Medical Engineering & Physics最新文献

{"title":"Mathematical representation and nonlinear modelling of the Wheatley mitral valve","authors":"H.L. Oliveira ,&nbsp;G.C. Buscaglia ,&nbsp;J.A. Cuminato ,&nbsp;S. McKee ,&nbsp;I.W. Stewart ,&nbsp;M.M. Kerr ,&nbsp;D.J. Wheatley","doi":"10.1016/j.medengphy.2025.104283","DOIUrl":"10.1016/j.medengphy.2025.104283","url":null,"abstract":"<div><div>This study is concerned with the Wheatley design of the mitral valve. A mathematical description, in terms of elementary functions, is provided for the S-shaped leaflets. This is based on a level set containing symmetric circles (or more generally ellipses) which allow parametrisation. A geometric nonlinear mechanical model subjected to a uniform pressure gradient and in the absence of inertial forces is introduced. The model results in a system of nonlinear equations that is solved using iterative incremental techniques. Under normal pressure loads, the S-shaped geometries induce internal forces which manifest themselves in two combined effects: bending and torsion. As a consequence, the supports are subject to periodic bending actions that tend to deform the support frame towards the interior of the valve. Providing resistance becomes vital for maintaining stable equilibrium. It is also observed that for circular base shape geometries, the mechanism for transmitting the equilibrium forces remains unchanged when the height/diameter ratio is kept below 2.</div></div>","PeriodicalId":49836,"journal":{"name":"Medical Engineering & Physics","volume":"136 ","pages":"Article 104283"},"PeriodicalIF":1.7,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143166301","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":"Comparison of wired and wireless electromagnetic hand motion tracking in central venous access: Are they equivalent enough to cut the cord?","authors":"Hamza Ali ,&nbsp;Oussama Metrouh ,&nbsp;Muneeb Ahmed ,&nbsp;John D. Mitchell ,&nbsp;Vincent Baribeau ,&nbsp;Matthew R. Palmer ,&nbsp;Christopher MacLellan ,&nbsp;Jeffrey Weinstein","doi":"10.1016/j.medengphy.2024.104280","DOIUrl":"10.1016/j.medengphy.2024.104280","url":null,"abstract":"<div><h3>Purpose</h3><div>This study aims to compare a commercially available wired and wireless tracker in motion analysis of interventional radiologists performing simulated ultrasound-guided central venous access.</div></div><div><h3>Methods and material</h3><div>Interventional radiologists were asked to volunteer for the study. Participants were asked to place central venous lines on a commercially available, standardized manikin as their needle hand and ultrasound probe motion were recorded using electromagnetic trackers. Each participant performed a total of 10 trials, with 5 trials recorded using a wired tracker and 5 using a wireless tracker. Institution-developed software was used to calculate established motion metrics (path length and number of movements). The motion metrics from the two trackers were compared.</div></div><div><h3>Results</h3><div>Seven interventional radiologists participated in the study. Path length (wireless vs. wired: 773.1 cm ± 85.7 cm vs. 959.5 cm ± 303.6 cm, <em>p</em> &lt; 0.001) and number of movements (193 ± 52 vs. 231 ± 50.5, <em>p</em> = 0.001) differed significantly between the two trackers; however, the time to complete the procedure (51.8 s ± 14.8 s vs. 49.8 s ± 10.5 s, <em>p</em> = 0.68) was similar across trackers.</div></div><div><h3>Conclusion</h3><div>The motion metrics of the same operators differ significantly between wired and wireless trackers. Accounting for the sampling frame rate and the frame efficiency of the wireless sensors can provide comparable motion data.</div></div>","PeriodicalId":49836,"journal":{"name":"Medical Engineering & Physics","volume":"136 ","pages":"Article 104280"},"PeriodicalIF":1.7,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143166667","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":"Impact testing methods to simulate head impacts due to falls from standing height","authors":"Morteza Seidi ,&nbsp;Vincent Caccese ,&nbsp;Marzieh Memar","doi":"10.1016/j.medengphy.2025.104299","DOIUrl":"10.1016/j.medengphy.2025.104299","url":null,"abstract":"<div><div>Fall is one of the leading causes of traumatic brain injury (TBI), and thus, there is an increasing interest in validated tools and protective devices to prevent fall-related TBI. Developing head protective technologies for fall requires a reliable testing method to realistically mimic kinematics of head impacts due to fall to evaluate the injury attenuation of such protective headgears. The objective of this study is to recommend an appropriate and repeatable testing method for simulating fall-related head impacts due to standing height falls. To that end, several impact testing methods that commonly use to assess the efficacy of protective headgear were evaluated and compared. The four different test methods include: (1) a whole-body anthropomorphic test device (ATD) drop; (2) a drop-tower equipped with a Hybrid III head and neck assembly; (3) ASTM F429/F1446 standard; and (4) a linear impactor equipped with a Hybrid III head and neck assembly. Although the ATD drop system simulates fall-related head impacts realistically by considering the whole-body kinematics during falls from standing height, this method showed low repeatability. Among the three repeatable testing methods, only the drop tower with Hybrid III head and neck assembly showed statistically similar results to the ATD drop system for front and rear head impacts for all parameters examined in this study including peak linear acceleration, Head Injury Criterion, peak angular acceleration and peak angular velocity. The results suggested that drop-tower with Hybrid III head and neck assembly can realistically captured both translational and rotational motions of the head during impact due to standing height falls in a repeatable manner.</div></div>","PeriodicalId":49836,"journal":{"name":"Medical Engineering & Physics","volume":"136 ","pages":"Article 104299"},"PeriodicalIF":1.7,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143350124","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":"DP-CLAM: A weakly supervised benign-malignant classification study based on dual-angle scanning ultrasound images of thyroid nodules","authors":"Shuhuan Wang ,&nbsp;Shuangqingyue Zhang ,&nbsp;Lingmin Liao ,&nbsp;Chunquan Zhang ,&nbsp;Debin Xu ,&nbsp;Long Huang ,&nbsp;He Ma","doi":"10.1016/j.medengphy.2025.104288","DOIUrl":"10.1016/j.medengphy.2025.104288","url":null,"abstract":"<div><div>In this paper, a two-stage task weakly supervised learning algorithm is proposed. It accurately achieved patient-level classification task of benign and malignant thyroid nodules based on ultrasound images from two scanning angles: long axis and short axis of the thyroid site. In the first stage, 68,208 ultrasound scanning images of 588 patients are used to train the underlying classification model. In the second stage, feature vectors of ultrasound images with dual scan angles are extracted using the classification model in the first stage. Then the feature vectors are assigned to position sequences in the order of visual reception by the physician. Finally, the location decision is made through a weakly supervised learning approach. Combined with the dual-angle difference information carried in the overall features, our method accurately achieved benign and malignant classification of thyroid nodules at the patient level. An accuracy of 93.81 % for benign and malignant classification of patients was obtained in our test set. The accuracy of benign and malignant classification of patients with thyroid nodules is improved by our weakly supervised learning method based on a two-stage classification task. It also reduced the pressure of imaging physicians in diagnosing a large number of images. In the clinical auxiliary diagnosis, it provides an effective reference for the timely determination of thyroid nodule patients.</div></div>","PeriodicalId":49836,"journal":{"name":"Medical Engineering & Physics","volume":"136 ","pages":"Article 104288"},"PeriodicalIF":1.7,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143166643","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":"MSRMMP: Multi-scale residual module and multi-layer pseudo-supervision for weakly supervised segmentation of histopathological images","authors":"Yuanchao Xue ,&nbsp;Yangsheng Hu ,&nbsp;Yu Yao ,&nbsp;Jie Huang ,&nbsp;Haitao Wang ,&nbsp;Jianfeng He","doi":"10.1016/j.medengphy.2025.104284","DOIUrl":"10.1016/j.medengphy.2025.104284","url":null,"abstract":"<div><div>Accurate semantic segmentation of histopathological images plays a crucial role in accurate cancer diagnosis. While fully supervised learning models have shown outstanding performance in this field, the annotation cost is extremely high. Weakly Supervised Semantic Segmentation (WSSS) reduces annotation costs due to the use of image-level labels. However, these WSSS models that rely on Class Activation Maps (CAM) focus only on the most salient parts of the image, which is challenging when dealing with semantic segmentation tasks involving multiple targets. We propose a two-stage weakly supervised segmentation framework (MSRMMP) to resolve the above problems, the generation of pseudo masks based on multi-scale residual networks (MSR-Net) and the semantic segmentation based on multi-layer pseudo-supervision. MSR-Net fully captures the local features of an image through multi-scale residual module (MSRM) and generates pseudo masks using image-level label. Additionally, we employ Transunet as the segmentation backbone, and uses multi-layer pseudo-supervision algorithms to solve the problem of pseudo-mask inaccuracy. Experiments performed on two publicly available histopathology image datasets show that our proposed method outperforms other state-of-the-art weakly supervised semantic segmentation methods. Additionally, it outperforms the fully-supervised model in mIoU and has a similar result in fwIoU when compared to fully-supervised models. Compared with manual labeling, our model can significantly save the labeling time from hours to minutes.</div></div>","PeriodicalId":49836,"journal":{"name":"Medical Engineering & Physics","volume":"136 ","pages":"Article 104284"},"PeriodicalIF":1.7,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143166644","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":"Improved U-Net based on ResNet and SE-Net with dual attention mechanism for glottis semantic segmentation","authors":"Jui-Chung Ni ,&nbsp;Shih-Hsiung Lee ,&nbsp;Yen-Cheng Shen ,&nbsp;Chu-Sing Yang","doi":"10.1016/j.medengphy.2025.104298","DOIUrl":"10.1016/j.medengphy.2025.104298","url":null,"abstract":"<div><div>In previous tasks of glottis image segmentation, the position attention mechanism was rarely incorporated, neglecting the detailed information in glottis position detection. Aiming to improve the U-Net architecture, this study introduces the dual attention mechanism based on the squeeze and excitation (SE)-Net model. This mechanism can integrate traditional channel attention with position attention mechanisms to effectively adjust the weights of crucial features and significance of positions. Replacing the weight adjustment mechanism in SE-Net with the dual attention mechanism creates a broader perspective, enhancing the sensitivity to important features in the model. Furthermore, based on the characteristics of SE-Net, the skip-connection feature of U-Net can still be retained. The architecture proposed in this paper further replaces the convolutional layers in the U-Net encoder with the bottleneck to preserve the information on the features without significantly increasing the amount of computation. In addition, the decoder is replaced with residual blocks to reduce overfitting. The results of the experiment showed that models with retained features demonstrate better accuracy while reducing overfitting. The proposed model achieved positive results in predicting the scores on the public benchmark for automatic glottis segmentation (BAGLS) dataset.</div></div>","PeriodicalId":49836,"journal":{"name":"Medical Engineering & Physics","volume":"136 ","pages":"Article 104298"},"PeriodicalIF":1.7,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143350125","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":"Improving spike sorting efficiency with separability index and spectral clustering","authors":"Leila Ranjbar ,&nbsp;Hossein Parsaei ,&nbsp;Mohammad Mehdi Movahedi ,&nbsp;Sam Sharifzadeh Javidi","doi":"10.1016/j.medengphy.2024.104265","DOIUrl":"10.1016/j.medengphy.2024.104265","url":null,"abstract":"<div><div>This study explores the effectiveness of spectral clustering for spike sorting and proposes a Separability Index to measure the difficulty of spike sorting for a signal. The accuracy of spectral clustering is evaluated using different feature sets, including raw samples, first and second derivatives, and principal components analysis (PCA), and compared to two previously published methods. The results obtained over a dataset including 16 signals show that raw samples, with an average accuracy of 73.84 %, are effective for spectral clustering-based spike sorting. The analysis demonstrates that the proposed Separability Index can be utilized to classify signals beforehand, reducing the cost and processing time of large datasets. Furthermore, the proposed index can reveal spike sorting difficulty, making it a valuable tool for comparing the performance of various spike sorting methods in depth. The proposed method has higher accuracy (up to 23 %) compared to two previously published methods, and its accuracy is aligned with the Separability Index (correlation coefficient = 0.71). Overall, this study contributes to the field of spike sorting and offers insights into leveraging spectral clustering for this task.</div></div>","PeriodicalId":49836,"journal":{"name":"Medical Engineering & Physics","volume":"135 ","pages":"Article 104265"},"PeriodicalIF":1.7,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143096506","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":"A novel ECG-based approach for classifying psychiatric disorders: Leveraging wavelet scattering networks","authors":"Hardik Telangore ,&nbsp;Nishant Sharma ,&nbsp;Manish Sharma ,&nbsp;U. Rajendra Acharya","doi":"10.1016/j.medengphy.2024.104275","DOIUrl":"10.1016/j.medengphy.2024.104275","url":null,"abstract":"<div><div>Individuals with neuropsychiatric disorders experience both physical and mental difficulties, hindering their ability to live healthy lives and participate in daily activities. It is challenging to diagnose these disorders due to a lack of reliable diagnostic tests and the complex symptoms and treatments for various disorders. Generally, psychiatric disorders are identified manually by doctors using questionnaires, which may be prone to subjectivity and human errors. A few automated systems have recently been developed to identify these disorders using physiological signals, including electroencephalogram (EEG) and electrocardiogram (ECG) signals. Often, EEG signals are used to identify psychiatric disorders, but the EEG signals are nonlinear and non-stationary in nature and hence are relatively complex to analyze when compared to the ECG signals. The ECG signals in psychiatric patients are used due to the connection between the heart and brain. The proposed study is aimed at investigating the use of ECG signals for the automated identification of neuropsychiatric disorders, including bipolar disorder (BD), depression (DP), and schizophrenia (SZ). Generally, convolution neural networks (CNNs) have proven to be effective in accurately identifying psychological conditions. However, their application requires a large amount of data and technical expertise. The wavelet scattering network (WSN), a variant of CNNs, was introduced to overcome these limitations. The WSN is a network capable of accurately detecting unique patterns in the signal. The proposed research incorporated the WSN network and was conducted using a Psychiatric ECG Beat Dataset with a population of 233 subjects, of whom 198 were diagnosed with multiple psychiatric disorders, and 35 were control subjects. ECG signals from 3570 heartbeats were collected and analyzed using wavelet scattering-based feature extraction and machine learning techniques. The Fine K-Nearest Neighbor (FKNN) algorithm produced the best results with an average classification accuracy of 99.8% and a Kappa value of 0.996 using a ten-fold cross-validation. The model yielded an accuracy of 99.78%, 99.94%, 99.98%, and 100% for automated identification of BD, DP, SZ, and control subjects, respectively, with F1 scores and precision values close to 1. The proposed method could also help in the automated clinical detection of different psychiatric disorders.</div></div>","PeriodicalId":49836,"journal":{"name":"Medical Engineering & Physics","volume":"135 ","pages":"Article 104275"},"PeriodicalIF":1.7,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143095987","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":"A combined experimental and numerical approach to evaluate hernia mesh biomechanical stability in situ","authors":"Arthur Jourdan ,&nbsp;Anthony Vegleur ,&nbsp;Jeff Bodner ,&nbsp;Pascal Rousset ,&nbsp;Guillaume Passot ,&nbsp;Anicet Le Ruyet","doi":"10.1016/j.medengphy.2024.104271","DOIUrl":"10.1016/j.medengphy.2024.104271","url":null,"abstract":"<div><div>A ventral hernia involves tissue protrusion through the abdominal wall (AW). It is a common surgical issue with high recurrence rates. Primary stability of hernia meshes is essential to guarantee mesh integration, yet existing meshes often fail to match the AW's complex biomechanics. This study proposes a novel method aiming at understanding post-operative mesh-AW interactions. Three fresh frozen human specimens underwent an open Rives-Stoppa implantation of a synthetic hernia mesh coated with metallic micro-beads. Additional beads were placed into the AW muscle tissue. CT scans were conducted at increasing levels of intra-abdominal pressure to reproduce forced breathing. Beads 3D coordinates were exported from the CT-scans and motion and strain of both the hernia mesh and the AW were calculated. At 30 mmHg, the mesh-muscle motion (or sliding) was 2.3 ± 1.3 mm. Muscle exhibited significantly higher strains (12.9 ± 4.7 %) than the hernia mesh (4.7 ± 1.1 %), most likely due to difference in material properties between the mesh and the AW. A repeatability study was carried out to build confidence in the proposed method. This protocol can bring insights of the hernia mesh use-conditions to improve hernia mesh design requirements and develop safer implants to reduce hernia recurrence.</div></div>","PeriodicalId":49836,"journal":{"name":"Medical Engineering & Physics","volume":"135 ","pages":"Article 104271"},"PeriodicalIF":1.7,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143095988","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":"A robust method for parkinson's disease diagnosis: Combining electroencephalography signal features with reconstructed phase space images","authors":"Farnaz Garehdaghi,&nbsp;Yashar Sarbaz","doi":"10.1016/j.medengphy.2024.104276","DOIUrl":"10.1016/j.medengphy.2024.104276","url":null,"abstract":"<div><div>Parkinson's disease (PD) is a neurodegenerative disease. Since the diagnosis of the PD is mainly made based on the symptoms and after the disease progression, early diagnosis can play a crucial role in delaying the passage of the PD. There have been many methods focusing on disease diagnosis using electroencephalography (EEG) signals, where most of the proposed methods are data-dependent. Here, the study aims to propose a technique that, despite its high accuracy, is robust. Various features including fractal dimension, approximate entropy, largest Lyapunov exponent, and the energy of different frequency sub-bands were extracted from EEG signals. Multi-layer perceptron neural networks were used for classification based on these features. Additionally, 2D phase space images reconstructed from EEG signals were classified using convolutional neural networks. Finally, a combination of these features and images was used for classification using ResNets. During 10 rounds of training and testing, the mean accuracies were calculated for three cases: using only features, only images, and a combination of both. The mean accuracies were 84.67 %, 76.5 %, and 90.2 % respectively. The variances for each case were 35.6 %, 19.5 %, and 13.97 %. The lower variance when using a combination of features and images indicates a more accurate and robust classification.</div></div>","PeriodicalId":49836,"journal":{"name":"Medical Engineering & Physics","volume":"135 ","pages":"Article 104276"},"PeriodicalIF":1.7,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143135687","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}