Irbm最新文献

{"title":"Attention-Based Neural Network for Cardiac MRI Segmentation: Application to Strain and Volume Computation","authors":"","doi":"10.1016/j.irbm.2024.100850","DOIUrl":"10.1016/j.irbm.2024.100850","url":null,"abstract":"<div><h3>Context</h3><p>Deep learning algorithms have been widely used for cardiac image segmentation. However, most of these architectures rely on convolutions that hardly model long-range dependencies, limiting their ability to extract contextual information. Moreover, the traditional U-net architecture suffers from the difference of semantic information between feature maps of the encoder and decoder (also known as the semantic gap).</p></div><div><h3>Material and method</h3><p>To address this issue, a new network architecture relying on attention mechanism was introduced. Swin Filtering Blocks (SFB), that use Swin Transformer blocks in a cross-attention manner, were added between the encoder and the decoder to filter information coming from the encoder based on the feature map from the decoder. Attention was also employed at the lowest resolution in the form of a transformer layer to increase the receptive field of the network.</p><p>We conducted experiments to assess both generalization capability and to evaluate how training on all frames of the cardiac cycle rather than only the end-diastole and end-systole impacts strain and segmentation performances.</p></div><div><h3>Results and conclusion</h3><p>Visual inspection of feature maps suggested that Swin Filtering Blocks contribute to the reduction of the semantic gap. Performing attention between all patches using a transformer layer brought higher performance than convolutions. Training the model with all phases of the cardiac cycle resulted in slightly more accurate segmentations while leading to a more noticeable improvement for strain estimation. A limited decrease in performance was observed when testing on out-of-distribution data, but the gap widens for the most apical slices.</p></div>","PeriodicalId":14605,"journal":{"name":"Irbm","volume":null,"pages":null},"PeriodicalIF":5.6,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1959031824000319/pdfft?md5=45a62576b482068e95734d0020169441&pid=1-s2.0-S1959031824000319-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141772929","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":"Transition Network-Based Analysis of Electrodermal Activity Signals for Emotion Recognition","authors":"","doi":"10.1016/j.irbm.2024.100849","DOIUrl":"10.1016/j.irbm.2024.100849","url":null,"abstract":"<div><h3>Background</h3><p>Emotion assessment plays a vital role in understanding and enhancing various aspects of human life, from mental well-being and social interactions to decision-making processes. Electrodermal Activity (EDA) is widely used for emotion assessment, as it is highly sensitive to sympathetic nervous system activity. While numerous existing approaches are available for EDA-based emotion assessment, they often fall short in capturing the dynamic non-linear variations and time-varying characteristics of EDA. These limitations hinder their effectiveness in accurately classifying emotional states along the Arousal and Valence dimensions. This study aims to address these shortcomings by introducing Transition Network Analysis (TNA) as a novel approach to EDA-based emotion assessment.</p></div><div><h3>Methods</h3><p>To explore the dynamic non-linear variations in EDA and their impact on the classification of Arousal and Valence dimensions, we decomposed EDA data into its phasic and tonic components. The phasic information is represented over a transition network. From the transition network, we extracted seven features. These features were subsequently used for classification purposes employing four different machine learning classifiers: logistic regression, multi-layer perceptron, random forest, and support vector machine (SVM). The performance of each classifier was evaluated using Leave-One-Subject-Out cross-validation. The study evaluated the performance of these classifiers in characterizing emotional dimensions.</p></div><div><h3>Results</h3><p>The results of this research reveal significant variations in Degree Centrality and Closeness Centrality within the transition network features, enabling effective characterization of Arousal and Valence dimensions. Among the classifiers, the SVM achieved F1 scores of 71% and 72% for Arousal and Valence classification, respectively.</p></div><div><h3>Significance</h3><p>This study holds significant implications as it not only enhances our understanding of EDA's non-linear dynamics but also demonstrates the potential of TNA in addressing the limitations of existing techniques for EDA-based emotion assessment. The findings open exciting opportunities for the advancement of wearable EDA monitoring devices in naturalistic settings, bridging a critical gap in the field of affective computing. Furthermore, this research underlines the importance of recognizing the limitations in current EDA-based emotion assessment methods and suggests an innovative path forward in the pursuit of more accurate and comprehensive emotional state classification.</p></div>","PeriodicalId":14605,"journal":{"name":"Irbm","volume":null,"pages":null},"PeriodicalIF":5.6,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141772930","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":"Influence of Image Factors on the Performance of Ophthalmic Ultrasound Deep Learning Model","authors":"","doi":"10.1016/j.irbm.2024.100848","DOIUrl":"10.1016/j.irbm.2024.100848","url":null,"abstract":"<div><h3>Objective</h3><p>This study aims to evaluate the impact of image factors on the performance of deep learning models used for ophthalmic ultrasound image diagnosis.</p></div><div><h3>Methods</h3><p>A total of 3,373 ophthalmic ultrasound images are used to deeply evaluate the influence of image factors on the performance of deep learning classification models. Inceptionv3, Xception, and the fusion model Inceptionv3-Xception are used to explore how brightness, contrast, gain, noise, size, format, pseudo-color seven image-related factors affect the classification performance of the model.</p></div><div><h3>Results</h3><p>Inceptionv3-Xception has advantages in the recognition accuracy of various image factors. When the image brightness changes, the model's performance shows a downward trend (0.5 vs. 1 vs. 1.8, ACC 95.73 vs. 97.06 vs. 93.54, P &lt; 0.05). When the image contrast changes, the model's performance is comparable (0.5 vs. 1 vs. 1.2, ACC 96.23 vs. 96.95 vs. 97.45, P &gt; 0.05). When the image gain drops to 50 dB, the model's accuracy decreases significantly (50 dB vs. 105 dB, ACC 96.49 vs. 97.57, P &lt; 0.05). When Gaussian noise is added to the image, the model's performance gradually decreases (0.02 vs. 0, ACC 89.48vs97.06, P &lt; 0.05). When the image size drops to 25% of the original image, the model's performance decreases significantly (25% vs. 100%, ACC 93.18 vs. 97.06, P &lt; 0.01). When the image format changes, the model's recognition accuracy is similar (JPG vs. BMP vs. PNG, ACC 96.98 vs. 97.06 vs. 97.06, P &gt; 0.05). The accuracy of the model in recognizing pseudo-color images decreases significantly compared to grayscale images (grayscale vs. pseudo-color, ACC 35.96 vs. 97.06).</p></div><div><h3>Conclusion</h3><p>These results indicate that image quality greatly influences the model training process, and acquiring high-quality images is an important prerequisite for high recognition performance of the model. This study offers valuable insights for the improvement of other robust deep learning models for ophthalmic ultrasound image recognition.</p></div>","PeriodicalId":14605,"journal":{"name":"Irbm","volume":null,"pages":null},"PeriodicalIF":5.6,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1959031824000290/pdfft?md5=d2db3fd118a09a6347da8a8332f055bd&pid=1-s2.0-S1959031824000290-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141609818","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":"Subject-Specific Probability Maps of Scalp, Skull and Cerebrospinal Fluid for Cranial Bones Segmentation in Neonatal Cerebral MRIs","authors":"","doi":"10.1016/j.irbm.2024.100844","DOIUrl":"10.1016/j.irbm.2024.100844","url":null,"abstract":"<div><h3>Objectives</h3><p>Segmentation of cranial bones in magnetic resonance images (MRIs) is a challenging and indispensable task to study neonatal brain development and injury. This paper presents a new approach for creating subject-specific probability maps of the scalp, skull and cerebrospinal fluid (CSF) from retrospective bimodal (MR and CT) images acquired from neonates in the gestational age range of 39 to 42 weeks. These maps are subsequently employed for the segmentation of cranial bones in cerebral MRIs from neonates in the same age range.</p></div><div><h3>Material and methods</h3><p>Retrospective MR and CT of neonates with normal head in the gestational age range of 39-42 weeks were preprocessed, segmented semi-automatically and employed as atlas data. For an input MR image acquired from a subject under study, a preprocessing stage and three main processing blocks were performed: First, subject-specific head and intracranial templates and CSF probability map were created using retrospective MR atlas data. Second, the CT atlas data were coregistered to MR templates and the resulted deformation matrices were fed to the next block to create subject-specific scalp and skull probability maps. Finally, some novel performance measures were presented to evaluate the performance of subject-specific CSF, scalp and skull probability maps for skull and intracranial segmentation in neonatal MRIs.</p></div><div><h3>Results</h3><p>The subject-specific probability maps were employed for brain tissue extraction and compared with two public methods such as Brain Extraction Tool (BET) and Brain Surface Extractor (BSE). They were also applied for cranial bone extraction. Then, the similarity in shape between the frontal and occipital sutures (which had been reconstructed from segmented cranial bones) and the ground truth landmarks was evaluated. For this purpose, modified versions of the Dice similarity coefficient (DSC) were used. Finally, a retrospective bimodal (MR-CT) data acquired from a neonate within a short time interval was used for evaluation. After co-alignment of the two images, the DSC and modified Hausdorff distance (MHD) were used to compare the similarity of cranial bones in the MR and CT images.</p></div><div><h3>Conclusion</h3><p>Significant improvements were achieved compared to conventional methods which rely solely on MR image intensities. These advancements hold promise for enhancing neurodevelopmental studies in neonates. The algorithm for creating subject-specific atlases is publicly accessible through a graphical user interface at <span><span>medvispy.ee.kntu.ac.ir</span><svg><path></path></svg></span>.</p></div>","PeriodicalId":14605,"journal":{"name":"Irbm","volume":null,"pages":null},"PeriodicalIF":5.6,"publicationDate":"2024-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141507485","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":"Automatic Classification Framework for Neonatal Seizure Using Wavelet Scattering Transform and Nearest Component Analysis","authors":"Vipin Prakash Yadav ,&nbsp;Kamlesh Kumar Sharma","doi":"10.1016/j.irbm.2024.100842","DOIUrl":"https://doi.org/10.1016/j.irbm.2024.100842","url":null,"abstract":"<div><h3>Introduction</h3><p>Neonatal seizure is a common neurologic disorder in neonates. The diagnosis of a neonatal seizure can be made clinically or with an EEG. However, the clinical diagnosis of neonatal seizures is difficult, particularly in critically ill infants, because of the multitude of epileptic and nonepileptic clinical manifestations. On the other hand neonatal seizure can be effectively detected using EEG recordings. Hence, there is a need for an electroencephalograph (EEG) based automatic diagnosis framework for neonatal seizure.</p></div><div><h3>Methods</h3><p>This work proposed a wavelet scattering transform (WST) and histogram-based nearest component analysis (HBNCA) based framework for classifying seizures and non-seizure neonate's EEG signals. The WST converts EEG signals into its translation invariant and deformation stable representation. The HBNCA method is deployed to find the effective wavelet scattering coefficients (WSC) for classifying seizures and non-seizures EEG signals. Then, various classifiers are used to identify the effectiveness of the features.</p></div><div><h3>Results</h3><p>The proposed framework is managed to get an average accuracy of 98.59% and 97.83% for a 1-second duration of EEG signal for repeated random subsampling validation (RRSV) and leave one out cross-validation (LOOCV), respectively.</p></div><div><h3>Conclusions</h3><p>The results are compared with the other state of art methods. The accurate classification from the 1-second duration of the EEG signal shows the potential of the proposed framework for reliable neonatal seizure classification.</p></div>","PeriodicalId":14605,"journal":{"name":"Irbm","volume":null,"pages":null},"PeriodicalIF":5.6,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141480568","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":"Optimizing Uterine Synchronization Analysis in Pregnancy and Labor Through Window Selection and Node Optimization","authors":"Kamil Bader El Dine ,&nbsp;Noujoud Nader ,&nbsp;Mohamad Khalil ,&nbsp;Catherine Marque","doi":"10.1016/j.irbm.2024.100843","DOIUrl":"https://doi.org/10.1016/j.irbm.2024.100843","url":null,"abstract":"<div><p>1) Introduction: Preterm labor (PL) has globally become the leading cause of death in children under the age of 5 years. One of the most significant keys to preventing preterm labor is its early detection. 2) Objectives: The primary objectives of this study are to address the problem of PL by providing a new approach by analyzing the electrohysterographic (EHG) signals, which are recorded on the mother's abdomen during labor and pregnancy. 3) Methods: The EHG signal reflects the electrical activity that induces the mechanical contraction of the myometrium. Because EHGs are known to be non-stationary signals, and because we anticipate connectivity to alter during contraction (due to electrical diffusion and the mechanotransduction process), we applied the windowing approach on real signals to identify the best windows and the best nodes with the most significant data to be used for classification. The suggested pipeline includes: i) dividing the 16 EHG signals that are recorded from the abdomen of pregnant women in N windows; ii) apply the connectivity matrices on each window; iii) apply the Graph theory-based measures on the connectivity matrices on each window; iv) apply the consensus Matrix on each window in order to retrieve the best windows and the best nodes. Following that, several neural network and machine learning methods are applied to the best windows and best nodes to categorize pregnancy and labor contractions, based on the different input parameters (connectivity method alone, connectivity method plus graph parameters, best nodes, all nodes, best windows, all windows). 4) Results: Results showed that the best nodes are nodes 8, 9, 10, 11, and 12; while the best windows are 2, 4, and 5. The classification results obtained by using only these best nodes are better than when using the whole nodes. The results are always better when using the full burst, whatever the chosen nodes. 5) Conclusion: The windowing approach proved to be an innovative technique that can improve the differentiation between labor and pregnancy EHG signals.</p></div>","PeriodicalId":14605,"journal":{"name":"Irbm","volume":null,"pages":null},"PeriodicalIF":5.6,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141483512","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":"Interpersonal Motor Coordination in Children with Autism and the Establishment of Machine Learning Models to Objectively Classify Children with Autism and Typical Development","authors":"","doi":"10.1016/j.irbm.2024.100838","DOIUrl":"10.1016/j.irbm.2024.100838","url":null,"abstract":"<div><h3>Background</h3><p>The global prevalence of autism spectrum disorder (ASD) is around 1%. Yet the current diagnosis of ASD mainly depends on clinicians' experience and caregivers' report, which are subjective, time consuming, and labor demanding. An objective and efficient way to diagnose ASD is urgently needed. The objective of this study was to quantify an omnipresent yet least studied behavioral characteristic in children with ASD – interpersonal motor coordination (IMC), and to investigate the feasibility of using IMC related features to identify ASD by implementing machine learning algorithms.</p></div><div><h3>Methods</h3><p>Twenty children with ASD and twenty-three children with typical development (TD) were filmed in a conversation with an interviewer. Motion energy analysis was implemented to obtain the movement time series, and cross wavelet analysis (CWA) quantified the level of IMC at different movement frequencies. Machine learning algorithms were utilized to examine whether these two groups of children could be accurately classified using features of IMC.</p></div><div><h3>Results</h3><p>Statistical analysis revealed reduced IMC in the ASD group at relatively high movement frequencies. The establishment of machine learning (ML) models showed that the maximum classification accuracy was 85.37% (specificity = 95.24%, sensitivity = 75.00%) using five original coherence values computed with CWA. In addition, the classification accuracy could be improved to 92.68% (specificity = 95.24%, sensitivity = 90.00%) with three novel features created by taking the sum of statistically significant features.</p></div><div><h3>Conclusions</h3><p>Children with ASD demonstrated an atypical profile of IMC, and IMC could be used to objectively classify children with ASD and TD. In addition, our analyses showed that creating novel features based on statistically significant features could help improve classification performance. It is proposed that such economic, contactless, and calibration-free approach to data collection might well serve both ASD research and practice, particularly early objective identification. However, this study could be improved with respect to larger sample size with balanced gender ratio and different severity.</p></div>","PeriodicalId":14605,"journal":{"name":"Irbm","volume":null,"pages":null},"PeriodicalIF":5.6,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141395742","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":"Overdistention Accelerates Electrophysiological Changes in Uterine Muscle Towards Labour in Multiple Gestations","authors":"Alba Diaz-Martinez ,&nbsp;Gema Prats-Boluda ,&nbsp;Rogelio Monfort-Ortiz ,&nbsp;Javier Garcia-Casado ,&nbsp;Alba Roca-Prats ,&nbsp;Enrique Tormo-Crespo ,&nbsp;Félix Nieto-del-Amor ,&nbsp;Vicente-José Diago-Almela ,&nbsp;Yiyao Ye-Lin","doi":"10.1016/j.irbm.2024.100837","DOIUrl":"10.1016/j.irbm.2024.100837","url":null,"abstract":"<div><h3>Background for the research</h3><p>Premature birth and its associated complications are one of the biggest global health problems, since there is currently no effective screening method in clinical practice to accurately identify the true Preterm Birth (PTB) from the false threatened ones. Despite the high prevalence of PTB in multiple gestation (MG) women which amounted up to 60%, in the literature there is any work about their uterine myoelectric activities in vivo system. Electrohysterography (EHG) has been emerged as an alternative technique for predicting PTB in single gestation (SG) women.</p></div><div><h3>Purpose</h3><p>The aim of this study was to characterize and compare the uterine myoelectrical activity in vivo system of SG and MG women in regular check-ups, to provide the basis for early detection and prevention of preterm labour in MG.</p></div><div><h3>Basic procedures</h3><p>A prospective observational cohort study was conducted on 31 SG and 18 MG women between the 28^th and 32^th WoG who underwent regular check-ups in the Polytechnic and University Hospital La Fe (Valencia, Spain). The 30-minute bipolar recording was filtered in the 0.1-4 Hz bandwidth and downsampled to 20 Hz. Signal analysis was performed in 120-second moving windows with 50% overlap, after removing artefacts by a double- blind expert process. A set of 8 temporal, spectral and non-linear parameters were calculated: root mean square (RMS), kurtosis of the Hilbert envelope (KHE), median frequency (MDF), H/L ratio, and sample entropy (SampEn) and bubble entropy (BubbEn) calculated in the whole bandwidth (WBW) and the fast wave high (FWH). The 10th, 50th and 90th percentiles of all windows analysed were calculated to obtain representative values of the recordings. For each parameter and percentile, statistically significant differences between the SG and MG groups and their statistical power (SP) were analysed to determine both the existence of an effect and substantive significance, respectively.</p></div><div><h3>Main findings</h3><p>In comparison to SG, MG EHG exhibited significant higher impulsiveness and higher predictability than SG which was reflected in the KHE (SP₁₀ = 85.2, p₁₀ &lt; 0.001) and entropy measures (SampEn FWH: SP₅₀ = 62.0, p₅₀ = 0.0.016; SP₉₀ = 52.5, p₉₀ = 0.059. BubbEn FWH: SP₅₀ = 75.2, p₅₀ &lt; 0.001; SP₉₀ = 60.3, p₉₀ = 0.002), suggesting an accelerated evolution of uterine electrophysiological condition. In addition, several EHG parameters were found to significantly correlate with foetal weight such as amplitude (RMS: r₉₀ = 0.311, p₉₀ = 0.006), signal impulsiveness (KHE: r₁₀ = 0.311, p₁₀ = 0.006) and entropy measures (SampEn FWH: r₅₀ = −0.317, p₅₀ = 0.005*; r₉₀ = −0.279, p₉₀ = 0.013*. BubbEn FWH: r₅₀ = −0.3","PeriodicalId":14605,"journal":{"name":"Irbm","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1959031824000186/pdfft?md5=255e8c281ae55cb57d5e7fff904cfa61&pid=1-s2.0-S1959031824000186-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141252897","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":"Body Water Volume Estimation Using Bio Impedance Analysis: Where Are We?","authors":"Sali El Dimassi ,&nbsp;Julien Gautier ,&nbsp;Vincent Zalc ,&nbsp;Sofiane Boudaoud ,&nbsp;Dan Istrate","doi":"10.1016/j.irbm.2024.100839","DOIUrl":"https://doi.org/10.1016/j.irbm.2024.100839","url":null,"abstract":"<div><p>BioImpedance Analysis (BIA) is a safe, simple, and noninvasive technology to measure body composition. By measuring the electrical impedance of biological tissues, BIA provides valuable biological insights such as body composition, hydration status, and some health conditions. The principle is to apply an electric current to body segments, which water content and conductivity are characteristics, and to determine the electric impedance depending on body tissues passed through. However, these measurements are indirectly related to body composition and intensively depend on limited and imprecise assumptions to estimate mathematical models. This is the source of methodological and experimental challenges. BIA is very promising to offer non-invasive and portable solutions to assess health status and well-being, but challenges must be considered: they impact technological limitations, methodological standardization, and data interpretation. Advancements in BIA require to address these hurdles to improve accuracy, reliability, and applicability in diverse settings. In this article, we reviewed in depth these challenges based on a systematic review of literature.</p></div><div><h3>Purpose</h3><p>The objective of this systematic review is to identify key challenges of BIA to assess body composition to develop possible directions for improving this technology. Our review underlines clearly the need to reduce these challenges with the multiplication of biostatistical sources, the definition of personalized models, and the adjustment of mathematical assumptions, to improve BIA reliability and adoption in e-health or specific applications.</p></div><div><h3>Methodology</h3><p>The objective of this systematic review from published literature was to answer the question: “How to assess whole body composition in the average human adult with BIA, what are the scientific challenges and limits for a wider adoption in medical practice?”. We limited our research within Pubmed, ScienceDirect and IEEE complementary databases. Our research was carried out in English using the keywords “body composition” and “bioimpedance analysis” over a period from the included 1995 to 2022. We controlled inclusion criteria to collect only articles with average human adults' groups: age from 18 years, both males and females, mixed ethnics, BMI ranging from 18 to 30 kg/m², either healthy or non-healthy status. We added the following exclusion criteria: athletics, malnourished, eating or mental disorders, pregnancy and menstrual period. Finally, we kept articles validated versus state-of-the-art methods DEXA, or isotope dilution.</p></div><div><h3>Summary findings</h3><p>Our literature review identified seven major challenges with BIA: <em>Rheological modeling precision</em> represent human body as an electrical circuit made of resistors and capacitors to reflect electrical properties of tissues; <em>Body compartments</em> to model human body as a combination of cylind","PeriodicalId":14605,"journal":{"name":"Irbm","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1959031824000204/pdfft?md5=689c5b6bf3f72fc512af9e5cf8afe9bb&pid=1-s2.0-S1959031824000204-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141264234","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":"MMANet: A multi-task residual network for Alzheimer's disease classification and brain age prediction","authors":"Chengyi Qian,&nbsp;Yuanjun Wang","doi":"10.1016/j.irbm.2024.100840","DOIUrl":"10.1016/j.irbm.2024.100840","url":null,"abstract":"<div><p>Objective: Alzheimer's disease (AD) is an irreversible neurodegenerative disease, while mild cognitive impairment (MCI) is a clinical precursor of AD, thus differentiation of AD, MCI and normal control (NC) from noninvasive magnetic resonance imaging (MRI) has positive clinical implications. Material and method: We utilize a 3D residual network to classify AD, MCI, and NC, and add a multiscale module to the original network to enhance the feature representation capability of the network, as well as a cross-dimensional attentional mechanism to enhance the network's attention to important brain regions. We experimentally verified that the network is more inclined to overestimate the brain age of patients in AD and MCI subgroups, thus proving that there is a high correlation between the brain age prediction task and the AD classification task. Therefore, we adopted a multi-task learning approach, using brain age prediction as a supplementary task for AD classification to reduce the risk of overfitting of the network during the training process. Results: Our method achieved 96.02% accuracy, 93.40% precision, 91.48% recall, and 92.24% F1 value in AD/MCI/NC classification. Conclusions: Ablation experiments confirmed that our proposed cross-dimensional attention and multiscale modules can improve the diagnostic performance of AD and MCI, and that multi-task learning in conjunction with brain age prediction can further improve the performance.</p></div>","PeriodicalId":14605,"journal":{"name":"Irbm","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141252681","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}