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Reliable prediction of difficult airway for tracheal intubation from patient preoperative photographs by machine learning methods Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-03-12 Fernando García-García, Dae-Jin Lee, Francisco J. Mendoza-Garcés, Susana García-Gutiérrez
Estimating the risk of a difficult tracheal intubation should help clinicians in better anaesthesia planning, to maximize patient safety. Routine bedside screenings suffer from low sensitivity. To develop and evaluate machine learning (ML) and deep learning (DL) algorithms for the reliable prediction of intubation risk, using information about airway morphology. Observational, prospective cohort study
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Sensitivity analysis of paediatric knee kinematics to the graft surgical parameters during anterior cruciate ligament reconstruction: a sequentially linked neuromusculoskeletal-finite element analysis Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-03-11 Ayda Karimi Dastgerdi, Amir Esrafilian, Christopher P. Carty, Azadeh Nasseri, Martina Barzan, Rami K. Korhonen, Ivan Astori, Wayne Hall, David John Saxby
Incidence of paediatric anterior cruciate ligament (ACL) ruptures has increased substantially over recent decades. Following ACL rupture, ACL reconstruction (ACLR) surgery is performed to restore knee function. This surgery involves replacing the failed ACL with a graft, however, surgeons must select from range of surgical parameters (e.g., type, size, insertion, and pre-tension) with no robust evidence
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MOB-CBAM: A Dual-Channel Attention-Based Deep Learning Generalizable Model for Breast Cancer Molecular Subtypes Prediction using Mammograms Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-03-10 Iqra Nissar, Shahzad Alam, Sarfaraz Masood, Mohammad Kashif
Deep Learning models have emerged as a significant tool in generating efficient solutions for complex problems including cancer detection, as they can analyze large amounts of data with high efficiency and performance. Recent medical studies highlight the significance of molecular subtype detection in breast cancer, aiding the development of personalized treatment plans as different subtypes of cancer
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STCGRU: A hybrid model based on CNN and BiGRU for mild cognitive impairment diagnosis Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-03-08 Hao Zhou, Liyong Yin, Rui Su, Ying Zhang, Yi Yuan, Ping Xie, Xin Li
Early diagnosis of mild cognitive impairment (MCI) is one of the essential measures to prevent its further development into Alzheimer's disease (AD). In this paper, we propose a hybrid deep learning model for early diagnosis of MCI, called spatio-temporal convolutional gated recurrent unit network (STCGRU). The STCGRU comprises three bespoke convolutional neural network (CNN) modules and a bi-directional
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IODeep: An IOD for the introduction of deep learning in the DICOM standard Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-03-08 Salvatore Contino, Luca Cruciata, Orazio Gambino, Roberto Pirrone
In recent years, Artificial Intelligence (AI) and in particular Deep Neural Networks (DNN) became a relevant research topic in biomedical image segmentation due to the availability of more and more data sets along with the establishment of well known competitions. Despite the popularity of DNN based segmentation on the research side, these techniques are almost unused in the daily clinical practice
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TipDet: A multi-keyframe motion-aware framework for tip detection during ultrasound-guided interventions Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-03-08 Ruixin Wang, Guoping Tan, Xiaohui Liu
Automatic needle tip detection is important in real-time ultrasound (US) images that are utilized to guide interventional needle puncture procedures in clinical settings. However, due to the problem caused by the severe background interferences and the tip characteristics of small size, being grayscale and indistinctive appearance patterns, tip detection in US images is challenging. To achieve precise
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Brain MR image simulation for deep learning based medical image analysis networks Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-03-07 Aymen Ayaz, Yasmina Al Khalil, Sina Amirrajab, Cristian Lorenz, Juergen Weese, Josien Pluim, Marcel Breeuwer
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DAX-Net: A dual-branch dual-task adaptive cross-weight feature fusion network for robust multi-class cancer classification in pathology images Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-03-07 Doanh C. Bui, Boram Song, Kyungeun Kim, Jin Tae Kwak
Multi-class cancer classification has been extensively studied in digital and computational pathology due to its importance in clinical decision-making. Numerous computational tools have been proposed for various types of cancer classification. Many of them are built based on convolutional neural networks. Recently, Transformer-style networks have shown to be effective for cancer classification. Herein
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Active learning for left ventricle segmentation in echocardiography Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-03-07 Eman Alajrami, Tiffany Ng, Jevgeni Jevsikov, Preshen Naidoo, Patricia Fernandes, Neda Azarmehr, Fateme Dinmohammadi, Matthew J. Shun-shin, Nasim Dadashi Serej, Darrel P. Francis, Massoud Zolgharni
Training deep learning models for medical image segmentation require large annotated datasets, which can be expensive and time-consuming to create. Active learning is a promising approach to reduce this burden by strategically selecting the most informative samples for segmentation. This study investigates the use of active learning for efficient left ventricle segmentation in echocardiography with
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Facial augmented reality based on hierarchical optimization of similarity aspect graph Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-03-07 Long Shao, Tianyu Fu, Yucong Lin, Deqiang Xiao, Danni Ai, Tao Zhang, Jingfan Fan, Hong Song, Jian Yang
The existing face matching method requires a point cloud to be drawn on the real face for registration, which results in low registration accuracy due to the irregular deformation of the patient's skin that makes the point cloud have many outlier points. This work proposes a non-contact pose estimation method based on similarity aspect graph hierarchical optimization. The proposed method constructs
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Circadian assessment of heart failure using explainable deep learning and novel multi-parameter polar images Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-03-06 Mohanad Alkhodari, Ahsan H. Khandoker, Herbert F. Jelinek, Angelos Karlas, Stergios Soulaidopoulos, Petros Arsenos, Ioannis Doundoulakis, Konstantinos A. Gatzoulis, Konstantinos Tsioufis, Leontios J. Hadjileontiadis
Heart failure (HF) is a multi-faceted and life-threatening syndrome that affects more than 64.3 million people worldwide. Current gold-standard screening technique, echocardiography, neglects cardiovascular information regulated by the circadian rhythm and does not incorporate knowledge from patient profiles. In this study, we propose a novel multi-parameter approach to assess heart failure using heart
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TDASD: Generating medically significant fine-grained lung adenocarcinoma nodule CT images based on stable diffusion models with limited sample size Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-03-05 Yidan Xu, Jiaqing Liang, Yaoyao Zhuo, Lei Liu, Yanghua Xiao, Lingxiao Zhou
Spread through air spaces (STAS) is an emerging lung cancer infiltration pattern. Predicting its spread through CT scans is crucial. However, limited STAS data makes this prediction task highly challenging. Stable diffusion is capable of generating more diverse and higher-quality images compared to traditional GAN models, surpassing the dominating GAN family models in image synthesis over the past
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3D-MRI super-resolution reconstruction using multi-modality based on multi-resolution CNN Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-03-05 Li Kang, Bin Tang, Jianjun Huang, Jianping Li
High-resolution (HR) MR images provide rich structural detail to assist physicians in clinical diagnosis and treatment plan. However, it is arduous to acquire HR MRI due to equipment limitations, scanning time or patient comfort. Instead, HR MRI could be obtained through a number of computer assisted post-processing methods that have proven to be effective and reliable. This paper aims to develop a
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Multi-scale feature fusion for prediction of IDH1 mutations in glioma histopathological images Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-03-03 Xiang Liu, Wanming Hu, Songhui Diao, Deboch Eyob Abera, Racoceanu Daniel, Wenjian Qin
Mutations in isocitrate dehydrogenase 1 (IDH1) play a crucial role in the prognosis, diagnosis, and treatment of gliomas. However, current methods for determining its mutation status, such as immun histochemistry and gene sequencing, are difficult to implement widely in routine clinical diagnosis. Recent studies have shown that using deep learning methods based on pathological images of glioma can
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Towards Precision Medicine in Breast Imaging: A Novel Open Mammography Database with Tailor-Made 3D Image Retrieval for AI and Teaching Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-03-03 Natália Monteiro Cordeiro, Gil Facina, Afonso Celso Pinto Nazário, Vanessa Monteiro Sanvido, Joaquim Teodoro Araujo Neto, Ernandez Rodrigues dos Santos, Morgana Domingues da Silva, Simone Elias
This project addresses the global challenge of breast cancer, particularly in low-resource settings, by creating a pioneering mammography database. Breast cancer, identified by the World Health Organization as a leading cause of cancer death among women, often faces diagnostic and treatment resource constraints in low- and middle-income countries. To enhance early diagnosis and address educational
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Classification of recurrent major depressive disorder using a residual denoising autoencoder framework: Insights from large-scale multisite fMRI data Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-03-02 Peishan Dai, Yun Shi, Da Lu, Ying Zhou, Jialin Luo, Zhuang He, Zailiang Chen, Beiji Zou, Hui Tang, Zhongchao Huang, Shenghui Liao
Recurrent major depressive disorder (rMDD) has a high recurrence rate, and symptoms often worsen with each episode. Classifying rMDD using functional magnetic resonance imaging (fMRI) can enhance understanding of brain activity and aid diagnosis and treatment of this disorder. We developed a Residual Denoising Autoencoder (Res-DAE) framework for the classification of rMDD. The functional connectivity
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Investigating the interpretability of schizophrenia EEG mechanism through a 3DCNN-based hidden layer features aggregation framework Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-03-01 Zhifen Guo, Jiao Wang, Tianyu Jing, Longyue Fu
Electroencephalogram (EEG) signals record brain activity, with growing interest in quantifying neural activity through complexity analysis as a potential biological marker for schizophrenia. Presently, EEG complexity analysis primarily relies on manual feature extraction, which is subjective and yields varied findings in studies involving schizophrenia and healthy controls. This study aims to leverage
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A comparison between centralized and asynchronous federated learning approaches for survival outcome prediction using clinical and PET data from non-small cell lung cancer patients Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-02-29 Vi Thi-Tuong Vo, Tae-ho Shin, Hyung-Jeong Yang, Sae-Ryung Kang, Soo-Hyung Kim
Background and Objective: Survival analysis plays an essential role in the medical field for optimal treatment decision-making. Recently, survival analysis based on the deep learning (DL) approach has been proposed and is demonstrating promising results. However, developing an ideal prediction model requires integrating large datasets across multiple institutions, which poses challenges concerning
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Medical long-tailed learning for imbalanced data: Bibliometric analysis Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-02-29 Zheng Wu, Kehua Guo, Entao Luo, Tian Wang, Shoujin Wang, Yi Yang, Xiangyuan Zhu, Rui Ding
In the last decade, long-tail learning has become a popular research focus in deep learning applications in medicine. However, no scientometric reports have provided a systematic overview of this scientific field. We utilized bibliometric techniques to identify and analyze the literature on long-tailed learning in deep learning applications in medicine and investigate research trends, core authors
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Spatiotemporal modeling of nano-delivered chemotherapeutics for synergistic microwave ablation cancer therapy Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-02-27 Masoud H.H. Tehrani, Farshad Moradi Kashkooli, M. Soltani
The effectiveness of current microwave ablation (MWA) therapies is limited. Administration of thermosensitive liposomes (TSLs) which release drugs in response to heat has presented a significant potential for enhancing the efficacy of thermal ablation treatment, and the benefits of targeted drug delivery. However, a complete knowledge of the mechanobiological processes underlying the drug release process
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One-shot screening: Utilization of a two-dimensional convolutional neural network for automatic detection of left ventricular hypertrophy using electrocardiograms Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-02-25 Chun Cai, Takeshi Imai, Eriko Hasumi, Katsuhito Fujiu
Left ventricular hypertrophy (LVH) can impair ejection function and elevate the risk of heart failure. Therefore, early detection through screening is crucial. This study aimed to propose a novel method to enhance LVH detection using 12-lead electrocardiogram (ECG) waveforms with a two-dimensional (2D) convolutional neural network (CNN). Utilizing 42,127 pairs of ECG-transthoracic echocardiogram data
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A multi-task fusion model based on a residual–Multi-layer perceptron network for mammographic breast cancer screening Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-02-24 Yutong Zhong, Yan Piao, Baolin Tan, Jingxin Liu
Deep learning approaches are being increasingly applied for medical computer-aided diagnosis (CAD). However, these methods generally target only specific image-processing tasks, such as lesion segmentation or benign state prediction. For the breast cancer screening task, single feature extraction models are generally used, which directly extract only those potential features from the input mammogram
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Deep neural networks can differentiate thyroid pathologies on infrared hyperspectral images Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-02-24 Matheus de Freitas Oliveira Baffa, Denise Maria Zezell, Luciano Bachmann, Thiago Martini Pereira, Thomas Martin Deserno, Joaquim Cezar Felipe
The thyroid is a gland responsible for producing important body hormones. Several pathologies can affect this gland, such as thyroiditis, hypothyroidism, and thyroid cancer. The visual histological analysis of thyroid specimens is a valuable process that enables pathologists to detect diseases with high efficiency, providing the patient with a better prognosis. Existing computer vision systems developed
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Atom-ProteinQA: Atom-Level Protein Model Quality Assessment through Fine-grained Joint Learning Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-02-23 Yatong Han, Yingfeng Lu, Xu Yan, Hannah Cui, Shenghui Cheng, Jiayou Zheng, Yuzhe Zhou, Sheng Wang, Zhen Li
Protein model quality assessment (ProteinQA) is a fundamental task that is essential for biologically relevant applications, , protein structure refinement, protein design, . Previous works aimed to conduct ProteinQA only on the global structure or per-residue level, ignoring potentially usable and precise cues from a fine-grained per-atom perspective. In this study, we propose an atom-level ProteinQA
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Extending the code in the open-source saemix package to fit joint models of longitudinal and time-to-event data Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-02-23 Alexandra Lavalley-Morelle, France Mentré, Emmanuelle Comets, Jimmy Mullaert
Joint modeling of longitudinal and time-to-event data has gained attention over recent years with extensive developments including nonlinear models for longitudinal outcomes and flexible time-to-event models for survival outcomes, possibly involving competing risks. However, in popular software such as R, the function used to describe the biomarker dynamic is mainly linear in the parameters, and the
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HSG-MGAF Net: Heterogeneous subgraph-guided multiscale graph attention fusion network for interpretable prediction of whole-slide image Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-02-23 Meiyan Liang, Xing Jiang, Jie Cao, Shupeng Zhang, Haishun Liu, Bo Li, Lin Wang, Cunlin Zhang, Xiaojun Jia
Pathological whole slide image (WSI) prediction and region of interest (ROI) localization are important issues in computer-aided diagnosis and postoperative analysis in clinical applications. Existing computer-aided methods for predicting WSI are mainly based on multiple instance learning (MIL) and its variants. However, most of the methods are based on instance independence and identical distribution
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Reducing residual forces in spinal fusion using a custom-built rod bending machine Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-02-23 Marco von Atzigen, Florentin Liebmann, Nicola A. Cavalcanti, The Anh Baran, Florian Wanivenhaus, José Miguel Spirig, Georg Rauter, Jess Snedeker, Mazda Farshad, Philipp Fürnstahl
As part of spinal fusion surgery, shaping the rod implant to align with the anatomy is a tedious, error-prone, and time-consuming manual process. Inadequately contoured rod implants introduce stress on the screw-bone interface of the pedicle screws, potentially leading to screw loosening or even pull-out.
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Predicting amyloid positivity from FDG-PET images using radiomics: A parsimonious model Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-02-22 Ramin Rasi, Albert Guvenis, Alzheimer's Disease Neuroimaging Initiative
Amyloid plaques are one of the physical hallmarks of Alzheimer's disease. The objective of this study is to predict amyloid positivity non-invasively from FDG-PET images using a radiomics approach. We obtained FDG-PET images of 301 individuals from various groups, including control normal (CN), mild cognitive impairment (MCI), and Alzheimer's Disease (AD), from the ADNI database. Following the utilization
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Performance of Fourier-based activation function in physics-informed neural networks for patient-specific cardiovascular flows Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-02-22 Arman Aghaee, M. Owais Khan
Physics-informed neural networks (PINNs) can be used to inversely model complex physical systems by encoding the governing partial differential equations and training data into the neural network. However, neural networks are known to be biased towards learning less complex functions, called spectral bias. This has important implications in modeling cardiovascular flows, where spatial frequencies can
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Residual graph transformer for autism spectrum disorder prediction Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-02-19 Yibin Wang, Haixia Long, Tao Bo, Jianwei Zheng
Brain functional connectivity (FC) based on resting-state functional magnetic resonance imaging (rs-fMRI) has been in vogue to predict Autism Spectrum Disorder (ASD), which is a neuropsychiatric disease up the plight of locating latent biomarkers for clinical diagnosis. Albeit massive endeavors have been made, most studies are fed up with several chronic issues, such as the intractability of harnessing
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A comparative study on the deformation behavior and mechanical properties of new lower extremity arterial stents Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-02-18 Haiquan Feng, Xinyuan Shi, Tianqi Wang, Kun Wang, Juan Su
The lower extremity movement involves a complex and large amplitude extremity movement process, and arterial stents implanted in the lower extremity are prone to complex mechanical deformation behavior. Hence, the lower extremity arterial stent is required to have favorable comprehensive mechanical properties. In this study, a new lower extremity arterial stent (New) was proposed, and its deformation
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Intelligent assessment of atrial fibrillation gradation based on sinus rhythm electrocardiogram and baseline information Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-02-18 Biqi Tang, Sen Liu, Xujian Feng, Chunpu Li, Hongye Huo, Aiguo Wang, Xintao Deng, Cuiwei Yang
Atrial fibrillation (AF) is a progressive arrhythmia that significantly affects a patient's quality of life. The 4S-AF scheme is clinically recommended for AF management; however, the evaluation process is complex and time-consuming. This renders its promotion in primary medical institutions challenging. This retrospective study aimed to simplify the evaluation process and present an objective assessment
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OrthoCalc: The six degrees of freedom measurement workflow of rotational and displacement changes for maxilla positioning evaluation Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-02-16 Yao Gao, Yifei Gu, Jeroen Van Dessel, Heinz-Theo Lübbers, Lei Tian, Constantinus Politis, Michel Bila, Robin Willaert, Xiaojun Chen, Yi Sun
This study is undertaken to establish the accuracy and reliability of OrthoCalc, a 3D application designed for the evaluation of maxillary positioning. We registered target virtual planned models, maxillary models from pre-operative and post-operative CT scans, and post-operative intra-oral scans to a common reference system, allowing for digital evaluation. To assess rotational changes, we introduced
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A homogenized two-phase computational framework for meso- and macroscale blood flow simulations Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-02-16 Abhishek Karmakar, Greg W. Burgreen, Grant Rydquist, James F. Antaki
Owing to the complexity of physics linked with blood flow and its associated phenomena, appropriate modeling of the multi-constituent rheology of blood is of primary importance. To this effect, various kinds of computational fluid dynamic models have been developed, each with merits and limitations. However, when additional physics like thrombosis and embolization is included within the framework of
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Patient-specific non-invasive estimation of the aortic blood pressure waveform by ultrasound and tonometry Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-02-16 Shuran Zhou, Kai Xu, Yi Fang, Jordi Alastruey, Samuel Vennin, Jun Yang, Junli Wang, Lisheng Xu, Xiaocheng Wang, Steve E. Greenwald
Aortic blood pressure (ABP) is a more effective prognostic indicator of cardiovascular disease than peripheral blood pressure. A highly accurate algorithm for non-invasively deriving the ABP wave, based on ultrasonic measurement of aortic flow combined with peripheral pulse wave measurements, has been proposed elsewhere. However, it has remained at the proof-of-concept stage because it requires knowledge
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UCFN[sbnd]Net: Ulcerative colitis evaluation based on fine-grained lesion learner and noise suppression gating Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-02-15 Haiyan Li, Zhixin Wang, Zheng Guan, Jiarong Miao, Weihua Li, Pengfei Yu, Carlos Molina Jimenez
Ulcerative colitis (UC) is a chronic disease characterized by recurrent symptoms and significant morbidity. The exact cause of the disease remains unknown. The selection of current treatment options for ulcerative colitis depends on the severity and location of the disease in each patient. Therefore, developing a fully automated endoscopic images for evaluating UC is crucial for guiding treatment plans
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Unsupervised sequence-to-sequence learning for automatic signal quality assessment in multi-channel electrical impedance-based hemodynamic monitoring Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-02-15 Chang Min Hyun, Tae-Geun Kim, Kyounghun Lee
This study proposes an unsupervised sequence-to-sequence learning approach that automatically assesses the motion-induced reliability degradation of the cardiac volume signal (CVS) in multi-channel electrical impedance-based hemodynamic monitoring. The proposed method attempts to tackle shortcomings in existing learning-based assessment approaches, such as the requirement of manual annotation for motion
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A Laplacian regularized graph neural network for predictive modeling of multiple chronic conditions Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-02-13 Julian Carvajal Rico, Adel Alaeddini, Syed Hasib Akhter Faruqui, Susan P. Fisher-Hoch, Joseph B. Mccormick
Background and Goals: One of the biggest difficulties facing healthcare systems today is the prevalence of multiple chronic diseases (MCC). Mortality and the development of new chronic illnesses are more likely in those with MCC. Pre-existing diseases and risk factors specific to the patient have an impact on the complex stochastic process that guides the evolution of MCC. This study's goal is to use
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Scale resolving simulations of the effect of glottis motion and the laryngeal jet on flow dynamics during respiration Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-02-12 Jake Emmerling, Sara Vahaji, David A.V. Morton, David F. Fletcher, Kiao Inthavong
The movement of the respiratory walls has a significant impact on airflow through the respiratory tract. The majority of computational fluid dynamics (CFD) studies assume a static geometry which may not provide a realistic flow field. Furthermore, many studies use Reynolds Averaged Navier-Stokes (RANS) turbulence models that do not resolve turbulence structure. Combining the application of advanced
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A pathway-based computational framework for identification of a new modal of multi-omics biomarkers and its application in esophageal cancer Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-02-12 Qi Zhou, Weicai Ye, Xiaolan Yu, Yun-Juan Bao
The pathway-based strategy has been recently proposed for identifying biomarkers with the advantages of higher biological interpretability and cross-data robustness than the conventional gene-based strategy. However, its utility in clinical applications has been limited due to the high computational complexity and ill-defined performance. The current study presents a machine learning-based computational
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Modified Theoretical Model Predicts Radial Support Capacity of Polymer Braided Stents Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-02-10 Xue Hu, Qingwei Liu, Li Chen, Jie Cheng, Muqing Liu, Gensheng Wu, Renhua Sun, Gutian Zhao, Juekuan Yang, Zhonghua Ni
Self-expanding polymer braided stents are expected to replace metallic stents in the treatment of Peripheral Arterial Disease, which seriously endangers human health. To restore the patency of blocked peripheral arteries with different properties and functions, the radial supporting capacity of the stent should be considered corresponding to the vessel. A theoretical model can be established as an
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Automated anxiety detection using probabilistic binary pattern with ECG signals Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-02-10 Mehmet Baygin, Prabal Datta Barua, Sengul Dogan, Turker Tuncer, Tan Jen Hong, Sonja March, Ru-San Tan, Filippo Molinari, U. Rajendra Acharya
Anxiety disorder is common; early diagnosis is crucial for management. Anxiety can induce physiological changes in the brain and heart. We aimed to develop an efficient and accurate handcrafted feature engineering model for automated anxiety detection using ECG signals. We studied open-access electrocardiography (ECG) data of 19 subjects collected via wearable sensors while they were shown videos that
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CT-FEM of the human thorax: Frequency response function and 3D harmonic analysis at resonance Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-02-09 Arife Uzundurukan, Sébastien Poncet, Daria Camilla Boffito, Philippe Micheau
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LSTM-CNN: An efficient diagnostic network for Parkinson's disease utilizing dynamic handwriting analysis Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-02-09 Xuechao Wang, Junqing Huang, Marianna Chatzakou, Kadri Medijainen, Aaro Toomela, Sven Nõmm, Michael Ruzhansky
Dynamic handwriting analysis, due to its noninvasive and readily accessible nature, has emerged as a vital adjunctive method for the early diagnosis of Parkinson's disease (PD). An essential step involves analysing subtle variations in signals to quantify PD dysgraphia. Although previous studies have explored extracting features from the overall signal, they may ignore the potential importance of local
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A transformer-based diffusion probabilistic model for heart rate and blood pressure forecasting in Intensive Care Unit Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-02-08 Ping Chang, Huayu Li, Stuart F. Quan, Shuyang Lu, Shu-Fen Wung, Janet Roveda, Ao Li
Vital sign monitoring in the Intensive Care Unit (ICU) is crucial for enabling prompt interventions for patients. This underscores the need for an accurate predictive system. Therefore, this study proposes a novel deep learning approach for forecasting Heart Rate (HR), Systolic Blood Pressure (SBP), and Diastolic Blood Pressure (DBP) in the ICU. We extracted ICU stays from the MIMIC-III database which
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Automatic planning and geometric analysis of the drilling path in core decompression surgery for osteonecrosis of the femoral head Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-02-08 Jiping Zhang, Sijia Guo, Mingzhu Tao, Degang Yu, Cheng-Kung Cheng
Core decompression surgery is an effective treatment method for patients with pre-collapse osteonecrosis of the femoral head (ONFH). The treatment relies on accurately predrilling the wire into the necrotic lesion. However, the surgical planning of this drilling path remains unclear. This paper aims to develop a framework to automatically plan the drilling path and analyze its geometric parameters
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In silico identification of viral loads in cough-generated droplets – Seamless integrated analysis of CFPD-HCD-EWF Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-02-08 Hanyu Li, Nguyen Dang Khoa, Kazuki Kuga, Kazuhide Ito
Respiratory diseases caused by respiratory viruses have significantly threatened public health worldwide. This study presents a comprehensive approach to predict viral dynamics and the generation of stripped droplets within the mucus layer of the respiratory tract during coughing using a larynx-trachea-bifurcation (LTB) model. This study integrates computational fluid-particle dynamics (CFPD), host-cell
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A machine learning model for predicting acute exacerbation of in-home chronic obstructive pulmonary disease patients Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-02-07 Huiming Yin, Kun Wang, Ruyu Yang, Yanfang Tan, Qiang Li, Wei Zhu, Suzi Sung
This study utilized intelligent devices to remotely monitor patients with chronic obstructive pulmonary disease (COPD), aiming to construct and evaluate machine learning (ML) models that predict the probability of acute exacerbations of COPD (AECOPD). Patients diagnosed with COPD Group C/D at our hospital between March 2019 and June 2021 were enrolled in this study. The diagnosis of COPD Group C/D
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Implicit neural representations for unsupervised super-resolution and denoising of 4D flow MRI Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-02-07 Simone Saitta, Marcello Carioni, Subhadip Mukherjee, Carola-Bibiane Schönlieb, Alberto Redaelli
: 4D flow magnetic resonance imaging provides time-resolved blood flow velocity measurements, but suffers from limitations in spatio-temporal resolution and noise. In this study, we investigated the use of sinusoidal representation networks (SIRENs) to improve denoising and super-resolution of velocity fields measured by 4D flow MRI in the thoracic aorta.
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Human-airway surface mesh smoothing based on graph convolutional neural networks Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-02-06 Thao Thi Ho, Minh Tam Tran, Xinguang Cui, Ching-Long Lin, Stephen Baek, Woo Jin Kim, Chang Hyun Lee, Gong Yong Jin, Kum Ju Chae, Sanghun Choi
A detailed representation of the airway geometry in the respiratory system is critical for predicting precise airflow and pressure behaviors in computed tomography (CT)-image-based computational fluid dynamics (CFD). The CT-image-based geometry often contains artifacts, noise, and discontinuities due to the so-called stair step effect. Hence, an advanced surface smoothing is necessary. The existing
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Unraveling motor imagery brain patterns using explainable artificial intelligence based on Shapley values Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-01-30 Sergio Pérez-Velasco, Diego Marcos-Martínez, Eduardo Santamaría-Vázquez, Víctor Martínez-Cagigal, Selene Moreno-Calderón, Roberto Hornero
Background and objective. Motor imagery (MI) based brain-computer interfaces (BCIs) are widely used in rehabilitation due to the close relationship that exists between MI and motor execution (ME). However, the underlying brain mechanisms of MI remain not well understood. Most MI-BCIs use the sensorimotor rhythms elicited in the primary motor cortex (M1) and somatosensory cortex (S1), which consist
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ECGMiner: A flexible software for accurately digitizing ECG Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-02-03 Adolfo F. Santamónica, Rocío Carratalá-Sáez, Yolanda Larriba, Alberto Pérez-Castellanos, Cristina Rueda
The electrocardiogram (ECG) is the most important non-invasive method for elucidating information about heart and cardiovascular disease diagnosis. Typically, the ECG system manufacturing companies provide ECG images, but store the numerical data in a proprietary format that is not interpretable and is not therefore useful for automatic diagnosis. There have been many efforts to digitize paper-based
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Improving preliminary clinical diagnosis accuracy through knowledge filtering techniques in consultation dialogues Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-01-30 Ashu Abdul, Binghong Chen, Siginamsetty Phani, Jenhui Chen
Background and Objective Symptom descriptions by ordinary people are often inaccurate or vague when seeking medical advice, which often leads to inaccurate preliminary clinical diagnoses. To address this issue, we propose a deep learning model named the knowledgeable diagnostic transformer (KDT) for the natural language processing (NLP)-based preliminary clinical diagnoses. Methods The KDT extracts
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Fluid-structure interaction analysis of the thromboembolic risk in the left atrial appendage under atrial fibrillation: Effect of hemodynamics and morphological features Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-02-02 Giulio Musotto, Alessandra Monteleone, Danila Vella, Bernardo Zuccarello, Ruggero Cannova, Andrew Cook, Giorgia Maria Bosi, Gaetano Burriesci
Complications of atrial fibrillation (AF) include ischemic events originating within the left atrial appendage (LAA), a protrusion of the left atrium with variable morphological characteristics. The role of the patient specific morphology and pathological haemodynamics on the risk of ischemia remains unclear. This work performs a comparative assessment of the hemodynamic parameters among patient-specific
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A marker-based approach to determine the centers of rotation of finger joints Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-02-02 Martina Lapresa, Eugenio Guglielmelli, Loredana Zollo, Francesca Cordella
The methods proposed in literature to estimate the position of hand joints Centers of Rotation (CoRs) typically require computationally non-trivial optimization routines and exploit a high number of markers to calculate CoRs positions from surface marker trajectories. Moreover, most of the existing works evaluated the accuracy only in simulation. This work proposes a new procedure, based on the Pratt
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Non-invasive estimation of atrial fibrillation driver position using long-short term memory neural networks and body surface potentials Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-02-02 Miriam Gutiérrez-Fernández-Calvillo, Miguel Ángel Cámara-Vázquez, Ismael Hernández-Romero, María S. Guillem, Andreu M. Climent, Carlos Fambuena-Santos, Óscar Barquero-Pérez
Atrial Fibrillation (AF) is a supraventricular tachyarrhythmia that can lead to thromboembolism, hearlt failure, ischemic stroke, and a decreased quality of life. Characterizing the locations where the mechanisms of AF are initialized and maintained is key to accomplishing an effective ablation of the targets, hence restoring sinus rhythm. Many methods have been investigated to locate such targets
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A recurrent positional encoding circular attention mechanism network for biomedical image segmentation Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-02-02 Xiaoxia Yu, Yong Qin, Fanghong Zhang, Zhigang Zhang
Deep-learning-based medical image segmentation techniques can assist doctors in disease diagnosis and rapid treatment. However, existing medical image segmentation models do not fully consider the dependence between feature segments in the feature extraction process, and the correlated features can be further extracted. Therefore, a recurrent positional encoding circular attention mechanism network
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Convolutional neural network model for automatic recognition and classification of pancreatic cancer cell based on analysis of lipid droplet on unlabeled sample by 3D optical diffraction tomography Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-01-26 Seok Jin Hong, Jong-Uk Hou, Moon Jae Chung, Sung Hun Kang, Bo-Seok Shim, Seung-Lee Lee, Da Hae Park, Anna Choi, Jae Yeon Oh, Kyong Joo Lee, Eun Shin, Eunae Cho, Se Woo Park
INTRODUCTION Pancreatic cancer cells generally accumulate large numbers of lipid droplets (LDs), which regulate lipid storage. To promote rapid diagnosis, an automatic pancreatic cancer cell recognition system based on a deep convolutional neural network was proposed in this study using quantitative images of LDs from stain-free cytologic samples by optical diffraction tomography. METHODS We retrieved