Medical Engineering & Physics最新文献

{"title":"Utilization of skin color change for image-based tactile sensing","authors":"Seitaro Kaneko ,&nbsp;Hiroki Ishizuka ,&nbsp;Hidenori Yoshimura ,&nbsp;Hiroyuki Kajimoto","doi":"10.1016/j.medengphy.2025.104357","DOIUrl":"10.1016/j.medengphy.2025.104357","url":null,"abstract":"<div><div>Measurement of pressure distribution applied to a fingertip is crucial for the teleoperation of robots and human computer interface. Previous studies have acquired pressure distribution by affixing a sensor array to the fingertip or by optically recording the deformation of an object. However, these existing methods inhibit the fingertip from directly contacting the texture, and the pressure applied to the fingertip is measured indirectly. In this study, we propose a method to measure pressure distribution by directly touching a transparent object, focusing on the change in skin color induced by the applied pressure, caused by blood flow. We evaluated the relationship between pressure and skin color change when local pressure is applied, and found a correlation between the pressure and the color change. However, the contact area and the color change area did not align perfectly. We further explored the factor causing the spatial non-uniformity of the color change, by accounting for the stress distribution using finite element analysis. These results suggest that the proposed measurement method can be utilized to measure the internal stress distribution, and it is anticipated to serve as a simple sensor in the field of human computer interface.</div></div>","PeriodicalId":49836,"journal":{"name":"Medical Engineering & Physics","volume":"140 ","pages":"Article 104357"},"PeriodicalIF":1.7,"publicationDate":"2025-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143942198","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 properties of surgical mask modified by iodine-doped cellulose film with 2D reduced graphene oxide decorated with TiO2 nanocomposite","authors":"Bansod Sneha Bharat,&nbsp;Anju R Babu","doi":"10.1016/j.medengphy.2025.104360","DOIUrl":"10.1016/j.medengphy.2025.104360","url":null,"abstract":"<div><div>The surgical mask protects against respiratory infectious diseases and prevents the risk of secondary infection due to the lack of inactivation of viruses or pathogenic bacteria. This study aims to evaluate the properties of surgical masks after the modification with a nanocomposite of titanium oxide decorated on 2D reduced graphene oxide and iodine-carboxymethyl cellulose (TIRC). The titanium oxide decorated on 2D reduced graphene oxide was prepared by the hydrothermal method. Then, TIRC was synthesized by the solution-cast method, and the nanocomposite was spray-coated on the surgical mask. Further, chemical, structural, and morphological characterization was performed for TIRC-coated masks. The antibacterial properties of the TIRC-coated mask were evaluated using the disc diffusion method. Furthermore, the photocatalytic properties of the TIRC-coated mask were determined towards methylene blue (MB) dye. The Ti-O-Ti (624 cm⁻¹) bonds and <em>C</em> = <em>C</em> (1637 cm⁻¹) bonds of the TIRC-coated mask were confirmed by Fourier transform infrared spectroscopy. Raman spectra for TIRC nanocomposite also confirmed the presence of O-Ti-O bonds at peak 142 cm⁻¹, iodine molecules at peak 72 cm⁻¹, D band at 1354 cm⁻¹, and G band<del>s</del> at 1593 cm⁻¹. The TIRC coating improved the antibacterial properties of the mask with a zone of inhibition of 13.97 ± 0.14 mm and 13.12 ± 1.58 mm towards <em>S. aureus</em> and <em>Escherichia coli</em> bacteria, respectively. Moreover, the TIRC-coated mask exhibited 92.20 % methylene blue dye degradation efficiency under visible light. The TIRC exhibits effective photocatalyst properties, making it a promising candidate for a self-sterilizing coated mask.</div></div>","PeriodicalId":49836,"journal":{"name":"Medical Engineering & Physics","volume":"141 ","pages":"Article 104360"},"PeriodicalIF":1.7,"publicationDate":"2025-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144168191","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":"Automated step analysis algorithm for CMAP scan study","authors":"N. Tuğrul Artuğ ,&nbsp;Nermin Gorkem Sirin ,&nbsp;Sezin Alpaydın Baslo ,&nbsp;Elif Kocasoy Orhan ,&nbsp;M. Baris Baslo ,&nbsp;A. Emre Oge","doi":"10.1016/j.medengphy.2025.104353","DOIUrl":"10.1016/j.medengphy.2025.104353","url":null,"abstract":"<div><h3>Objective</h3><div>To create an automated method for step analysis in compound muscle action potential (CMAP) scan curves and compare the step parameters calculated using the conventional semi-automated and the new automated method between patients with amyotrophic lateral sclerosis (ALS) and controls.</div></div><div><h3>Methods</h3><div>Twenty ALS patients and fifteen controls were enrolled into the study. Median nerve was stimulated at the wrist to record CMAP scans from abductor pollisis brevis muscle. Automated step analysis software revealed gaps on CMAP scan graphics by using new parameters indicating the steps. New parameters were calculated and compared with the conventional semi-automated step analysis. Intra-class correlation coefficients (ICC) were measured for reliability between methods.</div></div><div><h3>Results</h3><div>Step parameters calculated by two methods showed no significant difference in patients with ALS, except step%, but their similarities were less favorable in controls. The reliability of parameters between two methods was good-to-excellent in patient group, but controls did not show a significant ICC for any of the parameters.</div></div><div><h3>Conclusion</h3><div>A completely new automated step analysis software was developed. Analyses were done within 5 s. New step parameters were presented with supporting graphics. Results of automated step analysis were in concordance with semi-automated one for ALS patients, but not in controls.</div></div>","PeriodicalId":49836,"journal":{"name":"Medical Engineering & Physics","volume":"141 ","pages":"Article 104353"},"PeriodicalIF":1.7,"publicationDate":"2025-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144099460","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":"Performance and usability of an intraoperative registration paste frame for a spine surgical navigation system","authors":"Wen-Shuo Chang ,&nbsp;Tsung-Mu Wu ,&nbsp;Chung-Han Ho ,&nbsp;Chi-Ming Huang ,&nbsp;Chi-Sheng Chien ,&nbsp;Kin Weng Wong","doi":"10.1016/j.medengphy.2025.104358","DOIUrl":"10.1016/j.medengphy.2025.104358","url":null,"abstract":"<div><h3>Study design</h3><div>Experimental and observational study</div></div><div><h3>Objectives</h3><div>Despite the rapid development of various spine surgical navigation systems, they are not yet widespread. The possible explanation is that navigation accuracy is not guaranteed and user errors still occur. Therefore, we develop four different intraoperative registration paste frames and conducted simulated spine surgery in a hybrid room environment. Through performance and usability tests, we aim to evaluate the proper registration tool for spine surgical navigation system.</div></div><div><h3>Methods</h3><div>Five spine surgeons used the spine surgical navigation system and a robotic 3D C-arm to execute performance and usability tests for four different intraoperative registration paste frames. In performance test, we measured the registration and navigation accuracies. As for usability test, three observers recorded the user errors during operation and surgeons filled out a questionnaire after the procedure. Statistical analysis was performed to evaluate the test result between different paste frame designs.</div></div><div><h3>Results</h3><div>L-type intraoperative registration paste frame made by PEEK achieved small registration error (0.060 ± 0.014 mm) and small navigation error (1.30 ± 0.69 mm). The usability test showed that the L-type design not only reduced the operational risk, but also increased satisfaction.</div></div><div><h3>Conclusion</h3><div>Through the performance and usability tests, we identified the L-type intraoperative registration paste frame made by PEEK as a promising registration tool in spine surgical navigation system. Further study in the clinical setting with more case numbers is necessary to consolidate this finding.</div></div>","PeriodicalId":49836,"journal":{"name":"Medical Engineering & Physics","volume":"141 ","pages":"Article 104358"},"PeriodicalIF":1.7,"publicationDate":"2025-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144125260","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":"An explainable machine learning framework for predicting driving states using electroencephalogram","authors":"Iqram Hussain ,&nbsp;Se-Jin Park ,&nbsp;AKM Azad ,&nbsp;Salem Ali Alyami","doi":"10.1016/j.medengphy.2025.104355","DOIUrl":"10.1016/j.medengphy.2025.104355","url":null,"abstract":"<div><h3>Objectives</h3><div>Understanding drivers' cognitive load is essential for enhancing road safety, as cognitive demands fluctuate across different driving scenarios, potentially impacting performance, and safety, particularly for drivers with neurological disabilities. This study aims to predict driving states in healthy adult drivers using electroencephalogram (EEG) and machine learning (ML) models; and interpret the neural activity associated with each driving condition.</div></div><div><h3>Methods</h3><div>EEG data were collected from participants using a Cognionics Quick-20 EEG headset in Resting state, City-way, Highway, and Suburb-way driving states in 360 full-screen real car cabin inside the driving simulator. Participants drove while experiencing varied cognitive workloads due to various driving environments. EEG Features, including spectral band powers and power ratios, were extracted to extract neural activity patterns relevant to driving states. Gradient Boosting (GBoost) and Random Forest (RF) machine-learning classifiers were applied to classify the driving states based on EEG features. A model-agnostic explanation approach was implemented for model interpretability, which highlighted EEG spectral features contributing to each driving state.</div></div><div><h3>Results</h3><div>The GBoost model achieved the highest classification accuracy among tested models, with ROC-AUC values of 1.00 for Resting, 0.96 for Suburb-Way, 0.91 for Highway, and 0.90 for City-Way states, effectively distinguishing driving states using EEG features. Model agnostic feature attribution approach highlighted key EEG contributors, such as power in Theta and Delta bands, and power ratios (DAR, DTR), providing interpretability and aligning with established neurological indicators of cognitive workload.</div></div><div><h3>Conclusion</h3><div>This study demonstrates the potential of EEG-based features combined with interpretable machine learning for driver states prediction. The approach offers a foundation for personalized, responsive driving assistance systems at improving road safety and preventing disability by monitoring to drivers' cognitive states.</div></div>","PeriodicalId":49836,"journal":{"name":"Medical Engineering & Physics","volume":"140 ","pages":"Article 104355"},"PeriodicalIF":1.7,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143946530","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":"Verifying the accuracy and precision of foot bone segmentation from fan beam and cone beam computed tomography scans","authors":"Eric D. Thorhauer ,&nbsp;Corey Wukelic ,&nbsp;Nicholas Entress ,&nbsp;Jane B. Shofer ,&nbsp;William R. Ledoux","doi":"10.1016/j.medengphy.2025.104349","DOIUrl":"10.1016/j.medengphy.2025.104349","url":null,"abstract":"<div><div>Accurate segmentation of foot and ankle bones from computed tomography scans is an essential precursor to morphological comparison studies and model-based biplanar fluoroscopic tracking. Errors in modeling the bones may propagate into coordinate system definitions or affect arthrokinematic calculations. Cone beam computed tomography scanners with sensitive detectors offer a lower dose alternative to fan beam scanners, although with signal-to-noise ratio and tissue contrast tradeoffs. The primary aims of this study were to validate the accuracy of foot bone models (distal tibia, talus, calcaneus, navicular, first metatarsal, and proximal phalanx) generated using established semi-automatic segmentation techniques relative to ground truth laser scans of the dissected bone specimens and to compare the errors in models segmented from a fan beam scanner to those from a cone beam scanner. We found excellent between and within-operator intra-class correlation scores for segmentations (all &gt; 0.90). Voxel-based metrics revealed small but significant differences between scanner types when comparing the Dice score and precision of segmentations, with cone beam having higher scores for nearly every metric type. Surface-based error data also revealed cone beam scans to be more accurate to the actual bone morphology despite having noisier images. The small isotropic voxels of the cone beam scanner produced bone models with errors averaging 0.43 ± 0.11 mm compared to the fan beam average of 0.74 ± 0.15 mm error across all bones. The findings suggest that voxel resolution is more critical than scanner type for modeling foot bones and that these segmentation techniques may overcome the limitations in cone beam scanners. These outcomes support the growing movement towards more widespread adoption of low-dose cone beam weight-bearing computed tomography imaging for 3D modeling of foot and ankle bones for clinical and research purposes.</div></div>","PeriodicalId":49836,"journal":{"name":"Medical Engineering & Physics","volume":"140 ","pages":"Article 104349"},"PeriodicalIF":1.7,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143937554","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":"Utilizing machine learning algorithms for cardiovascular disease prediction: “Detailed analysis based on medical parameters”","authors":"Mahesh Kumar Joshi ,&nbsp;Deepak Dembla ,&nbsp;Suman Bhatia","doi":"10.1016/j.medengphy.2025.104347","DOIUrl":"10.1016/j.medengphy.2025.104347","url":null,"abstract":"<div><div>Among the most prevalent and dangerous ailments impacting human health are cardiovascular diseases (CVDs). Early diagnosis may help avoid or lessen CVDs, thereby lowering death rates. Several clinical methods have already been deployed for diagnosing and treating CVD. However, one interesting approach is to use Machine Learning (ML) approaches to identify risk characteristics. The suggested model uses a variety of ML approaches to accurately forecast cardiac disease. Initially, the CVD dataset is collected and trained in the Python tool. The null and duplicate records are removed in the data preprocessing stage. Moreover, extracts relevant information from the dataset using feature extraction. Inter Quartile Range (IQR) is used in AdaBoost and Gradient Boosting to identify continuously distributed outliers in data. Moreover, 16 ML classifiers are employed to accurately forecast the CVD disease. Compared with other approaches, the AdaBoost and Gradient Boosting approach gained better results of 96 %. The developed model dataset is trained and tested with k-fold testing. GridSearchCV and the results are visualized using the SHAP tool. The designed technique enhances the CVD prediction system using several MLs.</div></div>","PeriodicalId":49836,"journal":{"name":"Medical Engineering & Physics","volume":"140 ","pages":"Article 104347"},"PeriodicalIF":1.7,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144069177","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":"3-D contour-aware U-Net for efficient rectal tumor segmentation in magnetic resonance imaging","authors":"Yi Lu ,&nbsp;Jun Dang ,&nbsp;Junzhang Chen ,&nbsp;Yuanyuan Wang ,&nbsp;Tao Zhang ,&nbsp;Xiangzhi Bai","doi":"10.1016/j.medengphy.2025.104352","DOIUrl":"10.1016/j.medengphy.2025.104352","url":null,"abstract":"<div><div>Magnetic resonance imaging (MRI), as a non-invasive detection method, is crucial for the clinical diagnosis and treatment plan of rectal cancer. However, due to the low contrast of rectal tumor signal in MRI, segmentation is often inaccurate. In this paper, we propose a new three-dimensional rectal tumor segmentation method CAU-Net based on T2-weighted MRI images. The method adopts a convolutional neural network to extract multi-scale features from MRI images and uses a Contour-Aware decoder and attention fusion block (AFB) for contour enhancement. We also introduce adversarial constraint to improve augmentation performance. Furthermore, we construct a dataset of 108 MRI-T2 volumes for the segmentation of locally advanced rectal cancer. Finally, CAU-Net achieved a DSC of 0.7112 and an ASD of 2.4707, which outperforms other state-of-the-art methods. Various experiments on this dataset show that CAU-Net has high accuracy and efficiency in rectal tumor segmentation. In summary, proposed method has important clinical application value and can provide important support for medical image analysis and clinical treatment of rectal cancer. With further development and application, this method has the potential to improve the accuracy of rectal cancer diagnosis and treatment.</div></div>","PeriodicalId":49836,"journal":{"name":"Medical Engineering & Physics","volume":"140 ","pages":"Article 104352"},"PeriodicalIF":1.7,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143894920","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":"Efficient digitally reconstructed radiographs with middle-out rendering and convex hull-based early stopping","authors":"William Burton,&nbsp;Casey Myers ,&nbsp;Chadd Clary ,&nbsp;Paul Rullkoetter","doi":"10.1016/j.medengphy.2025.104351","DOIUrl":"10.1016/j.medengphy.2025.104351","url":null,"abstract":"<div><div>Kinematic tracking of native anatomy in dynamic radiography facilitates understanding of <em>in vivo</em> movement. Tracking is performed using model-image registration, which involves estimating 6 degree-of-freedom poses of anatomic structures by comparing captured radiographs to dynamically rendered images of digital models. This procedure can produce accurate pose estimates of native anatomy, but computational efficiency remains a concern. A key source of latency in model-image registration is the iterative rendering of digitally reconstructed radiographs, required for evaluation of candidate poses. This technical note introduces an efficient algorithm for rendering digitally reconstructed radiographs. The proposed method is shown to accelerate rendering speeds by up to 50% compared to a baseline method which is commonly used in registration frameworks. Efficient rendering of digitally reconstructed radiographs may enhance the overall viability of model-image registration for use in kinematic tracking, and may contribute to bringing this technology closer to adoption in clinical settings.</div></div>","PeriodicalId":49836,"journal":{"name":"Medical Engineering & Physics","volume":"140 ","pages":"Article 104351"},"PeriodicalIF":1.7,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143894805","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":"Hemodynamic insight into the iliac vein after stent intervention: Mind the persistent compression of iliac vein stent","authors":"Jian Lu ,&nbsp;Zhenmin Fan ,&nbsp;Hao Cheng ,&nbsp;Kailei Liu ,&nbsp;Pengfei Zhao ,&nbsp;Xia Ye ,&nbsp;Xiaoyan Deng ,&nbsp;Mingyuan Liu","doi":"10.1016/j.medengphy.2025.104344","DOIUrl":"10.1016/j.medengphy.2025.104344","url":null,"abstract":"<div><div>This study hypothesizes that lumbar spine and iliac artery compression of veins persists after stenting, rendering the iliac vein susceptible to restenosis, thrombosis, and other clinical complications. To examine this hypothesis, a vascular model post-iliac vein stenting was constructed based on vein images from patients with iliac vein compression syndrome. This model facilitated the examination of how varying degrees of compression affect the hemodynamic environment of the host vasculature post-stenting. Results indicated that while compression increased blood flow velocity near the affected area, it also led to a more disturbed blood flow environment within the stent segment, at bifurcation points, and in the contralateral iliac vein, as evidenced by elevated Oscillatory Shear Index (OSI) and Relative Residence Time (RRT) values. Furthermore, the study observed that while the implantation of long stents minimally impacted the blood vessels within the stent segment, it did marginally mitigate adverse blood flow conditions at intersections under compression. Consequently, the ongoing adverse effects of compression on the flow environment post-stenting, potentially linked to suboptimal postoperative outcomes at these sites, should be carefully considered.</div></div>","PeriodicalId":49836,"journal":{"name":"Medical Engineering & Physics","volume":"140 ","pages":"Article 104344"},"PeriodicalIF":1.7,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143924937","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}