Chen, Yuting Shi, Linlin Wan, Jing Liu, Yongyan Wan, Hong Jiang, Rong Qiu","doi":"10.1186/s12938-024-01265-5","DOIUrl":"10.1186/s12938-024-01265-5","url":null,"abstract":"<p><strong>Background: </strong>Transcranial sonography (TCS) plays a crucial role in diagnosing Parkinson's disease. However, the intricate nature of TCS pathological features, the lack of consistent diagnostic criteria, and the dependence on physicians' expertise can hinder accurate diagnosis. Current TCS-based diagnostic methods, which rely on machine learning, often involve complex feature engineering and may struggle to capture deep image features. While deep learning offers advantages in image processing, it has not been tailored to address specific TCS and movement disorder considerations. Consequently, there is a scarcity of research on deep learning algorithms for TCS-based PD diagnosis.</p><p><strong>Methods: </strong>This study introduces a deep learning residual network model, augmented with attention mechanisms and multi-scale feature extraction, termed AMSNet, to assist in accurate diagnosis. Initially, a multi-scale feature extraction module is implemented to robustly handle the irregular morphological features and significant area information present in TCS images. This module effectively mitigates the effects of artifacts and noise. When combined with a convolutional attention module, it enhances the model's ability to learn features of lesion areas. Subsequently, a residual network architecture, integrated with channel attention, is utilized to capture hierarchical and detailed textures within the images, further enhancing the model's feature representation capabilities.</p><p><strong>Results: </strong>The study compiled TCS images and personal data from 1109 participants. Experiments conducted on this dataset demonstrated that AMSNet achieved remarkable classification accuracy (92.79%), precision (95.42%), and specificity (93.1%). It surpassed the performance of previously employed machine learning algorithms in this domain, as well as current general-purpose deep learning models.</p><p><strong>Conclusion: </strong>The AMSNet proposed in this study deviates from traditional machine learning approaches that necessitate intricate feature engineering. It is capable of automatically extracting and learning deep pathological features, and has the capacity to comprehend and articulate complex data. This underscores the substantial potential of deep learning methods in the application of TCS images for the diagnosis of movement disorders.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"23 1","pages":"76"},"PeriodicalIF":2.9,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11290250/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141858925","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}