Medical Engineering & Physics最新文献

{"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}

{"title":"Morphological characterization of median nerve and transverse carpal ligament from ultrasound images using convolutional neural networks","authors":"Jocelyn L. Hawk ,&nbsp;Shalon Walter ,&nbsp;Xiaoxiao Sun ,&nbsp;Zong-Ming Li","doi":"10.1016/j.medengphy.2025.104383","DOIUrl":"10.1016/j.medengphy.2025.104383","url":null,"abstract":"<div><h3>Objectives</h3><div>The purpose of this study was to automatically segment and quantify the median nerve and carpal arch from ultrasound images using convolutional neural network (CNN).</div></div><div><h3>Methods</h3><div>A U-Net method based on CNN was implemented for median nerve and transverse carpal ligament segmentation from cross-sectional ultrasound images of the distal carpal tunnel. Median nerve and ligament were measured using the manual segmentations and model predictions. Model performance was evaluated using Dice score coefficient (DSC), recall, and precision. Model performance parameters and morphological parameters were compared between the healthy and carpal tunnel syndrome patients using Wilcoxon signed-rank test. The reliability of the morphological measurements from the predictions was assessed by calculating mean average error and the intra-class coefficient (ICC).</div></div><div><h3>Results</h3><div>The DSC, recall, and precision were 0.89 ± 0.81, 0.94 ± 0.04, and 0.86 ± 0.08 for healthy subjects, respectively, for median nerve segmentation; the corresponding values for patients were 0.81 ± 0.08, 0.86 ± 0.10, and 0.77 ± 0.11, respectively. For ligament segmentation, the DSC, recall, and precision were 0.87 ± 0.03, 0.88 ± 0.04, and 0.87 ± 0.05, respectively, for healthy subjects; the corresponding values for patients were 0.77 ± 0.10, 0.77 ± 0.12, and 0.77 ± 0.09, respectively. Acceptable to excellent agreement was found between morphological measurements calculated using manual segmentations and model predictions. The carpal tunnel syndrome patients had larger median nerve cross-sectional area and carpal arch height than the healthy subjects when measured from the model predictions (p &lt; 0.05).</div></div><div><h3>Conclusions</h3><div>CNNs were used to automatically segment the median nerve and TCL with high accuracy. The model predictions provided reliable quantification of the carpal tunnel anatomy, demonstrating the potential diagnostic value using CNNs.</div></div>","PeriodicalId":49836,"journal":{"name":"Medical Engineering & Physics","volume":"143 ","pages":"Article 104383"},"PeriodicalIF":1.7,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536023","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":"Exploring the neural mechanisms of alpha-band EEG neurofeedback using portable devices: A pre-post comparative study","authors":"Xiaoyu Chen ,&nbsp;Li Sui","doi":"10.1016/j.medengphy.2025.104380","DOIUrl":"10.1016/j.medengphy.2025.104380","url":null,"abstract":"<div><h3>Objective</h3><div>This study aimed to clarify the neural mechanisms of alpha-band neurofeedback (NF) training using a portable EEG system and to evaluate its feasibility for cognitive enhancement in practical settings.</div></div><div><h3>Methods</h3><div>Sixteen healthy young adults (<span><math><mi>M</mi><mo>=</mo><mn>24.28</mn><mo>±</mo><mn>1.31</mn></math></span> years) completed eight sessions of alpha-band NF training. EEG signals were collected before and after training with a wearable 16-channel device. Neural changes were assessed using power spectral analysis, source localization (sLORETA), and functional connectivity analysis across five frequency bands.</div></div><div><h3>Results</h3><div>Training led to increased power in theta, alpha, beta, and gamma bands. Source analysis showed decreased current density in lower frequencies and increased activity in higher bands, reflecting a shift toward more efficient neural processing and enhanced cognitive network engagement, as supported by previous studies. Functional connectivity revealed stronger synchronization among frontal, parietal, and occipital regions involved in working memory.</div></div><div><h3>Conclusion</h3><div>Portable alpha-band neurofeedback training induces widespread neural modulation across multiple frequency bands and brain networks, supporting the feasibility of wearable EEG systems for accessible cognitive training.</div></div>","PeriodicalId":49836,"journal":{"name":"Medical Engineering & Physics","volume":"142 ","pages":"Article 104380"},"PeriodicalIF":1.7,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144262667","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":"Effects of wall compliance on pulsatile flow in a full-scale, patient-specific cerebral aneurysm model: Particle image velocimetry experiments","authors":"Ryuhei Yamaguchi ,&nbsp;Muhamed Albadawi ,&nbsp;Nadia Shaira Shafii ,&nbsp;Atsushi Saito ,&nbsp;Toshiyuki Nakata ,&nbsp;Khalid M. Saqr ,&nbsp;Hitomi Anzai ,&nbsp;Makoto Ohta","doi":"10.1016/j.medengphy.2025.104381","DOIUrl":"10.1016/j.medengphy.2025.104381","url":null,"abstract":"<div><div>The hemodynamics of elastic cerebral aneurysms are complicated by phenomena that affect the initiation and the progress of each aneurysm. The blood vessel deforms with pulsatile flow. In a phantom, however, it remains unclear whether the wall compliance can be neglected. In our previous study, the flow structure at another plane oriented perpendicular to the median plane was not clarified. In the approach presented here, an identical phantom is used for both the rigid and elastic wall models by adjusting the surrounding fluid when immersed in a bath. For this purpose, the full-scale phantom of an aneurysm was fabricated using a silicone elastomer. The hemodynamic factors at the orthogonal planes in the non-deformable and deformable models of the bifurcation in the middle cerebral artery were examined. Using two-dimensional particle image velocimetry, the flow velocity, the wall shear stress (WSS), the WSS gradient (WSSG), and the turbulent kinetic energy (TKE) were measured during pulsatile flow. Overall, the WSSG at the median plane is smaller than that at corresponding perpendicular plane. Additionally, the TKE in the deformable model is smaller than that in the non-deformable model. Our results have clarified the complex effects of aneurysm wall compliance on these hemodynamic factors.</div></div>","PeriodicalId":49836,"journal":{"name":"Medical Engineering & Physics","volume":"142 ","pages":"Article 104381"},"PeriodicalIF":1.7,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144306224","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}