Medical Engineering & Physics最新文献

{"title":"Detecting inter-adjustment coupling changes for transtibial adjustable prosthetic sockets: A novel motion capture model feasibility study","authors":"Michael Baldock,&nbsp;Niamh Gill,&nbsp;Vikranth Harthikote Nagaraja,&nbsp;Samantha Curtin","doi":"10.1016/j.medengphy.2025.104390","DOIUrl":"10.1016/j.medengphy.2025.104390","url":null,"abstract":"<div><div>Prosthetic sockets provide a connection between the residual limb and prosthesis. However, this connection is non-rigid, leading to unintended prosthesis movement during functional activities. These movements can diminish socket function, cause discomfort, and damage soft tissue. Adjustable sockets allow volumetric adjustments to be made; however, their impact on this connection is unknown.</div><div>This study aims to assess the feasibility of a novel motion capture model in measuring inter-adjustment level prosthetic movement changes for an adjustable socket.</div><div>A single male participant with unilateral transtibial amputation was recruited, and a bespoke socket with a large adjustable posterior panel was manufactured. Relative movement and comfort of the prosthesis were measured for five socket tightness settings.</div><div>The novel model detected significant changes in inter-adjustment level prosthetic movements during swing phase, in both surge and pistoning displacements. However, there was no significant difference in the range of movement over the whole gait cycle. The tightest and loosest socket adjustments produced a detectable difference in comfort. Although specific to this individual and socket design, the novel motion capture model proved effective in detecting differences in adjustable prosthetic socket performance. Quantifying the influence of changes in socket shape facilitates a greater understanding of prosthetic fit and comfort.</div></div>","PeriodicalId":49836,"journal":{"name":"Medical Engineering & Physics","volume":"143 ","pages":"Article 104390"},"PeriodicalIF":1.7,"publicationDate":"2025-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144581272","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 brain-computer interface performance with optimized frequency interaction and enhancement techniques: CFC-PSO-XGBoost (CPX)","authors":"Xiao Xiao ,&nbsp;Haoyue Li","doi":"10.1016/j.medengphy.2025.104392","DOIUrl":"10.1016/j.medengphy.2025.104392","url":null,"abstract":"<div><h3>Purpose</h3><div>This work aims to increase the classification accuracy of motor imagery-based brain-computer interface (MI-BCI) by employing Cross-Frequency Coupling (CFC) and using spontaneous EEG as an input for the features to increase the system's robustness.</div></div><div><h3>Methods</h3><div>Using a benchmark MI-BCI dataset, we examined 25 participants who completed two trials of a motor imagery task split into two classes. Our methodology involved preprocessing EEG data, using Phase-Amplitude Coupling (PAC) to extract CFC characteristics and Particle Swarm Optimization (PSO) to identify the optimal channels. The XGBoost method was utilized to classify the data, and 10-fold cross-validation was employed to verify the results. They are integrated into a single pipeline, named CFC-PSO-XGBoost (CPX).</div></div><div><h3>Results</h3><div>With an average classification accuracy of 76.7 % ± 1.0 %, with only eight EEG channels, the suggested approach (CPX) outperformed cutting-edge techniques like CSP (60.2 % ± 12.4 %), FBCSP (63.5 % ± 13.5 %), FBCNet (68.8 % ± 14.6 %), and EEGNet. This significant improvement demonstrates the effectiveness of CFC features and PSO for channel selection in MI-BCI classification. Furthermore, the method was evaluated on the public BCI Competition IV-2a dataset, achieving an average multi-class classification accuracy of 78.3 % (95 % CI: 74.85–81.76 %), confirming the scalability and robustness of CPX on external benchmarks.</div></div><div><h3>Conclusion</h3><div>CPX leveraging spontaneous EEG signals and CFC features significantly improves classification accuracy. We anticipate this methodology will be a robust and practical solution in BCI applications, providing better brain-to-device communication with low-channel utilization and considerable performance metrics.</div></div>","PeriodicalId":49836,"journal":{"name":"Medical Engineering & Physics","volume":"143 ","pages":"Article 104392"},"PeriodicalIF":1.7,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144596980","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":"Automatically predicting lung tumor invasiveness using deep neural networks","authors":"Xiuyuan Xu ,&nbsp;Nan Chen ,&nbsp;Zongxuan Jin ,&nbsp;Zihuai Wang ,&nbsp;Yan Wang ,&nbsp;Qiang Pu ,&nbsp;Zhang Yi ,&nbsp;Lunxu Liu ,&nbsp;Jixiang Guo","doi":"10.1016/j.medengphy.2025.104385","DOIUrl":"10.1016/j.medengphy.2025.104385","url":null,"abstract":"<div><div>Early lung cancer invasive detection is important for further treatment and saving lives. In clinical practice, lung tumor invasiveness (LTI) detection is very challenging, imaging-based automatic prediction algorithms offer a non-invasive approach. However, the lack of publicly available datasets and the imbalance of clinical categories are key issues limiting the development of automatic predictive methods. To address the above issues, a large well-labeled high-quality computed tomography dataset was collected from 804 patients, and each sample was labeled with a binary classification label according to the gold standard pathological report after surgery. Then, a novel artificial system, lung tumor invasiveness prediction neural network (LTI-Net), was proposed to perform the binary classification of lung tumors by solving the class imbalance problem and improving the performance diagnosis under such imbalance settings. We adopted a three-dimensional residual neural network as the backbone architecture to effectively captures intra-tumor heterogeneity through scanning the distribution changes of CT values in lesion regions in imaging data. Additionally, we introduced a novel surrogate function to approximate the area under the curve (AUC) metric. By leveraging both positive and negative sample pairs during the training process, this formulation enhances discriminative feature extraction while maintaining stable optimization dynamics. Comprehensive experiments on our collected dataset demonstrated the potential of our LTI-Net method. LTI-Net improved the score of the <strong>h</strong>armonic <strong>m</strong>ean of true <strong>p</strong>ositives rate and true <strong>n</strong>egatives rate (HMoPN) significantly when compared to the current state-of-the-art methods and improved 2.92% of the HMoPN score in different imbalanced settings.</div></div>","PeriodicalId":49836,"journal":{"name":"Medical Engineering & Physics","volume":"143 ","pages":"Article 104385"},"PeriodicalIF":1.7,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144557547","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":"Optimization dental implant thread using FEA and RSM technique","authors":"Sambhrant Srivastava","doi":"10.1016/j.medengphy.2025.104389","DOIUrl":"10.1016/j.medengphy.2025.104389","url":null,"abstract":"<div><div>This article explores the popularity of dental implants for missing teeth, introducing a novel bio-composite material made from chitosan-reinforced bamboo mat and nano-silica particles. Mechanical properties of five samples are compared, and Response Surface Methodology optimizes the implant's thread shape, compared to titanium alloy. Stress shielding is noted, and bone shielding is analyzed using von Mises (ž) and deformation (δ) ratios, showing comparable efficacy to titanium alloy. The study supports bio-composite dental implants as a potential alternative to the standard titanium alloy, indicating similar effects on bone shielding.</div></div>","PeriodicalId":49836,"journal":{"name":"Medical Engineering & Physics","volume":"143 ","pages":"Article 104389"},"PeriodicalIF":1.7,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144632086","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":"Posture prediction models in digital human modeling for ergonomic design: A systematic review","authors":"Mengjie Zhang,&nbsp;Arne Nieuwenhuys,&nbsp;Yanxin Zhang","doi":"10.1016/j.medengphy.2025.104391","DOIUrl":"10.1016/j.medengphy.2025.104391","url":null,"abstract":"<div><div>Posture prediction models have been widely used to support ergonomic design. This systematic review critically assessed the development, validation, and applications of posture prediction models in Digital Human Modeling (DHM). Following PRISMA guidelines, 24 studies were included from a search across nine academic databases, categorized into data-driven models (n = 12) and optimization-based models (n = 12). Data-driven models, particularly those employing neural network regression and artificial neural networks, demonstrated strong predictive accuracy and adaptability, but often lacked generalizability due to data imbalance and limited participant/task diversity. Optimization-based models, using algorithms such as gradient descent and genetic algorithms, showed high biomechanical fidelity but computational challenges and limited computer-aided design (CAD) integration. While a few models have been integrated with existing CAD software such as JACK and Santos™, most lacked ergonomic evaluation and real-time usability. Limitations identified include insufficient diverse datasets, computational inefficiencies, and limited validation in real-world conditions. Future research should prioritize model development supported by scalable motion data using computer vision-based technologies and hybrid strategies that combine learning-based inference with biomechanical simulation, offering a promising path toward achieving both accuracy and physiological realism in posture prediction.</div></div>","PeriodicalId":49836,"journal":{"name":"Medical Engineering & Physics","volume":"143 ","pages":"Article 104391"},"PeriodicalIF":1.7,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144570866","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":"Effect of tool center point angles on drilling performance in robotic cortical bone procedures","authors":"Ju-Hyung Ha ,&nbsp;Joon-Hyeok Choe ,&nbsp;Jisoo Kim ,&nbsp;Dong Min Kim ,&nbsp;Jaewoo Seo","doi":"10.1016/j.medengphy.2025.104388","DOIUrl":"10.1016/j.medengphy.2025.104388","url":null,"abstract":"<div><div>This study investigates the effects of different tool center point (TCP) angles on drilling precision and accuracy in cortical bone using a six-axis robot. Tool paths were generated using simulation software, and various TCP angles and corresponding robot postures were implemented in a real-world setting. To assess dynamic characteristics, experimental modal analysis was performed to measure natural frequencies and damping ratios (DRs) across the different configurations. Based on these results, spindle displacement was measured to quantify vibrations during drilling, allowing identification of configurations associated with lower vibration levels. To validate these findings, drilling experiments were conducted on cortical bone specimens to compare cutting performance. The results showed a 20.77 % average reduction in drilling torque and a 7.42 % decrease in the delamination factor (DF), which affects the bonding strength between the cortical screw and the bone. These findings suggest that the selection of TCP angle parameters may influence drilling performance and hole quality, underlining the relevance of robotic-assisted drilling (RAD) angle control in cortical bone surgery.</div></div>","PeriodicalId":49836,"journal":{"name":"Medical Engineering & Physics","volume":"143 ","pages":"Article 104388"},"PeriodicalIF":1.7,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144557546","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 closed-form expression for the relationship between shear modulus from shear wave elastography and tangent modulus from tensile test","authors":"Luiz Carlos da Silva Nunes ,&nbsp;Liliam Fernandes de Oliveira ,&nbsp;Maria Clara Albuquerque Brandão ,&nbsp;Luciano Luporini Menegaldo","doi":"10.1016/j.medengphy.2025.104387","DOIUrl":"10.1016/j.medengphy.2025.104387","url":null,"abstract":"<div><div>Although shear wave elastography has been increasingly employed for in-vivo studies of the mechanical properties of human tendons, critical questions have emerged regarding the correlation between acoustic measurements and the shear modulus determined from mechanical testing. This study proposes a closed-form expression for estimating the tangent modulus of tendons under tension. This expression is formulated as a function of the shear modulus and is obtained by combining identifications on both tensile testing and elastography measurements. A one-dimensional nonlinear model is employed for tensile test data, accounting for the strain behavior of tendon fiber bundles as a function of stress and four identifiable parameters. This model describes the entire physiological range, including the tendon in its crimped state. A new model based on empirical observations defines the shear modulus response obtained from elastography in terms of tensile stress. By combining these models, the closed-form expression was derived. Stress-strain data obtained from tensile tests and shear modulus measurements from shear wave elastography of eleven in vitro samples of fresh-frozen human Achilles tendons, experimentally obtained, were reanalyzed. The proposed methodology reduces high-frequency noise in the stress-strain data, producing tangent-modulus estimates less sensitive to numerical differentiation. This approach is also practical in scenarios where tendons are crimped, or fibers are fully extended, providing estimations of material properties that combine potentially in-vivo SSI elastography with a tendon material constitutive model.</div></div>","PeriodicalId":49836,"journal":{"name":"Medical Engineering & Physics","volume":"143 ","pages":"Article 104387"},"PeriodicalIF":1.7,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144549169","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 3D multi-modal multi-scale end-to-end classifier for Alzheimer's disease diagnosis","authors":"M. Khojaste-Sarakhsi,&nbsp;Seyedhamidreza Shahabi Haghighi,&nbsp;S.M.T. Fatemi Ghomi","doi":"10.1016/j.medengphy.2025.104382","DOIUrl":"10.1016/j.medengphy.2025.104382","url":null,"abstract":"<div><div>This study presents a novel 3D multi-modal multi-scale end-to-end classifier to enhance Alzheimer's Disease (AD) diagnosis by integrating MRI, PET, age, and MMSE cognitive test scores. Leveraging a ResNet-inspired architecture with trainable multi-scale convolutional scaling, the classifier categorizes subjects into four classes—Normal Control (NC), Stable Mild Cognitive Impairment (sMCI), Progressive Mild Cognitive Impairment (pMCI), and AD—capturing both structural and functional brain pathology. A tailored fusion strategy (MA_PC) processes MRI with age and PET with MMSE in parallel branches, optimizing complementary information use. Extensive experiments using the ADNI dataset, a five-fold cross-validation scheme, and an unseen test set demonstrate that MA_PC with convolutional scaling achieves superior performance, outperforming commonly used fusion strategies as well as pre-trained 3D ResNets designed for medical imaging applications. A comparative analysis reveals that 4-class classification consistently surpasses a 3-class approach (NC, MCI, AD), highlighting the model's ability to distinguish subtle AD progression stages. These findings highlight the critical role of advanced data fusion and scaling methods in enhancing AD diagnosis accuracy and underscore the potential of multi-modal CNNs in advancing medical imaging research.</div></div>","PeriodicalId":49836,"journal":{"name":"Medical Engineering & Physics","volume":"143 ","pages":"Article 104382"},"PeriodicalIF":1.7,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144518367","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":"Computer aided diagnostic model for knee osteoarthritis: A multi-modal feature regression approach","authors":"Zewen Shi ,&nbsp;Fang Yang ,&nbsp;Rongyao Yu ,&nbsp;Zeming Chen ,&nbsp;Xianjun Chen ,&nbsp;Qingjiang Pang ,&nbsp;Zhewei Ye ,&nbsp;Lin Shi ,&nbsp;Yang Song","doi":"10.1016/j.medengphy.2025.104378","DOIUrl":"10.1016/j.medengphy.2025.104378","url":null,"abstract":"<div><h3>Background</h3><div>X-ray imaging is crucial for diagnosing knee osteoarthritis (KOA) in clinical treatment. Computer-assisted diagnostic models reduce the impact of physicians' subjective factors on the accuracy of X-ray diagnoses. Therefore, continuous improvement of these models is necessary.</div></div><div><h3>Methods</h3><div>This study introduces a novel computer-aided diagnostic model for KOA, based on multi-modal feature regression using X-ray images. Specifically, two different modalities of diagnostic features are extracted. Firstly, by analyzing the diagnostic indicators of KOA in X-ray images, we explore the diagnostic basis of osteoarthritis from the perspective of image content and design several image content-based features for measuring bone gap, bone skin thickness, and bone mass. Meanwhile, medical information-based features such as age, gender, and surgical history are also integrated as diagnostic features. Then, the mapping relationship between the diagnostic features and the severity of osteoarthritis (denoted by K-L classification) is established using support vector regression to build the knee osteoarthritis diagnostic model.</div></div><div><h3>Results</h3><div>To validate the efficacy of our diagnostic model, we curated the NDKY-N2H knee X-ray image database, encompassing 1200 knee joint X-ray images, each paired with its corresponding K-L classification. By incorporating image preprocessing and a module for basic patient information, the accuracy of the model's KOA diagnosis has been improved. The final X-ray image KOA diagnostic model, developed based on multimodal feature regression, achieved an accuracy of over 98.42 % ± 0.11 % in identifying KOA. Additionally, for diagnosing KOA severity using K-L grading, the accuracy reached 85.06 % ± 0.49 %. These findings indicate that the proposed model performs exceptionally well in diagnosing knee osteoarthritis.</div></div><div><h3>Conclusion</h3><div>In conclusion, our research highlights the critical role of accurate KOA diagnosis in enhancing patient health. The innovative approach using multi-modal feature regression, which combines image content and medical information features, shows significant promise for providing reliable and efficient KOA diagnoses through X-ray imaging.</div></div>","PeriodicalId":49836,"journal":{"name":"Medical Engineering & Physics","volume":"143 ","pages":"Article 104378"},"PeriodicalIF":1.7,"publicationDate":"2025-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144518366","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":"Medical devices: Materials, mechanics, and manufacturing","authors":"Sotiris Korossis,&nbsp;Vadim V. Silberschmidt","doi":"10.1016/j.medengphy.2025.104384","DOIUrl":"10.1016/j.medengphy.2025.104384","url":null,"abstract":"","PeriodicalId":49836,"journal":{"name":"Medical Engineering & Physics","volume":"142 ","pages":"Article 104384"},"PeriodicalIF":1.7,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144480849","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}