BioMedical Engineering OnLine

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List of Papers (Total 3,312)

A neural network with a human learning paradigm for breast fibroadenoma segmentation in sonography

Breast fibroadenoma poses a significant health concern, particularly for young women. Computer-aided diagnosis has emerged as an effective and efficient method for the early and accurate detection of various solid tumors. Automatic segmentation of the breast fibroadenoma is important and potentially reduces unnecessary biopsies, but challenging due to the low image quality and...

Unsupervised corneal contour extraction algorithm with shared model for dynamic deformation videos: improving accuracy and noise resistance

In this study, an automatic corneal contour extraction algorithm with a shared model is developed to extract contours from dynamic corneal videos containing noise, which improves the accuracy of corneal biomechanical evaluation and clinical diagnoses. The algorithm does not require manual labeling and completes the unsupervised semantic segmentation of each frame in corneal...

Bioelectricity in dental medicine: a narrative review

Bioelectric signals, whether exogenous or endogenous, play crucial roles in the life processes of organisms. Recently, the significance of bioelectricity in the field of dentistry is steadily gaining greater attention. This narrative review aims to comprehensively outline the theory, physiological effects, and practical applications of bioelectricity in dental medicine and to...

Cycling using functional electrical stimulation therapy to improve motor function and activity in post-stroke individuals in early subacute phase: a systematic review with meta-analysis

Stroke necessitates interventions to rehabilitate individuals with disabilities, and the application of functional electrical stimulation therapy (FEST) has demonstrated potential in this regard. This study aimed to analyze the efficacy and effectiveness of cycling using FEST to improve motor function and lower limb activity in post-stroke individuals. We performed a systematic...

Effect of test duration and sensor location on the reliability of standing balance parameters derived using body-mounted accelerometers

Balance parameters derived from wearable sensor measurements during postural sway have been shown to be sensitive to experimental variables such as test duration, sensor number, and sensor location that influence the magnitude and frequency-related properties of measured center-of-mass (COM) and center-of-pressure (COP) excursions. In this study, we investigated the effects of...

RAGE plays key role in diabetic retinopathy: a review

RAGE is a multiligand receptor for the immunoglobulin superfamily of cell surface molecules and is expressed in Müller cells, vascular endothelial cells, nerve cells and RPE cells of the retina. Diabetic retinopathy (DR) is a multifactorial disease associated with retinal inflammation and vascular abnormalities and is the leading cause of vision loss or impairment in older or...

A weakly supervised deep learning model integrating noncontrasted computed tomography images and clinical factors facilitates haemorrhagic transformation prediction after intravenous thrombolysis in acute ischaemic stroke patients

Haemorrhage transformation (HT) is a serious complication of intravenous thrombolysis (IVT) in acute ischaemic stroke (AIS). Accurate and timely prediction of the risk of HT before IVT may change the treatment decision and improve clinical prognosis. We aimed to develop a deep learning method for predicting HT after IVT for AIS using noncontrast computed tomography (NCCT) images...

Artificial intelligence in glaucoma: opportunities, challenges, and future directions

Artificial intelligence (AI) has shown excellent diagnostic performance in detecting various complex problems related to many areas of healthcare including ophthalmology. AI diagnostic systems developed from fundus images have become state-of-the-art tools in diagnosing retinal conditions and glaucoma as well as other ocular diseases. However, designing and implementing AI models...

Assessment of the functional severity of coronary lesions from optical coherence tomography based on ensembled learning

Atherosclerosis is one of the most frequent cardiovascular diseases. The dilemma faced by physicians is whether to treat or postpone the revascularization of lesions that fall within the intermediate range given by an invasive fractional flow reserve (FFR) measurement. The paper presents a monocentric study for lesions significance assessment that can potentially cause ischemia...

Wireless capsule endoscopy multiclass classification using three-dimensional deep convolutional neural network model

Wireless capsule endoscopy (WCE) is a patient-friendly and non-invasive technology that scans the whole of the gastrointestinal tract, including difficult-to-access regions like the small bowel. Major drawback of this technology is that the visual inspection of a large number of video frames produced during each examination makes the physician diagnosis process tedious and prone...

Radiotranscriptomics of non-small cell lung carcinoma for assessing high-level clinical outcomes using a machine learning-derived multi-modal signature

Multi-omics research has the potential to holistically capture intra-tumor variability, thereby improving therapeutic decisions by incorporating the key principles of precision medicine. The purpose of this study is to identify a robust method of integrating features from different sources, such as imaging, transcriptomics, and clinical data, to predict the survival and therapy...

Prediction of non-perfusion volume ratio for uterine fibroids treated with ultrasound-guided high-intensity focused ultrasound based on MRI radiomics combined with clinical parameters

Prediction of non-perfusion volume ratio (NPVR) is critical in selecting patients with uterine fibroids who will potentially benefit from ultrasound-guided high-intensity focused ultrasound (HIFU) treatment, as it reduces the risk of treatment failure. The purpose of this study is to construct an optimal model for predicting NPVR based on T2-weighted magnetic resonance imaging...

Effects of vibration therapy for post-stroke spasticity: a systematic review and meta-analysis of randomized controlled trials

The efficacy of vibration therapy (VT) in people with post-stroke spasticity (PSS) remains uncertain. This study aims to conduct a comprehensive meta-analysis to assess the effectiveness of VT in PSS. PubMed, Embase, Cochrane Library, Physiotherapy Evidence Database, and Web of Science were searched from inception to October 2022 for randomized controlled trials (RCTs) of VT in...

Comparison of plugin and redundant marker sets to analyze gait kinematics between different populations

Gait model consists of a marker set and a segment pose estimation algorithm. Plugin marker set and inverse kinematic algorithm (IK.) are prevalent in gait analysis, especially musculoskeletal motion analysis. Adding extra markers for the plugin marker set could increase the robustness to marker misplacement, motion artifacts, and even markers occlusion. However, how the different...

Evaluating the ability of a predictive vision-based machine learning model to measure changes in gait in response to medication and DBS within individuals with Parkinson’s disease

Gait impairments in Parkinson’s disease (PD) are treated with dopaminergic medication or deep-brain stimulation (DBS), although the magnitude of the response is variable between individuals. Computer vision-based approaches have previously been evaluated for measuring the severity of parkinsonian gait in videos, but have not been evaluated for their ability to identify changes...

Selective peripheral nerve recording using simulated human median nerve activity and convolutional neural networks

It is difficult to create intuitive methods of controlling prosthetic limbs, often resulting in abandonment. Peripheral nerve interfaces can be used to convert motor intent into commands to a prosthesis. The Extraneural Spatiotemporal Compound Action Potentials Extraction Network (ESCAPE-NET) is a convolutional neural network (CNN) that has previously been demonstrated to be...

Development of early prediction model of in-hospital cardiac arrest based on laboratory parameters

In-hospital cardiac arrest (IHCA) is an acute disease with a high fatality rate that burdens individuals, society, and the economy. This study aimed to develop a machine learning (ML) model using routine laboratory parameters to predict the risk of IHCA in rescue-treated patients. This retrospective cohort study examined all rescue-treated patients hospitalized at the First...

Deep learning-driven multi-view multi-task image quality assessment method for chest CT image

Chest computed tomography (CT) image quality impacts radiologists’ diagnoses. Pre-diagnostic image quality assessment is essential but labor-intensive and may have human limitations (fatigue, perceptual biases, and cognitive biases). This study aims to develop and validate a deep learning (DL)-driven multi-view multi-task image quality assessment (M $$^2$$ IQA) method for...

Diagnostic test accuracy of machine learning algorithms for the detection intracranial hemorrhage: a systematic review and meta-analysis study

This systematic review and meta-analysis were conducted to objectively evaluate the evidence of machine learning (ML) in the patient diagnosis of Intracranial Hemorrhage (ICH) on computed tomography (CT) scans. Until May 2023, systematic searches were conducted in ISI Web of Science, PubMed, Scopus, Cochrane Library, IEEE Xplore Digital Library, CINAHL, Science Direct, PROSPERO...

Classification of lung pathologies in neonates using dual-tree complex wavelet transform

Undiagnosed and untreated lung pathologies are among the leading causes of neonatal deaths in developing countries. Lung Ultrasound (LUS) has been widely accepted as a diagnostic tool for neonatal lung pathologies due to its affordability, portability, and safety. However, healthcare institutions in developing countries lack well-trained clinicians to interpret LUS images, which...

Integrated particle image velocimetry and fluid–structure interaction analysis for patient-specific abdominal aortic aneurysm studies

Understanding the hemodynamics of an abdominal aortic aneurysm (AAA) is crucial for risk assessment and treatment planning. This study introduces a low-cost, patient-specific in vitro AAA model to investigate hemodynamics using particle image velocimetry (PIV) and flow-simulating circuit, validated through fluid–structure interaction (FSI) simulations. In this study, 3D printing...

Multi-classification model incorporating radiomics and clinic-radiological features for predicting invasiveness and differentiation of pulmonary adenocarcinoma nodules

To develop a comprehensive multi-classification model that combines radiomics and clinic-radiological features to accurately predict the invasiveness and differentiation of pulmonary adenocarcinoma nodules. A retrospective analysis was conducted on a cohort comprising 500 patients diagnosed with lung adenocarcinoma between January 2020 and December 2022. The dataset included...

Comparison of deep learning-based image segmentation methods for intravascular ultrasound on retrospective and large image cohort study

The aim of this study was to investigate the generalization performance of deep learning segmentation models on a large cohort intravascular ultrasound (IVUS) image dataset over the lumen and external elastic membrane (EEM), and to assess the consistency and accuracy of automated IVUS quantitative measurement parameters. A total of 11,070 IVUS images from 113 patients and...

Spatial mapping of tumor heterogeneity in whole-body PET–CT: a feasibility study

Tumor heterogeneity is recognized as a predictor of treatment response and patient outcome. Quantification of tumor heterogeneity across all scales may therefore provide critical insight that ultimately improves cancer management. An image registration-based framework for the study of tumor heterogeneity in whole-body images was evaluated on a dataset of 490 FDG-PET–CT images of...

Interpretable classification for multivariate gait analysis of cerebral palsy

The Gross Motor Function Classification System (GMFCS) is a widely used tool for assessing the mobility of people with Cerebral Palsy (CP). It classifies patients into different levels based on their gross motor function and its level is typically determined through visual evaluation by a trained expert. Although gait analysis is commonly used in CP research, the functional...