Irbm最新文献

{"title":"Contrast-Free High Frame Rate Ultrasound Imaging for Assessment of Vascular Remodeling During Wound Healing","authors":"David Lemonnier ,&nbsp;Ikram Mezghani ,&nbsp;Georgios Theocharidis ,&nbsp;Brandon J. Sumpio ,&nbsp;Samuel K. Sia ,&nbsp;Aristidis Veves ,&nbsp;Parag V. Chitnis","doi":"10.1016/j.irbm.2023.100818","DOIUrl":"10.1016/j.irbm.2023.100818","url":null,"abstract":"<div><h3>Background</h3><p>Monitoring of wound healing progression is critical due to the risk of infection, non-healing wounds, or evolution towards a chronic state. Tissue vasculature is one of the most representative features reflecting healing status. This study explores the feasibility of vascular ultrasound imaging of open wounds and the extraction of vascular-related features in a longitudinal study.</p></div><div><h3>Material and methods</h3><p>C57BL/6 mice received a 1 cm-diameter full-thickness wound on their dorsum and were imaged using ultrasound from the surgical day (Day 0) to 25 days post-wounding. The high frame rate, plane waves acquisitions with a 15 MHz transducer were postprocessed with Singular Value Decomposition (SVD) filtering to provide vascular information.</p></div><div><h3>Results</h3><p>Vascularity Index (VI) calculations showed an increased vascular signal in the wound from Day 2 post-wounding and were significantly higher from day 6 to day 10 post-wounding compared to Day 0 (p&lt;0.05). VI values were back to the basal level after 3 weeks. In comparison, no significant difference was highlighted for the vascular signal in the peri-wound area.</p></div><div><h3>Conclusions</h3><p>These results show that vascular ultrasound imaging can be applied to track vascular changes of open wounds during the healing process. This approach may also be extended to other types of wounds for detecting early signs likely to cause complications.</p></div>","PeriodicalId":14605,"journal":{"name":"Irbm","volume":"45 1","pages":"Article 100818"},"PeriodicalIF":4.8,"publicationDate":"2023-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1959031823000672/pdfft?md5=a990e9fa0f9e6fcd12e5b1ae999b89ef&pid=1-s2.0-S1959031823000672-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139051904","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":"Ultrasound Imaging for Wall Shear Stress Measurements","authors":"Dong Chan Park ,&nbsp;Dae Woo Park ,&nbsp;Dae Woo Park","doi":"10.1016/j.irbm.2023.100817","DOIUrl":"https://doi.org/10.1016/j.irbm.2023.100817","url":null,"abstract":"<div><p><strong>Background</strong><span><span>: Wall shear stress<span><span> (WSS) plays an indispensable role in shaping the trajectory of vascular diseases such as atherosclerosis and aneurysms. Specific patterns of low and oscillating WSS are implicated in the promotion of plaque accumulation, whereas elevated WSS levels are associated with inflammatory responses, the synthesis of </span>metalloproteases<span>, and eventual rupture of plaque. Therefore, an accurate, noninvasive quantification of local hemodynamics and WSS is integral to the precise diagnosis of vascular disorders. </span></span></span>Ultrasound imaging<span> has emerged as a favored modality for measuring the WSS owing to its noninvasive nature, ease of access, and user-friendly interface. However, existing reviews primarily focus on the assessment of blood flow characteristics, including velocity profiles and volume flow rates. To the best of our knowledge, thus far, no review has been dedicated to ultrasound imaging techniques for the measurement of </span></span><em>in vivo</em> WSS.</p><p><strong>Purpose</strong>: This study aimed to perform a thorough overview of current and emerging ultrasound imaging methodologies tailored for <em>in vivo</em> WSS quantification.</p><p><strong>Basic procedure</strong><span>: The fundamental principles of WSS measurements were explored, and various techniques—-Doppler ultrasound imaging, ultrasound imaging velocimetry, and speckle decorrelation—-that are employed for WSS assessment were studied.</span></p><p><strong>Main findings</strong><span>: These techniques show promise for clinical applications by facilitating noninvasive and accurate WSS measurements of vital parameters concerning vascular physiology. Further investigations are warranted to overcome specific challenges, such as the accurate detection of vascular wall boundaries.</span></p><p><strong>Conclusions</strong>: The findings of this review are anticipated to contribute to advancements in ultrasound imaging techniques for <em>in vivo</em> WSS measurements.</p></div>","PeriodicalId":14605,"journal":{"name":"Irbm","volume":"45 1","pages":"Article 100817"},"PeriodicalIF":4.8,"publicationDate":"2023-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138454006","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 Autonomous Lumen Segmentation in Intravascular Optical Coherence Tomography Images: Unveiling the Potential of Polynomial-Regression Convolutional Neural Network","authors":"Yu Shi Lau ,&nbsp;Li Kuo Tan ,&nbsp;Kok Han Chee ,&nbsp;Chow Khuen Chan ,&nbsp;Yih Miin Liew","doi":"10.1016/j.irbm.2023.100814","DOIUrl":"https://doi.org/10.1016/j.irbm.2023.100814","url":null,"abstract":"<div><h3>Objectives</h3><p><span>Intravascular optical coherence tomography (IVOCT) is a crucial micro-resolution </span>imaging modality<span> used to assess the internal structure of blood vessels. Lumen segmentation in IVOCT images is vital for measuring the location and the extent of vessel blockages and for guiding percutaneous coronary intervention. Obtaining such information in real-time is essential, necessitating the use of fast automated algorithms. In this paper, we proposed an innovative polynomial-regression convolutional neural network (CNN) for fast and automated IVOCT lumen segmentation.</span></p></div><div><h3>Materials and methods</h3><p>The polynomial-regression CNN architecture was uniquely crafted to enable single-pass extraction of lumen borders via IVOCT image regression, ensuring real-time processing efficiency without compromising accuracy. The architecture designed convolution for regression while omitting fully connected layers, leading to the spatial output of lumen representation as polynomial coefficients, thus enabling the formation of interconnected lumen points. The approach equipped the network to comprehend the intricate and continuous geometries and curvatures intrinsic to blood vessels in transverse and longitudinal dimensions. The network was trained on a dataset of 16,165 images and evaluated using 7,016 images.</p></div><div><h3>Results</h3><p>The predicted segmentations exhibited a distance error of less than 2 pixels (26.40 μm), Dice's coefficient of 0.982, Jaccard Index of 0.966, sensitivity of 0.980, specificity of 0.999, and a prediction time of 4 s (for a pullback containing 360 images). This technique demonstrated significantly improved performance in both accuracy and speed compared to published techniques.</p></div><div><h3>Conclusion</h3><p>The strong segmentation performance, fast speed, and robustness to image variations highlight the practical clinical utility of the proposed polynomial-regression network.</p></div>","PeriodicalId":14605,"journal":{"name":"Irbm","volume":"45 1","pages":"Article 100814"},"PeriodicalIF":4.8,"publicationDate":"2023-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138466625","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":"One-Dimensional Convolutional Multi-branch Fusion Network for EEG-Based Motor Imagery Classification","authors":"Xiaoguang Liu ,&nbsp;Mingjin Zhang ,&nbsp;Shicheng Xiong ,&nbsp;Xiaodong Wang ,&nbsp;Tie Liang ,&nbsp;Jun Li ,&nbsp;Peng Xiong ,&nbsp;Hongrui Wang ,&nbsp;Xiuling Liu","doi":"10.1016/j.irbm.2023.100812","DOIUrl":"10.1016/j.irbm.2023.100812","url":null,"abstract":"<div><p>The Brain-Computer Interface (BCI) system based on motor imagery (MI) is a hot research topic nowadays, which can control external devices through the brain and has a wide range of applications in rehabilitation, gaming, and entertainment. Due to the non-smooth, non-linear, and low signal-to-noise ratio of the MI EEG signal, it is challenging to accurately decode the MI task intention. A new end-to-end deep learning method is proposed to decode raw MI EEG signals without preprocessing, such as filtering and feature reinforcement. The 1D convolution is used to learn the time-frequency features in MI signals, and a four-branch fusion network is used as the main body to add a 1D CNN-AE block and 1D SE-block to enhance the algorithm's performance. Experiments on two publicly available datasets demonstrate that our proposed algorithm outperforms the current state-of-the-art methods. It achieves 86.11% and 89.51% on the BCI Competition IV-2a and the BCI Competition IV-2b datasets, respectively, and a 6.9% improvement in the generalizability test. The proposed data enhancement method can effectively alleviate the overfitting of the algorithm and improve the decoding performance. Further analysis shows that 1D convolution can effectively extract the features associated with the MI task.</p></div>","PeriodicalId":14605,"journal":{"name":"Irbm","volume":"44 6","pages":"Article 100812"},"PeriodicalIF":4.8,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135764371","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":"Estimating Fluid Intake Volume Using a Novel Vision-Based Approach","authors":"Rachel Cohen ,&nbsp;Geoff Fernie ,&nbsp;Atena Roshan Fekr","doi":"10.1016/j.irbm.2023.100813","DOIUrl":"https://doi.org/10.1016/j.irbm.2023.100813","url":null,"abstract":"<div><h3>Introduction</h3><p>Staying hydrated is an essential aspect of good health for people of all ages. Tracking fluid intake is important to ensure proper hydration and prompt users to drink as needed. Previous literature has attempted to measure the amount of fluid consumption, often using wearables or sensors embedded in containers.</p></div><div><h3>Objective</h3><p>In this paper, we introduce a novel vision-based method to estimate the amount of fluid consumed.</p></div><div><h3>Methods</h3><p>We trained different 3D Convolutional Neural Networks on data from 8 participants drinking from multiple containers and engaging in other activities in a simulated home environment.</p></div><div><h3>Results</h3><p>We show that it is possible to perform both drinking detection and volume intake estimation in a single algorithm with a Mean Absolute Percent Error (MAPE) of 28.5% and a Mean Percent Error (MPE) of 2.6% with 10-Fold and a MAPE of 42.4% and MPE of 25.4% for Leave-One-Subject-Out cross validation.</p></div><div><h3>Conclusion</h3><p>This shows that using video inputs does have the potential to detect and estimate the amount of fluid consumed throughout the day.</p></div>","PeriodicalId":14605,"journal":{"name":"Irbm","volume":"44 6","pages":"Article 100813"},"PeriodicalIF":4.8,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1959031823000623/pdfft?md5=da6e7f5e4e01e8004b72ca21df8c9f5d&pid=1-s2.0-S1959031823000623-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134657063","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":"A Wearable Monitoring Device for COVID-19 Biometric Symptoms Detection","authors":"Claudino Costa ,&nbsp;João M. Faria ,&nbsp;Diana Guimarães ,&nbsp;Demétrio Matos ,&nbsp;António H.J. Moreira ,&nbsp;Pedro Morais ,&nbsp;João L. Vilaça ,&nbsp;Vítor Carvalho","doi":"10.1016/j.irbm.2023.100810","DOIUrl":"https://doi.org/10.1016/j.irbm.2023.100810","url":null,"abstract":"<div><h3>Background</h3><p>Monitoring COVID-19 symptoms has become a critical task in controlling the spread of the virus and preventing hospitalizations. Aiming to contribute to efficient monitoring solutions, this article presents the development and testing of a wearable device capable of continuous monitoring biometric signals associated with the presence of COVID-19, such as the heart rate, the blood oxygen saturation, and the body temperature.</p></div><div><h3>Methods</h3><p>To ensure continuous monitoring the device is designed to be worn in the ear. Here, the temperature is measured through a non-contact infrared temperature sensor placed inside the ear canal while the heart rate and the pulse oximetry signals are monitored through a photoplethysmography reflective sensor positioned at the earlobe.</p><p>The proposed device's performance was evaluated by comparing it against a medical certified station. Usability and ergonomics were assessed through users' questionnaires. Additionally, experiments were performed to evaluate the hearing loss when the proposed device is in use. Data was acquired from 30 individuals of different sex, aged between 20 and 43 years old. In relation to usability and ergonomics the variation in ear dimensions was accessed and related to the device's comfort limitations.</p></div><div><h3>Results</h3><p>The temperature measurement produced a moderate correlation (<span><math><mi>R</mi><mo>=</mo><mn>0.42</mn></math></span>), despite a higher standard deviation was found in the proposed solution. This is due to the limited variability in temperature data, creating a short measuring range, as only healthy people were tested. The heart rate measurement also showed good correlation (<span><math><mi>R</mi><mo>=</mo><mn>0.96</mn></math></span>), with the proposed solution showing good repeatability with a standard deviation of 6.06 BPM, however, the SpO2 measurement was suboptimal (<span><math><mi>R</mi><mo>=</mo><mn>0.14</mn></math></span>).</p><p>The ergonomic evaluation revealed that most participants found the device shape comfortable, but some found the dimensions not adequate.</p><p>Additionally, the device was found to be user-friendly, with most participants reporting that they found it to be intuitive, and none reported a major loss in hearing in a normal conversation, however, there's a negligible loss of approximately 0.56 dB.</p></div><div><h3>Conclusions</h3><p>During this study, it was possible to develop and evaluate a wearable device that was suggested for monitoring biometric signals. The device demonstrated great reliability in temperature and heart rate measurement but showed limitations in the accuracy of pulse oximetry. The main contribution of this work is the evaluation of a continuous non-invasive monitoring concept for COVID-19 related biometric signals, which indicates good applicability in the case study.</p></div>","PeriodicalId":14605,"journal":{"name":"Irbm","volume":"44 6","pages":"Article 100810"},"PeriodicalIF":4.8,"publicationDate":"2023-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1959031823000593/pdfft?md5=e5bc5df4de60ee41ec011eb453f85a37&pid=1-s2.0-S1959031823000593-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"92101277","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":"Prediction of Atrial Fibrillation from Sinus-Rhythm Electrocardiograms Based on Deep Neural Networks: Analysis of Time Intervals and Longitudinal Study","authors":"Pietro Melzi ,&nbsp;Ruben Vera-Rodriguez ,&nbsp;Ruben Tolosana ,&nbsp;Ancor Sanz-Garcia ,&nbsp;Alberto Cecconi ,&nbsp;Guillermo J. Ortega ,&nbsp;Luis Jesus Jimenez-Borreguero","doi":"10.1016/j.irbm.2023.100811","DOIUrl":"https://doi.org/10.1016/j.irbm.2023.100811","url":null,"abstract":"<div><h3>Objective</h3><p>Artificial Intelligence (AI) in electrocardiogram (ECG) analysis helps to identify persons at risk of developing atrial fibrillation (AF) and reduces the risk for severe complications. Our aim is to investigate the performance of AI-based methods predicting future AF from sinus rhythm (SR) ECGs, according to different characteristics of patients, time intervals for prediction, and longitudinal measures.</p></div><div><h3>Methods</h3><p>We designed a retrospective, prognostic study to predict AF occurrence in patients from 12-lead SR ECGs. We classified patients in two groups, according to their ECGs: 3,761 developed AF and 22,896 presented only SR ECGs. We assessed the impact of age on the overall performance of deep neural network (DNN)-based systems, which consist in a variation of Residual Networks for time series. Then, we analysed how much in advance our system can predict AF from SR ECGs and the performance for different categories of patients with AUC and other metrics.</p></div><div><h3>Results</h3><p>After balancing the age distribution between the two groups of patients, our model achieves AUC of 0.79 (0.72-0.86) without additional constraints, 0.83 (0.76-0.89) for ECGs recorded in the last six months before AF, and 0.87 (0.81-0.93) for patients with stable AF risk measures over time, with sensitivity of 90.62% (80.70-96.48) and diagnostic odd ratio of 20.49 (8.56-49.09).</p></div><div><h3>Conclusion</h3><p>This study shows the ability of DNNs to predict new onsets of AF from SR ECGs, with the best performance achieved for patients with stable AF risk score over time. The introduction of this time-based score opens new possibilities for AF prediction, thanks to the analysis of long-span time intervals and score stability.</p></div>","PeriodicalId":14605,"journal":{"name":"Irbm","volume":"44 6","pages":"Article 100811"},"PeriodicalIF":4.8,"publicationDate":"2023-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S195903182300060X/pdfft?md5=029d208308cda42d40c652bd0a384bbe&pid=1-s2.0-S195903182300060X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"92017994","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":"Normative Data of the External Work of Individual Limbs and of the Distribution of Joint Work During Stair Crossing","authors":"Helene Pillet ,&nbsp;Boris Dauriac ,&nbsp;Coralie Villa ,&nbsp;Isabelle Loiret ,&nbsp;François Lavaste ,&nbsp;Xavier Bonnet","doi":"10.1016/j.irbm.2023.100806","DOIUrl":"https://doi.org/10.1016/j.irbm.2023.100806","url":null,"abstract":"<div><h3>Background</h3><p>Stair walking requires to elevate or lower the body center of mass and results in increased muscle contractions and consumed energy compared to level walking. Mechanical work produced by the body can be quantified through Individual Limb Method and the summed lower limb joint work but there does not exist normative data of these works in stair ascent and descent compared to slope ascent and descent of the same individuals.</p></div><div><h3>Methods</h3><p>Upstair and downstair walking were investigated at 0%, 5% and 12% inclinations and compared to upslope and downslope walking for thirteen able-bodied volunteers. Lower limb joint and individual limb powers and works were compared across walking conditions.</p></div><div><h3>Findings</h3><p>Work production and absorption required to elevate or lower the center of mass directly depend on the inclination to be crossed (about 0.35 J/kg for 5% slope, 0.9 J/kg for 12% slope and 1.6 J/kg for stair). However, the distribution among joints and between gait phases is different when considering stair versus slope walking. In particular, the role of the knee is exacerbated for work production in stair ascent (45% of total work) as well as for work absorption in stair descent (61% of total work). Also, more work production/absorption is performed during the swing phase for stair walking then for slope walking.</p></div><div><h3>Interpretation</h3><p>This study provides reference data of the Individual Limb mechanical work performed during stair walking and show that this method can substitute to summed lower limb joint one during the stance phase of stair walking.</p></div>","PeriodicalId":14605,"journal":{"name":"Irbm","volume":"44 6","pages":"Article 100806"},"PeriodicalIF":4.8,"publicationDate":"2023-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"92101276","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":"Complete 3D Kinematics Parameters of the Temporo-Mandibular Joints Using in Vivo Data Fusion","authors":"Gaël Bescond ,&nbsp;Michèle Gales ,&nbsp;Régine Glineur ,&nbsp;Viktor Sholukha ,&nbsp;Stéphane Louryan ,&nbsp;Serge Van Sint Jan","doi":"10.1016/j.irbm.2023.100804","DOIUrl":"https://doi.org/10.1016/j.irbm.2023.100804","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;Objectives&lt;/h3&gt;&lt;p&gt;&lt;span&gt;&lt;span&gt;The temporo-mandibular joint (TMJ) has implications in vital functions and its disorder prevalence is between 5% and 12%. The &lt;/span&gt;mandible motions rely on two joints where mandibular condyles are generally asymmetric and highly individual. They rotate during jaw opening and closing and translate vertically and anteroposteriorly. Quantitative motion analysis tools are of interest to better understand normal and abnormal &lt;/span&gt;TMJ behavior. Previous studies have reported the asymmetrical behavior of the mandible compared to the skull as well as the synchronism of rotation and translation during its motions. But none of them has developed an experimental protocol using in vivo motion data fused with a tridimensional (3D) model. Therefore, we aim to provide the detailed kinematic parameters of the mandible compared to the skull, of the 2 condyles compared to their sockets and the instantaneous helicoidal axis (IHA) calculation through a clearly described new technology: in vivo data motion fused with virtual palpation on 3D models. We also compare the accuracy and the consistency of our results with the existing literature.&lt;/p&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Material and methods&lt;/h3&gt;&lt;p&gt;Five healthy subjects fitted with a tailor-made dental and head clusters performed mouth opening/closing, diduction and chewing motions. 15 anatomical landmarks (ALs) were palpated on their skull and their mandible. The trajectory of the markers and ALs was recorded by opto-electronic cameras. 3D models created from magnetic resonance imaging (MRI) from the 5 subjects were processed through a segmentation procedure and imported into a musculo-skeletal data processing software. Virtual palpation was used to locate specific ALs and to build coordinate systems following the ISB recommendations. The ALs coordinates, the motion files and the morphological model were fused. Motion cycles were normalized from 1 to 100% of rotations and translations duration in coordinate systems, instantaneous helical axis (IHA) parameters were computed for the 3 motions.&lt;/p&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Results&lt;/h3&gt;&lt;p&gt;Median RMSE between manually and virtually palpated ALs was 8,0 mm.&lt;/p&gt;&lt;p&gt;During opening motion, rotation around the Z-axis (median 24,9°), translations along the X-axis and the Y-axis (median 9,7 mm and 6,3 mm respectively) were happening all at once. The IHA was obliquely orientated.&lt;/p&gt;&lt;p&gt;During diduction motion, rotations around the Y-axis and the X-axis (median 10,7° and 3.3° respectively), translation on the Z-axis is (median −9.4 mm) occurred simultaneously. The IHA orientation was oblique and changed accordingly to the diduction side.&lt;/p&gt;&lt;p&gt;During chewing motion, median rotation around the Z-axis was −2.2° and median translation on the Y-axis −1.0 mm. The IHA pathway high asymmetry coincided with typical movements of working and balancing condyles.&lt;/p&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Conclusion&lt;/h3&gt;&lt;p&gt;Complete 3D kinematics parameters of the TMJs, corresponding to the ISB reco","PeriodicalId":14605,"journal":{"name":"Irbm","volume":"44 6","pages":"Article 100804"},"PeriodicalIF":4.8,"publicationDate":"2023-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49728920","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":"Classification of Moderate and Advanced Alzheimer's Patients Using Radial Basis Function Based Neural Networks Initialized with Fuzzy Logic","authors":"Carlos Roncero Parra ,&nbsp;Alfonso Parreño Torres ,&nbsp;Jorge Mateo Sotos ,&nbsp;Alejandro L. Borja","doi":"10.1016/j.irbm.2023.100795","DOIUrl":"https://doi.org/10.1016/j.irbm.2023.100795","url":null,"abstract":"<div><h3>Background</h3><p>Alzheimer's disease can be diagnosed through various clinical methods. Among them, electroencephalography has proven to be a powerful, non-invasive, affordable, and painless tool for its diagnosis.</p></div><div><h3>Objectives</h3><p><span>In this study, eight machine learning (ML) approaches, including SVM<span>, BLDA<span>, DT, GNB, KNN, RF, and deep learning (DL) methods such as </span></span></span>RNN<span> and RBF, were employed to classify Alzheimer's disease into two stages: moderate Alzheimer's disease (ADM) and advanced Alzheimer's disease (ADA).</span></p></div><div><h3>Material and methods</h3><p>To this aim, electroencephalography data collected from five different hospitals over a decade has been used. A novel method based on neural networks has been proposed to increase accuracy and obtain fast classification times.</p></div><div><h3>Results</h3><p>Results show that deep neuronal networks based on radial basis functions initialized with fuzzy means achieved the best balanced accuracy with 96.66% accuracy in ADA classification and 93.31% accuracy in ADM classification.</p></div><div><h3>Conclusion</h3><p>Apart from improving accuracy, it is noteworthy that this algorithm had never been used before to classify patients with Alzheimer's disease.</p></div>","PeriodicalId":14605,"journal":{"name":"Irbm","volume":"44 5","pages":"Article 100795"},"PeriodicalIF":4.8,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49704792","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}