Irbm最新文献

{"title":"A Novel QCT-Based Deep Transfer Learning Approach for Predicting Stiffness Tensor of Trabecular Bone Cubes","authors":"Pengwei Xiao ,&nbsp;Tinghe Zhang ,&nbsp;Yufei Huang ,&nbsp;Xiaodu Wang","doi":"10.1016/j.irbm.2024.100831","DOIUrl":"10.1016/j.irbm.2024.100831","url":null,"abstract":"<div><h3>Objectives</h3><p>This study was performed to prove the concept that transfer learning techniques, assisted with a generative model, could be used to alleviate the ‘big data’ requirement for training high-fidelity deep learning (DL) models in prediction of stiffness tensor of trabecular bone cubes.</p></div><div><h3>Material and methods</h3><p>Transfer learning approaches of domain adaptation were used, in which a source domain included 1,641 digital trabecular bone cubes synthesized from a generative model, and a target domain included 868 real trabecular bone cubes from human cadaver femurs. Simulated quantitative computed tomography (QCT) images of both the synthesized and real bone cubes were used as input, whereas the stiffness tensor of these cubes determined using finite element simulations were used as output. Three transfer learning algorithms, including instance-based (TrAdaBoostR2 and WANN) and parameter-based (RNN) methods, were used. Two case studies, one with varying sizes of training dataset and the other with a gender-biased training dataset, were performed to evaluate these deep transfer learning models in comparison with a base deep learning (DL) model trained using the dataset from the target domain.</p></div><div><h3>Results</h3><p>The results indicated that these deep transfer learning models were robust both to sample size and to the gender-biased training dataset, whereas the base DL model was very sensitive to such changes. Among the three transfer learning algorithms, the prediction accuracy of the RNN-based deep transfer learning model was the best (0.92-0.96%) and comparable to that of the base DL model trained using the dataset from the target domain.</p></div><div><h3>Conclusion</h3><p>This study proved the proposed concept and confirmed that high fidelity QCT-based deep learning models could be obtained for prediction of stiffness tensor of trabecular bone cubes.</p></div>","PeriodicalId":14605,"journal":{"name":"Irbm","volume":"45 2","pages":"Article 100831"},"PeriodicalIF":4.8,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140166332","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":"High-Order Temporal Convolutional Network for Improving Classification Performance of SSVEP-EEG","authors":"Jianli Yang,&nbsp;Songlei Zhao,&nbsp;Wei Zhang,&nbsp;Xiuling Liu","doi":"10.1016/j.irbm.2024.100830","DOIUrl":"10.1016/j.irbm.2024.100830","url":null,"abstract":"<div><h3>Background and objective</h3><p>Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) aim to detect target frequencies corresponding to specific commands in electroencephalographic (EEG) signals by classification algorithms to achieve the desired control. However, SSVEP signals suffer from low signal-to-noise ratio and large differences in brain activity. Moreover, the existing CNN models have small receptive fields, which make it difficult to receive large range of feature information and limit the effectiveness of classification algorithms.</p></div><div><h3>Methods</h3><p>To this end, we proposed a high-order temporal convolutional neural network (HOT-CNN) model for enhancing the performance of SSVEP target recognition. Specifically, the SSVEP-EEG signals was divided into equal-length time segments and a time-slice attention module was designed to capture the correlation between time slices. The module improves the local characterization of signals and reduces biological noise interference by automatically assigning high weights to locally relevant temporal sampling cues and lower weights to other temporal cues. Moreover, for global features, a temporal convolutional network module was designed to increases the receptive field of the network and to extract more comprehensive time domain features by using dilated causal convolution. Finally, the fusion and analysis of local and global features are achieved by designing a feature fusion and classification module to accomplish accurate classification of SSVEP signals.</p></div><div><h3>Results</h3><p>Our method was evaluated on large publicly available datasets containing 35 subjects and 40 categories. Experimental results indicated that HOT-CNN achieved encouraging performance compared with other advanced methods: the highest information transfer rate of 241.01bits/min was obtained using 0.5s stimuli, and the highest average accuracy of 96.39% was obtained using 1.0s stimuli.</p></div><div><h3>Conclusions</h3><p>The method effectively reinforced the global and local time-domain information and improved the classification performance of SSVEP, which has wide application prospects.</p></div>","PeriodicalId":14605,"journal":{"name":"Irbm","volume":"45 2","pages":"Article 100830"},"PeriodicalIF":4.8,"publicationDate":"2024-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140106860","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":"Blockchain-Based Trusted Tracking Smart Sensing Network to Prevent the Spread of Infectious Diseases","authors":"Riaz Ullah Khan ,&nbsp;Rajesh Kumar ,&nbsp;Amin Ul Haq ,&nbsp;Inayat Khan ,&nbsp;Mohammad Shabaz ,&nbsp;Faheem Khan","doi":"10.1016/j.irbm.2024.100829","DOIUrl":"10.1016/j.irbm.2024.100829","url":null,"abstract":"<div><h3>Background</h3><p>Infectious diseases like COVID-19 pose major global health threats. Robust surveillance systems are needed to swiftly detect and contain outbreaks. This study investigates the integration of Blockchain technology and machine learning to establish a secure and ethically sound approach to tracking infectious diseases.</p></div><div><h3>Methods</h3><p>We established a Blockchain-based framework for the collection and analysis of epidemiological data while upholding privacy standards. We employed encryption and privacy-enhancing technologies to gather information on case numbers, locations, and disease progression. Artificial neural networks were employed to scrutinize the data and pinpoint transmission patterns. A prototype was specifically designed to work with COVID-19 data from specific countries.</p></div><div><h3>Results</h3><p>The Blockchain system enabled reliable and tamper-proof data gathering with enhanced transparency. The evaluation showed it allowed cost-effective tracking of infectious diseases while upholding confidentiality safeguards. The neural networks effectively modeled disease spread based on the Blockchain data.</p></div><div><h3>Conclusions</h3><p>This research demonstrates the viability of Blockchain and machine learning for infectious disease surveillance. The system strikes a balance between public health concerns and personal privacy considerations. It also addresses the challenges of misinformation and accountability gaps during disease outbreaks. Ongoing development can lay the foundation for an ethical framework for digital disease tracking, ensuring both pandemic preparedness and response capabilities are upheld.</p></div>","PeriodicalId":14605,"journal":{"name":"Irbm","volume":"45 2","pages":"Article 100829"},"PeriodicalIF":4.8,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139813901","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":"Ultrasound Applications in Ophthalmology: A Review","authors":"Sylvain Poinard ,&nbsp;Alice Ganeau ,&nbsp;Maxime Lafond ,&nbsp;Oliver Dorado ,&nbsp;Stefan Catheline ,&nbsp;Cyril Lafon ,&nbsp;Florent Aptel ,&nbsp;Gilles Thuret ,&nbsp;Philippe Gain","doi":"10.1016/j.irbm.2024.100828","DOIUrl":"10.1016/j.irbm.2024.100828","url":null,"abstract":"<div><p>Ultrasound is a powerful tool in ophthalmology with a wide range of physical effects that can interact with biological tissue. This ranges from low-intensity linear transducers for diagnosis to high-intensity pulsed or continuous focused ultrasound for therapy. Designing devices for ophthalmological applications requires creating fine focal spots, minimizing heating, and accounting for eye movements. Ultrasound is essential for ophthalmologists to provide accurate diagnosis and quantitative information on tissue composition and blood flow. Ultrasound has revolutionized cataract surgery, making it less invasive and in an outpatient basis, while enhancing the safety and predictability of glaucoma treatment using high-intensity focused ultrasound. The article aims to review the complex and multifaceted bioeffects of ultrasound used in ophthalmology, and its current and future applications of ultrasound in ophthalmology, notably regarding cavitation-mediated drug delivery.</p></div>","PeriodicalId":14605,"journal":{"name":"Irbm","volume":"45 2","pages":"Article 100828"},"PeriodicalIF":4.8,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139828638","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":"Acknowledging our reviewers","authors":"","doi":"10.1016/S1959-0318(24)00006-X","DOIUrl":"https://doi.org/10.1016/S1959-0318(24)00006-X","url":null,"abstract":"","PeriodicalId":14605,"journal":{"name":"Irbm","volume":"45 1","pages":"Article 100825"},"PeriodicalIF":4.8,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S195903182400006X/pdfft?md5=7b99a7407da8ee505b1e0120628c3e03&pid=1-s2.0-S195903182400006X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139719191","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Stylohyoid and Posterior Digastric Recruitment Pattern Evaluation in Swallowing and Non-swallowing Tasks","authors":"Adrien Mialland ,&nbsp;Ihab Atallah ,&nbsp;Agnès Bonvilain","doi":"10.1016/j.irbm.2024.100823","DOIUrl":"10.1016/j.irbm.2024.100823","url":null,"abstract":"<div><h3>Objectives</h3><p>Electromyography is one of the few measurement methods that can be implanted, and it has been used in swallowing detection to measure superficial muscles, but has failed to provide satisfactory performances for a real-time detection. Yet, we seek to allow for the feasibility of an implantable active artificial larynx that would protect the airway during swallowing. Therefore, it requires a real-time detection of swallowing through measurements that must provide dedicated and early activity on swallowing, to close the airways soon as possible. In that regard, promising results were published about the stylohyoid and posterior digastric muscles, but no study provided simultaneous and independent measurements. So, this paper aims to evaluate both muscles with intra muscular EMG, in a large set of tasks, to evaluate their recruitment pattern for the feasibility of an implantable active artificial larynx.</p></div><div><h3>Materials and methods</h3><p>we used intramuscular EMG to measure the stylohyoid and the posterior digastric muscles independently. We also used surface electrodes to measure the submental muscles and provide a basis for comparison. Besides, the swallowing sound measurement method was used to locate the moment the bolus starts to enter the upper esophageal sphincter (UES). That moment defines a temporal limit after which the airway are in danger of aspiration and the temporal evolution of the muscles' is evaluated in comparison to that limit. The onsets and offsets of each muscles were located with a generalized likelihood ratio method, and the UES bolus passage was localized manually after the transformation of the signals with a Teager-Kaiser energy operator. 17 participants were measured, and were asked to perform 4 swallowing tasks and 13 non-swallowing tasks.</p></div><div><h3>Results</h3><p>we found a strong implication of the stylohyoid for swallowing and mastication. The posterior digastric showed a clear tendency towards swallow-related tasks, and especially swallowing, mastication, open mouth, jaw, and clench teeth. Both muscles provided significant activity before the temporal limit, with a characteristic pattern.</p></div><div><h3>Conclusion</h3><p>the stylohyoid and the posterior digastric muscles shows a net increase in potential for a detection, compared to the submental muscles, for the feasibility of an implantable active artificial larynx.</p></div>","PeriodicalId":14605,"journal":{"name":"Irbm","volume":"45 2","pages":"Article 100823"},"PeriodicalIF":4.8,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139827724","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 Estimation of Elbow Flexion Angle from IMU Measurements Using Anatomical Constraints","authors":"Anna Bicchi,&nbsp;Alessandro Colombo","doi":"10.1016/j.irbm.2024.100820","DOIUrl":"10.1016/j.irbm.2024.100820","url":null,"abstract":"<div><h3>Objectives</h3><p>Inertial Measurement Units (IMUs) are a valid alternative to optical tracking systems for human motion capture, but they are subject to several disturbances that limit their accuracy. We aim to improve the accuracy of elbow joint angle estimation from IMU measurements by introducing a novel postprocessing algorithm that uses anatomical constraints and does not require any prior calibration or knowledge of anthropometric parameters.</p></div><div><h3>Materials and Methods</h3><p>We propose a new error model that addresses sensor misalignment and fusion errors. We use an error state extended Kalman filter (ESEKF) with state constraints to integrate the anatomical constraints. We validate the proposed algorithm by testing it in different scenarios and comparing it with a state-of-the-art optical tracking system.</p></div><div><h3>Results</h3><p>The research results highlight the superior performance of the proposed method compared with existing techniques. The study demonstrates a significant reduction in errors, particularly in complex arm movements and under strong external disturbances. The results obtained in the three different tested scenarios underscore the robustness and effectiveness of the developed algorithm, reaching half the error committed by the existing calibration-free correction algorithms proposed in the literature.</p></div><div><h3>Conclusions</h3><p>The developed technique provides highly accurate estimates of joint angles in several challenging real-world scenarios.</p></div>","PeriodicalId":14605,"journal":{"name":"Irbm","volume":"45 1","pages":"Article 100820"},"PeriodicalIF":4.8,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1959031824000010/pdfft?md5=4603b04f7a7d6f3268a7480f4b5a8476&pid=1-s2.0-S1959031824000010-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139482135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparing Two Bootstrapped Regions in Images: The D-Test","authors":"Florentin Kucharczak ,&nbsp;Inés Couso ,&nbsp;Olivier Strauss ,&nbsp;Denis Mariano-Goulart","doi":"10.1016/j.irbm.2024.100821","DOIUrl":"10.1016/j.irbm.2024.100821","url":null,"abstract":"<div><h3>Objectives</h3><p>Many molecular imaging diagnoses involve comparing two regions of interest (ROIs) in the image or different images. Since the images are obtained by measuring a random phenomenon, such comparisons should be based on a statistical test to ensure reliability. Recent studies have shown that use of the bootstrap approach provides access to the statistical variability of reconstructed values in molecular images. However, although there is general agreement that this increase in information should make diagnosis based on molecular images more reliable, no approach has been proposed in the relevant literature to use bootstrap replicates to enhance the reliability of comparisons of two ROIs. In this paper, we propose to fill this gap by introducing the first statistical test that allows us to compare two sets of pixels/voxels for which bootstrap replicates are available.</p></div><div><h3>Material and methods</h3><p>After presenting the theoretical basis of this non-parametric statistical test, this article describes how to calculate it in practice. Finally, it proposes two experiments based on quantitative comparisons and expert judgment to assess its relevance.</p></div><div><h3>Results</h3><p>The results obtained are consistent with expert diagnosis on synthetic data. This validates the relevance of the D-test.</p></div><div><h3>Conclusion</h3><p>This paper presents the first statistical test to compare two ROIs in reconstructed images for which the statistical variability information is accessible.</p></div>","PeriodicalId":14605,"journal":{"name":"Irbm","volume":"45 1","pages":"Article 100821"},"PeriodicalIF":4.8,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1959031824000022/pdfft?md5=8064e78cbcbc3824ad2b83a752908c7b&pid=1-s2.0-S1959031824000022-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139499353","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"2D-3D Reconstruction of a Femur by Single X-Ray Image Based on Deep Transfer Learning Network","authors":"Ho-Gun Ha ,&nbsp;Jinhan Lee ,&nbsp;Gu-Hee Jung ,&nbsp;Jaesung Hong ,&nbsp;HyunKi Lee","doi":"10.1016/j.irbm.2024.100822","DOIUrl":"10.1016/j.irbm.2024.100822","url":null,"abstract":"<div><h3>Objective</h3><p>Constructing a 3D model from its 2D images, known as 2D-3D reconstruction, is a challenging task. Conventionally, a parametric 3D model such as a statistical shape model (SSM) is deformed by matching the shapes in its 2D images through a series of processes, including calibration, 2D-3D registration, and optimization for nonrigid deformation. To overcome this complicated procedure, a streamlined 2D-3D reconstruction using a single X-ray image is developed in this study.</p></div><div><h3>Methods</h3><p>We propose 2D-3D reconstruction of a femur by adopting a deep neural network, where the deformation parameters in the SSM determining the 3D shape of the femur are predicted from a single X-ray image using a deep transfer-learning network. For learning the network from distinct features representing the 3D shape information in the X-ray image, a specific proximal part of the femur from a unique X-ray pose that allows accurate prediction of the 3D femur shape is designated and used to train the network. Then, the corresponding proximal/distal 3D femur model is reconstructed from only the single X-ray image acquired at the designated position.</p></div><div><h3>Results</h3><p><span>Experiments were conducted using actual X-ray images of a femur phantom and X-ray images of a patient's femur derived from computed tomography to verify the proposed method. The average errors of the reconstructed 3D shape of the proximal and </span>distal femurs from the proposed method were 1.20 mm and 1.08 mm in terms of root mean squared point-to-surface distance, respectively.</p></div><div><h3>Conclusion</h3><p>The proposed method presents an innovative approach to simplifying the 2D-3D reconstruction using deep neural networks that exhibits performance compatible with the existing methodologies.</p></div>","PeriodicalId":14605,"journal":{"name":"Irbm","volume":"45 1","pages":"Article 100822"},"PeriodicalIF":4.8,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139498895","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":"Current and Future Role of HIFU in Obstetric Gynaecology","authors":"Benoit De La Fourniere ,&nbsp;Manon Basso ,&nbsp;Morgane Dairien ,&nbsp;Cyril Huissoud ,&nbsp;Cyril Lafon ,&nbsp;Gil Dubernard ,&nbsp;Marion Cortet ,&nbsp;David Melodelima ,&nbsp;Charles-André Philip","doi":"10.1016/j.irbm.2023.100819","DOIUrl":"10.1016/j.irbm.2023.100819","url":null,"abstract":"<div><p><span>Obstetric </span>gynaecology<span>, as a field in which diagnostic ultrasound has quickly found its place, especially in screening for birth defects and monitoring pregnancies, is also a speciality in which therapeutic ultrasound is used extensively.</span></p><p><span>In pelvic gynaecology, HIFU therapy is used more specifically in two types of uterine conditions: fibroids and </span>adenomyosis. In both cases, studies have shown significant efficacy in reducing pain and bleeding associated with the conditions, secondarily (more moderately but still significantly) reducing the volume of the lesions. Impact on fertility has yet to be demonstrated.</p><p><span>In rectosigmoid endometriosis<span>, clinical data indicates good treatment feasibility and significant efficacy on digestive and gynaecologic </span></span>pain symptoms<span>, as well as on quality of life, with no associated severe complications. Should the efficacy of HIFU in treating endometriosis be confirmed over time, it could revolutionise the management of digestive endometriosis by offering a valid minimally invasive alternative to rectosigmoid surgery.</span></p><p>In senology<span>, where visible scars have a particularly significant psychological impact, several teams have been researching the use of HIFU for the destruction of some types of breast lesions (fibroadenomas and breast tumours).</span></p><p><span>In obstetrics, HIFU could become a treatment of choice for vascular anomalies such as twin-to-twin transfusion syndrome in twin pregnancies. Promising studies are also available regarding the use of HIFU in the treatment of post-partum </span>placenta accreta.</p></div>","PeriodicalId":14605,"journal":{"name":"Irbm","volume":"45 1","pages":"Article 100819"},"PeriodicalIF":4.8,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139410106","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}