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A new high-throughput screening methodology for the discovery of cancer-testis antigen using multi-omics data Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-04-25 Dandan Li, Lingyun Xia, Xiangang Zhang, Yue Liu, Zidi Wang, Qiwei Guo, Pan Huang, Weidong Leng, Shanshan Qin
Cancer/testis antigens (CTAs), also known as tumor-specific antigens (TSAs) are specifically expressed in cancer cells and exhibit high immunogenicity, making them promising targets for immunotherapy and cancer vaccines. A new integrated high-throughput screening methodology for CTAs was proposed in this study through combining DNA methylation and RNA sequencing data. Briefly, the genes with increased
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Exploring the disruptions of the neurophysiological organization in Alzheimer’s disease: An integrative approach Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-04-24 Víctor Gutiérrez-de Pablo, Jesús Poza, Aarón Maturana-Candelas, Víctor Rodríguez-González, Miguel Ángel Tola-Arribas, Mónica Cano, Hideyuki Hoshi, Yoshihito Shigihara, Roberto Hornero, Carlos Gómez
Alzheimer’s disease (AD) is a neurological disorder that impairs brain functions associated with cognition, memory, and behavior. Noninvasive neurophysiological techniques like magnetoencephalography (MEG) and electroencephalography (EEG) have shown promise in reflecting brain changes related to AD. These techniques are usually assessed at two levels: local activation (spectral, nonlinear, and dynamic
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Autism spectrum disorder diagnosis with EEG signals using time series maps of brain functional connectivity and a combined CNN–LSTM model Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-04-24 Yongjie Xu, Zengjie Yu, Yisheng Li, Yuehan Liu, Ye Li, Yishan Wang
People with autism spectrum disorder (ASD) often have cognitive impairments. Effective connectivity between different areas of the brain is essential for normal cognition. Electroencephalography (EEG) has been widely used in the detection of neurological diseases. Previous studies on detecting ASD with EEG data have focused on frequency-related features. Most of these studies have augmented data by
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Facial expressions to identify post-stroke: A pilot study Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-04-24 Guilherme C. Oliveira, Quoc C. Ngo, Leandro A. Passos, Leonardo S. Oliveira, João P. Papa, Dinesh Kumar
Timely stroke treatment can limit brain damage and improve outcomes, which depends on early recognition of the symptoms. However, stroke cases are often missed by the first respondent paramedics. One of the earliest external symptoms of stroke is based on facial expressions. We propose a computerized analysis of facial expressions using action units to distinguish between Post-Stroke and healthy people
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Physiological and chaos effect on dynamics of neurological disorder with memory effect of fractional operator: A mathematical study Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-04-24 Anum Zehra, Parvaiz Ahmad Naik, Ali Hasan, Muhammad Farman, Kottakkaran Sooppy Nisar, Faryal Chaudhry, Zhengxin Huang
To study the dynamical system, it is necessary to formulate the mathematical model to understand the dynamics of various diseases that are spread worldwide. The main objective of our work is to examine neurological disorders by early detection and treatment by taking asymptomatic. The central nervous system (CNS) is impacted by the prevalent neurological condition known as multiple sclerosis (MS),
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All you need is data preparation: A systematic review of image harmonization techniques in Multi-center/device studies for medical support systems Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-04-23 Silvia Seoni, Alen Shahini, Kristen M. Meiburger, Francesco Marzola, Giulia Rotunno, U. Rajendra Acharya, Filippo Molinari, Massimo Salvi
Artificial intelligence (AI) models trained on multi-centric and multi-device studies can provide more robust insights and research findings compared to single-center studies. However, variability in acquisition protocols and equipment can introduce inconsistencies that hamper the effective pooling of multi-source datasets. This systematic review evaluates strategies for image harmonization, which
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Lesion attention guided neural network for contrast-enhanced mammography-based biomarker status prediction in breast cancer Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-04-22 Nini Qian, Wei Jiang, Xiaoqian Wu, Ning Zhang, Hui Yu, Yu Guo
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Integration of multi-omics data for survival prediction of lung adenocarcinoma Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-04-22 Dingjie Guo, Yixian Wang, Jing Chen, Xin Liu
The morbidity of lung adenocarcinoma (LUAD) has been increasing year by year and the prognosis is poor. This has prompted researchers to study the survival of LUAD patients to ensure that patients can be cured in time or survive after appropriate treatment. There is still no fully valid model that can be applied to clinical practice. We introduced struc2vec-based multi-omics data integration (SBMOI)
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RegWSI: Whole slide image registration using combined deep feature- and intensity-based methods: Winner of the ACROBAT 2023 challenge Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-04-22 Marek Wodzinski, Niccolò Marini, Manfredo Atzori, Henning Müller
The automatic registration of differently stained whole slide images (WSIs) is crucial for improving diagnosis and prognosis by fusing complementary information emerging from different visible structures. It is also useful to quickly transfer annotations between consecutive or restained slides, thus significantly reducing the annotation time and associated costs. Nevertheless, the slide preparation
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A protein-protein interaction network aligner study in the multi-objective domain Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-04-21 Manuel Menor-Flores, Miguel A. Vega-Rodríguez
The protein-protein interaction (PPI) network alignment has proven to be an efficient technique in the diagnosis and prevention of certain diseases. However, the difficulty in maximizing, at the same time, the two qualities that measure the goodness of alignments (topological and biological quality) has led aligners to produce very different alignments. Thus making a comparative study among alignments
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VENet: Variational energy network for gland segmentation of pathological images and early gastric cancer diagnosis of whole slide images Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-04-21 Shuchang Zhang, Ziyang Yuan, Xianchen Zhou, Hongxia Wang, Bo Chen, Yadong Wang
Background and objective: Gland segmentation of pathological images is an essential but challenging step for adenocarcinoma diagnosis. Although deep learning methods have recently made tremendous progress in gland segmentation, they have not given satisfactory boundary and region segmentation results of adjacent glands. These glands usually have a large difference in glandular appearance, and the statistical
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Hemodynamics of ventricular-arterial coupling under enhanced external counterpulsation: An optimized dual-source lumped parameter model Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-04-20 Sheng-Fu Liao, Yong-Jiang Li, Sen Cao, Chun-Dong Xue, Shuai Tian, Gui-Fu Wu, Xiao-Ming Chen, Dong Chen, Kai-Rong Qin
Enhanced external counterpulsation (EECP) is a mechanically assisted circulation technique widely used in the rehabilitation and management of ischemic cardiovascular diseases. It contributes to cardiovascular functions by regulating the afterload of ventricle to improve hemodynamic effects, including increased diastolic blood pressure at aortic root, increased cardiac output and enhanced blood perfusion
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The impact of small movements with dual lumen cannulae during venovenous extracorporeal membrane oxygenation: A computational fluid dynamics analysis Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-04-18 Zhun Yung Wong, Marjan Azimi, Mehrdad Khamooshi, Avishka Wickramarachchi, Aidan Burrell, Shaun D Gregory
Venovenous Extracorporeal Membrane Oxygenation (VV ECMO) provides respiratory support to patients with severe lung disease failing conventional medical therapy. An essential component of the ECMO circuit are the cannulas, which drain and return blood into the body. Despite being anchored to the patient to prevent accidental removal, minor cannula movements are common during ECMO. The clinical and haemodynamic
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A strategy based on integer programming for optimal dosing and timing of preventive hypoglycemic treatments in type 1 diabetes management Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-04-18 J. Pavan, G. Noaro, A. Facchinetti, D. Salvagnin, G. Sparacino, S. Del Favero
One of the major problems related to type 1 diabetes (T1D) management is hypoglycemia, a condition characterized by low blood glucose levels and responsible for reduced quality of life and increased mortality. Fast-acting carbohydrates, also known as hypoglycemic treatments (HT), can counteract this event. In the literature, dosage and timing of HT are usually based on heuristic rules. In the present
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To what extent can mastication functionality be restored following mandibular reconstruction surgery? A computer modeling approach Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-04-17 Hamidreza Aftabi, Benedikt Sagl, John E. Lloyd, Eitan Prisman, Antony Hodgson, Sidney Fels
Advanced cases of head and neck cancer involving the mandible often require surgical removal of diseased sections and subsequent replacement with donor bone. During the procedure, the surgeon must make decisions regarding which bones or tissues to resect. This requires balancing tradeoffs related to issues such as surgical access and post-operative function; however, the latter is often difficult to
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Brain tumor detection using proper orthogonal decomposition integrated with deep learning networks Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-04-15 Rita Appiah, Venkatesh Pulletikurthi, Helber Antonio Esquivel-Puentes, Cristiano Cabrera, Nahian I. Hasan, Suranga Dharmarathne, Luis J. Gomez, Luciano Castillo
The central organ of the human nervous system is the brain, which receives and sends stimuli to the various parts of the body to engage in daily activities. Uncontrolled growth of brain cells can result in tumors which affect the normal functions of healthy brain cells. An automatic reliable technique for detecting tumors is imperative to assist medical practitioners in the timely diagnosis of patients
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HRU-Net: A high-resolution convolutional neural network for esophageal cancer radiotherapy target segmentation Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-04-14 Muwei Jian, Chen Tao, Ronghua Wu, Haoran Zhang, Xiaoguang Li, Rui Wang, Yanlei Wang, Lizhi Peng, Jian Zhu
The effective segmentation of esophageal squamous carcinoma lesions in CT scans is significant for auxiliary diagnosis and treatment. However, accurate lesion segmentation is still a challenging task due to the irregular form of the esophagus and small size, the inconsistency of spatio-temporal structure, and low contrast of esophagus and its peripheral tissues in medical images. The objective of this
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Analysis of gait pattern related to high cerebral small vessel disease burden using quantitative gait data from wearable sensors Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-04-13 Kelin Xu, Yingzhe Wang, Yanfeng Jiang, Yawen Wang, Peixi Li, Heyang Lu, Chen Suo, Ziyu Yuan, Qi Yang, Qiang Dong, Li Jin, Mei Cui, Xingdong Chen
Sensor-based wearable devices help to obtain a wide range of quantitative gait parameters, which provides sufficient data to investigate disease-specific gait patterns. Although cerebral small vessel disease (CSVD) plays a significant role in gait impairment, the specific gait pattern associated with a high burden of CSVD remains to be explored. We analyzed the gait pattern related to high CSVD burden
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UsIL-6: An unbalanced learning strategy for identifying IL-6 inducing peptides by undersampling technique Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-04-12 Yan-hong Liao, Shou-zhi Chen, Yan-nan Bin, Jian-ping Zhao, Xin-long Feng, Chun-hou Zheng
Interleukin-6 (IL-6) is the critical factor of early warning, monitoring, and prognosis in the inflammatory storm of COVID-19 cases. IL-6 inducing peptides, which can induce cytokine IL-6 production, are very important for the development of diagnosis and immunotherapy. Although the existing methods have some success in predicting IL-6 inducing peptides, there is still room for improvement in the performance
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An adversarial learning approach to generate pressure support ventilation waveforms for asynchrony detection Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-04-12 L. Hao, T.H.G.F. Bakkes, A. van Diepen, N. Chennakeshava, R.A. Bouwman, A.J.R. De Bie Dekker, P.H. Woerlee, F. Mojoli, M. Mischi, Y. Shi, S. Turco
Mechanical ventilation is a life-saving treatment for critically-ill patients. During treatment, patient-ventilator asynchrony (PVA) can occur, which can lead to pulmonary damage, complications, and higher mortality. While traditional detection methods for PVAs rely on visual inspection by clinicians, in recent years, machine learning models are being developed to detect PVAs automatically. However
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Effect of patient-specific factors on regeneration in lumbar spine at healthy disc and total disc replacement. Computer simulation Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-04-11 Galina M. Eremina, Alexey Yu. Smolin
Degenerative diseases of the spine have a negative impact on the quality of life of patients. This study presents the results of numerical modelling of the mechanical behaviour of the lumbar spine with patient-specific conditions at physiological loads. This paper aims to numerically study the influence of degenerative changes in the spine and the presence of an endoprosthesis on the creation of conditions
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Mechanical behaviors of a new elliptical valve stent in bicuspid aortic valve Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-04-11 Xiang Shen, Yue Xu, Hanqing Li, Lei Wang, Peng Sun, Qiang Liu, Jiahao Chen, Zewen He
The conventional valve stents that are cylindrical in shape will become elliptical when implanted in bicuspid aortic valve, thereby reducing the durability of the artificial valve. In this study, a new design of valve stent is presented where valve stents have elliptical cross-section at the annulus and it is expected to have better expandability and circle shape during the interaction between the
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Real-time soft body dissection simulation with parallelized graph-based shape matching on GPU Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-04-11 Peng Yu, Zhiyuan Zhao, Ruiqi Wang, Junjun Pan
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SIMUS3: An open-source simulator for 3-D ultrasound imaging Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-04-10 Damien Garcia, François Varray
Computational Ultrasound Imaging (CUI) has become increasingly popular in the medical ultrasound community, facilitated by free simulation software. These tools enable the design and exploration of transmit sequences, transducer arrays, and signal processing. We recently introduced SIMUS, a frequency-based ultrasound simulator within the open-source MUST toolbox, which offers numerical advantages and
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Predicting adverse long-term neurocognitive outcomes after pediatric intensive care unit admission Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-04-10 Felipe Kenji Nakano, Karolijn Dulfer, Ilse Vanhorebeek, Pieter J. Wouters, Sascha C. Verbruggen, Koen F. Joosten, Fabian Güiza Grandas, Celine Vens, Greet Van den Berghe
Critically ill children may suffer from impaired neurocognitive functions years after ICU (intensive care unit) discharge. To assess neurocognitive functions, these children are subjected to a fixed sequence of tests. Undergoing all tests is, however, arduous for former pediatric ICU patients, resulting in interrupted evaluations where several neurocognitive deficiencies remain undetected. As a solution
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Cost function criteria using muscle synergies: Exploring the potential of muscle synergy hypothesis Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-04-09 Haoran Li, Qiguo Rong
Solving the redundant optimization problem for human muscles depends on the cost function. Choosing the appropriate cost function helps to address a specific problem. Muscle synergies are currently limited to those obtained by electromyography. Furthermore, debate continues regarding whether muscle synergy is derived or real. This study proposes new cost functions based on the muscle synergy hypothesis
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Dual-space high-frequency learning for transformer-based MRI super-resolution Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-04-09 Haoneng Lin, Jing Zou, Kang Wang, Yidan Feng, Cheng Xu, Jun Lyu, Jing Qin
Magnetic resonance imaging (MRI) can provide rich and detailed high-contrast information of soft tissues, while the scanning of MRI is time-consuming. To accelerate MR imaging, a variety of Transformer-based single image super-resolution methods are proposed in recent years, achieving promising results thanks to their superior capability of capturing long-range dependencies. Nevertheless, most existing
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Exploring machine learning for untargeted metabolomics using molecular fingerprints Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-04-08 Christel Sirocchi, Federica Biancucci, Matteo Donati, Alessandro Bogliolo, Mauro Magnani, Michele Menotta, Sara Montagna
Metabolomics, the study of substrates and products of cellular metabolism, offers valuable insights into an organism's state under specific conditions and has the potential to revolutionise preventive healthcare and pharmaceutical research. However, analysing large metabolomics datasets remains challenging, with available methods relying on limited and incompletely annotated metabolic pathways. This
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ProDiv: Prototype-driven consistent pseudo-bag division for whole-slide image classification Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-04-08 Rui Yang, Pei Liu, Luping Ji
Pathology image classification is one of the most essential auxiliary processes in cancer diagnosis. To overcome the problem of inadequate Whole-Slide Image (WSI) samples with weak labels, pseudo-bag-based multiple instance learning (MIL) methods have attracted wide attention in pathology image classification. In this type of method, the division scheme of pseudo-bags is usually a primary factor affecting
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Novel hybrid rigid-deformable fetal modeling for simulating the vaginal delivery within the second stage of labor Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-04-08 Abbass Ballit, Morgane Ferrandini, Tien-Tuan Dao
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4D-Precise: Learning-based 3D motion estimation and high temporal resolution 4DCT reconstruction from treatment 2D+t X-ray projections Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-04-04 Arezoo Zakeri, Alireza Hokmabadi, Michael G. Nix, Ali Gooya, Isuru Wijesinghe, Zeike A. Taylor
In radiotherapy treatment planning, respiration-induced motion introduces uncertainty that, if not appropriately considered, could result in dose delivery problems. 4D cone-beam computed tomography (4D-CBCT) has been developed to provide imaging guidance by reconstructing a pseudo-motion sequence of CBCT volumes through binning projection data into breathing phases. However, it suffers from artefacts
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Attention-based deep learning framework for automatic fundus image processing to aid in diabetic retinopathy grading Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-04-03 Roberto Romero-Oraá, María Herrero-Tudela, María I. López, Roberto Hornero, María García
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Machine learning based detection of T–wave alternans in real ambulatory conditions Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-04-03 Lidia Pascual-Sánchez, Rebeca Goya-Esteban, Fernando Cruz-Roldán, Antonio Hernández-Madrid, Manuel Blanco-Velasco
T-wave alternans (TWA) is a fluctuation in the repolarization morphology of the ECG. It is associated with cardiac instability and sudden cardiac death risk. Diverse methods have been proposed for TWA analysis. However, TWA detection in ambulatory settings remains a challenge due to the absence of standardized evaluation metrics and detection thresholds. In this work we use traditional TWA analysis
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SAGL: A self-attention-based graph learning framework for predicting survival of colorectal cancer patients Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-04-02 Ping Yang, Hang Qiu, Xulin Yang, Liya Wang, Xiaodong Wang
: Colorectal cancer (CRC) is one of the most commonly diagnosed cancers worldwide. The accurate survival prediction for CRC patients plays a significant role in the formulation of treatment strategies. Recently, machine learning and deep learning approaches have been increasingly applied in cancer survival prediction. However, most existing methods inadequately represent and leverage the dependencies
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Computational fluid-structure interaction analysis of the end-to-side radio-cephalic arteriovenous fistula Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-04-02 Fabio Marcinnò, Christian Vergara, Luca Giovannacci, Alfio Quarteroni, Giorgio Prouse
In the current work, we present a descriptive fluid-structure interaction computational study of the end-to-side radio-cephalic arteriovenous fistula. This allows us to account for the different thicknesses and elastic properties of the radial artery and cephalic vein. The core of the work consists in simulating different arteriovenous fistula configurations obtained by virtually varying the anastomosis
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Multicentric intelligent cardiotocography signal interpretation using deep semi-supervised domain adaptation via minimax entropy and domain invariance Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-04-02 Jialu Li, Jun Li, Chenshuo Guo, Qinqun Chen, Guiqing Liu, Li Li, Xiaomu Luo, Hang Wei
Obstetricians use Cardiotocography (CTG), which is the continuous recording of fetal heart rate and uterine contraction, to assess fetal health status. Deep learning models for intelligent fetal monitoring trained on extensively labeled and identically distributed CTG records have achieved excellent performance. However, creation of these training sets requires excessive time and specialist labor for
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A hybrid robotic system for zygomatic implant placement based on mixed reality navigation Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-03-27 Xingqi Fan, Yuan Feng, Baoxin Tao, Yihan Shen, Yiqun Wu, Xiaojun Chen
Zygomatic implant (ZI) placement surgery is a viable surgical option for patients with severe maxillary atrophy and insufficient residual maxillary bone. Still, it is difficult and risky due to the long path of ZI placement and the narrow field of vision. Dynamic navigation is a superior solution, but it presents challenges such as requiring operators to have advanced skills and experience. Moreover
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Performance improvement of atherosclerosis risk assessment based on feature interaction Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-03-26 Mengdie Yang, Lidan He, Wenjun Liu, Yudong Zhang, Hui Huang
Cardiovascular disease is a leading cause of mortality and premature death. Early intervention in asymptomatic individuals through risk assessment can reduce the incidence of disease. Atherosclerosis is a major cause of cardiovascular disease and early detection can effectively prevent and treat it. In this study, we used real patient data to evaluate the risk of atherosclerosis, assisting doctors
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FedDUS: Lung tumor segmentation on CT images through federated semi-supervised with dynamic update strategy Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-03-25 Dan Wang, Chu Han, Zhen Zhang, Tiantian Zhai, Huan Lin, Baoyao Yang, Yanfen Cui, Yinbing Lin, Zhihe Zhao, Lujun Zhao, Changhong Liang, An Zeng, Dan Pan, Xin Chen, Zhenwei Shi, Zaiyi Liu
Lung tumor annotation is a key upstream task for further diagnosis and prognosis. Although deep learning techniques have promoted automation of lung tumor segmentation, there remain challenges impeding its application in clinical practice, such as a lack of prior annotation for model training and data-sharing among centers. In this paper, we use data from six centers to design a novel federated semi-supervised
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Assessing the impact of tear direction in coronary artery dissection on thrombosis development: A hemodynamic computational study Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-03-24 Yan Pei, Pan Song, Kaiyue Zhang, Min Dai, Gang He, Jun Wen
Iatrogenic coronary artery dissection is a complication of coronary intimal injury and dissection due to improper catheter manipulation. The impact of tear direction on the prognosis of coronary artery dissection (CAD) remains unclear. This study examines the hemodynamic effects of different tear directions (transverse and longitudinal) of CAD and evaluates the risk of thrombosis, rupture and further
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Development of a Probabilistic Boolean network (PBN) to model intraoperative blood pressure management Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-03-24 Chamara Gunaratne, Ron Ison, Catherine C. Price, Francois Modave, Patrick Tighe
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Design and development of a personalized virtual reality-based training system for vascular intervention surgery Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-03-21 Pan Li, Boxuan Xu, Xinxin Zhang, Delei Fang, Junxia Zhang
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DGCBG-Net: A dual-branch network with global cross-modal interaction and boundary guidance for tumor segmentation in PET/CT images Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-03-20 Ziwei Zou, Beiji Zou, Xiaoyan Kui, Zhi Chen, Yang Li
Automatic tumor segmentation plays a crucial role in cancer diagnosis and treatment planning. Computed tomography (CT) and positron emission tomography (PET) are extensively employed for their complementary medical information. However, existing methods ignore bilateral cross-modal interaction of global features during feature extraction, and they underutilize multi-stage tumor boundary features. To
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Disease X epidemic control using a stochastic model and a deterministic approximation: Performance comparison with and without parameter uncertainties Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-03-20 Julien Flaig, Nicolas Houy
The spread of infectious diseases can be modeled using deterministic or stochastic models. A deterministic approximation of a stochastic model can be appropriate under some conditions, but is unable to capture the discrete nature of populations. We look into the choice of a model from the perspective of decision making.
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Agent-based systems in healthcare Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-03-19 Sara Montagna, Stefano Mariani, Michael I. Schumacher, Gaetano Manzo
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Patient-specific left atrium contraction quantification associated with atrial fibrillation: A region-based approach Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-03-19 Sachal Hussain, Matteo Falanga, Antonio Chiaravalloti, Corrado Tomasi, Cristiana Corsi
Atrial fibrillation (AF) is a widespread cardiac arrhythmia that significantly impacts heart function. AF disrupts atrial mechanical contraction, leading to irregular, uncoordinated, and slow blood flow inside the atria which favors the formation of clots, primarily within the left atrium (LA). A standardized region-based analysis of the LA is missing, and there is not even any consensus about how
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CPhaMAS: An online platform for pharmacokinetic data analysis based on optimized parameter fitting algorithm Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-03-19 Yun Kuang, Dong-sheng Cao, Yong-hui Zuo, Jing-han Yuan, Feng Lu, Yi Zou, Hong Wang, Dan Jiang, Qi Pei, Guo-ping Yang
Clinical pharmacological modeling and statistical analysis software is an essential basic tool for drug development and personalized drug therapy. The learning curve of current basic tools is steep and unfriendly to beginners. The curve is even more challenging in cases of significant individual differences or measurement errors in data, resulting in difficulties in accurately estimating pharmacokinetic
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Immunotherapy efficacy prediction through a feature re-calibrated 2.5D neural network Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-03-18 Haipeng Xu, Chenxin Li, Longfeng Zhang, Zhiyuan Ding, Tao Lu, Huihua Hu
Lung cancer continues to be a leading cause of cancer-related mortality worldwide, with immunotherapy emerging as a promising therapeutic strategy for advanced non-small cell lung cancer (NSCLC). Despite its potential, not all patients experience benefits from immunotherapy, and the current biomarkers used for treatment selection possess inherent limitations. As a result, the implementation of imaging-based
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A novel feature-level fusion scheme with multimodal attention CNN for heart sound classification Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-03-15 Kalpeshkumar Ranipa, Wei-Ping Zhu, M.N.S. Swamy
Most of the existing machine learning-based heart sound classification methods achieve limited accuracy. Since they primarily depend on single domain feature information and tend to focus equally on each part of the signal rather than employing a selective attention mechanism. In addition, they fail to exploit convolutional neural network (CNN) - based features with an effective fusion strategy. In
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SC-Net: Symmetrical conical network for colorectal pathology image segmentation Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-03-13 Gang Zhang, Zifen He, Yinhui Zhang, Zhenhui Li, Lin Wu
Image segmentation of histopathology of colorectal cancer is a core task of computer aided medical image diagnosis system. Existing convolutional neural networks generally extract multi-scale information in linear flow structures by inserting multi-branch modules, which is difficult to extract heterogeneous semantic information under multi-level and different receptive field and tough to establish
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Breaking presence in Immersive Virtual Reality toward behavioral and emotional engagement Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-03-13 Oscar I. Caldas, Mauricio Mauledoux, Oscar F. Aviles, Carlos Rodriguez-Guerrero
Many recent studies in virtual reality (VR) have managed the sense of Presence to assess the suitability of their designs, mainly when focused on learning goals that require high user engagement, such as in serious games for psychomotor training. However, the place and plausibility illusions needed to promote Presence are achieved by combining different VR-based design cues, and their individual contribution
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Estimation of the in-plane ultimate stress of lamellar tissue as a function of bone mineral density and osteocyte lacunae porosity Comput. Methods Programs Biomed. (IF 6.1) Pub Date : 2024-03-13 Ana Vercher-Martínez, Raquel Megías, Ricardo Belda, Pablo Vargas, Eugenio Giner
Detailed finite element models based on medical images (-CT) are commonly used to analyze the mechanical behavior of bone at microscale. In order to simulate the tissue failure onset, isotropic failure criteria of lamellar tissue are often used, despite its non-isotropic and heterogeneous nature. The main goal of the present work is to estimate the in-plane ultimate stress of lamellar bone, considering
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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) rupture has increased substantially over recent decades. Following ACL rupture, ACL reconstruction (ACLR) surgery is typically performed to restore passive knee stability. 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)
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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, Jürgen Weese, Josien Pluim, Marcel Breeuwer
As large sets of annotated MRI data are needed for training and validating deep learning based medical image analysis algorithms, the lack of sufficient annotated data is a critical problem. A possible solution is the generation of artificial data by means of physics-based simulations. Existing brain simulation data is limited in terms of anatomical models, tissue classes, fixed tissue characteristics
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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