This article demonstrates the power and flexibility of linear mixed-effects models (LMEMs) to investigate high-density surface electromyography (HD-sEMG) signals. The potentiality of the model is illustrated by investigating the root mean squared value of HD-sEMG signals in the tibialis anterior muscle of healthy (n = 11) and individuals with diabetic peripheral neuropathy (n = 12). We started by presenting

Counting the mitotic cells in histopathological cancerous tissue areas is the most relevant indicator of tumor grade in aggressive breast cancer diagnosis. In this paper, we propose a robust and accurate technique for the automatic detection of mitoses from histological breast cancer slides using the multi-task deep learning framework for object detection and instance segmentation Mask RCNN. Our mitosis

Breast cancer is a widespread disease and one of the primary causes of cancer mortality among women all over the world. Computer-aided methods are used to assist medical doctors to make early diagnosis of the disease. The aim of this study is to build an effective prediction model for breast cancer diagnosis based on anthropometric data and parameters collected through routine blood analysis. The proposed

A computer-assisted human peripheral blood leukocyte image classification method based on Siamese network is proposed. Firstly, a Siamese network with two identical convolutional neural network (CNN) sub-networks and a logistic regression for leukocyte five classification is designed, which can learn not only distinguishing features but also a similarity metric. Then for each category of the leukocytes

The purpose of this study is to assess and model age-related changes in the mechanical properties of human fascia. The samples were divided into three age groups: group A-up to 60 years (mean age 52.5 ± 6 years), group B-61-80 years (mean age 70.4 ± 5.2 years), and group C-81-90 years (mean age 83.2 ± 2 years). A uniaxial tensile test was applied to fascia specimens cut perpendicular and parallel to

Wrist injuries are common in paediatric trauma; however, only half of children evaluated with an x-ray for possible fractures will have one. Thermal imaging offers a possible non-ionising method of screening for fractures and thus reducing negative x-ray rates. One hundred five children attending the Emergency Department for wrist injuries were recruited. Two 30-s thermal videos were recorded from

Quantitative evaluation and training of the reach-to-target ability in stroke patients are needed for postdischarge rehabilitation, which can be achieved using a motion-tracking system. However, most of these systems are either costly, involve sophisticated parameter interpretation, or are not designed for rehabilitation. We developed an interactive reach-to-target assessment and training system (IRTATS)

Transfer learning enables the adaption of models to handle mismatches of distributions across sessions or across subjects. In this paper, we proposed a new transfer learning algorithm to classify motor imagery EEG data. By analyzing the power spectrum of EEG data related to motor imagery, the shared features across sessions or across subjects, namely, the mean and variance of model parameters, are

Currently, simulations of the induced currents in the brain produced by transcranial magnetic stimulation (TMS) are used to elucidate the regions reached by stimuli. However, models commonly found in the literature are too general and neglect imperfections in the windings. Aiming to predict the stimulation sites in patients requires precise modeling of the electric field (E-field), and a proper calibration

Dynamic magnetic resonance imaging (dMRI) strikes a balance between reconstruction speed and image accuracy in medical imaging field. In this paper, an improved robust tensor principal component analysis (RTPCA) method is proposed to reconstruct the dynamic magnetic resonance imaging (MRI) from highly under-sampled K-space data. The MR reconstruction problem is formulated as a high-order low-rank tenor

Many studies in the rigid gas permeable (RGP) lens fitting field have focused on providing the best fit for patients with irregular astigmatism, a challenging issue. Despite the ease and accuracy of fitting in the current fitting methods, no studies have provided a high-pace solution with the final best fit to assist experts. This work presents a deep learning solution for identifying features in Pentacam

The objective of this study was to design and develop a predictive model for 30-day risk of hospital readmission using machine learning techniques. The proposed predictive model was then validated with the two most commonly used risk of readmission models: LACE index and patient at risk of hospital readmission (PARR). The study cohort consisted of 180,118 admissions with 22,565 (12.5%) of actual readmissions

To the best of the authors' knowledge, testing the biocompatibility of graphene coatings can be considered as the first to demonstrate human carotid endothelial cell (HCtAEC) proliferation on Au, graphene oxide-coated Au (Au/GO), and reduced graphene oxide-coated Au (Au/rGO) surfaces. We hypothesized that stent material modified with graphene (G)-based coatings could be used as electrodes for electrical

Today's technological era, the booming desire for e-healthcare has inflated the attention towards the security of data from cyber attacks. As the digital medical images are transferred over the public network, there is a demand to shield an adequate level of protection. One of the prominent techniques is encryption which secures the medical images. This paper recommends a DICOM image encryption based

The study and applications of in vivo skin optics have been openly documented as early as the year 1954, or possibly earlier. To date, challenges in analyzing the complexities of this field remain, with wide scopes requiring more scrutiny. Recent advances in spectroscopic research and multivariate analytics allow a closer look into applications potentially for detecting or monitoring diseases. One

In cell-based research, the process of visually monitoring cells generates large image datasets that need to be evaluated for quantifiable information in order to track the effectiveness of treatments in vitro. With the traditional, end-point assay-based approach being error-prone, and existing computational approaches being complex, we tested existing machine learning frameworks to find methods that

Designing computer-assisted diagnosis (CAD) systems that can precisely identify lesions from mammography images would be useful for clinicians. Considering the morphological variation in breast cancer, it is necessary to extract robust features from the mammogram. Here, we propose a mass detection CAD system that is based on Faster R-CNN. First, we applied a novel convolution network in the backbone

Blood pressure (BP) variability is one of the important risk factors of cardiovascular disease (CVD). "Surge BP," which represents short-term BP variability, is defined as pathological exaggerated BP increase capable of triggering cardiovascular events. Surge BP is effectively evaluated by our new BP monitoring device. To the best of our knowledge, we are the first to develop an algorithm for the automatic

Neonatal sleep analysis at the neonatal intensive care units (NICU) is critical for the diagnosis of any brain growth risks during the early stages of life. In this paper, an investigation is carried out on the use of a long short-term memory (LSTM) learning system in automatic sleep stage scoring in neonates. The developed algorithm automatically classifies sleep stages based on inputs from a single

Variation in mechanical properties is a useful marker for cancer in soft tissue and has been used in clinical diagnosis for centuries. However, to develop such methods as instrumented palpation, there remain challenges in using the mechanical response during palpation to quantify tumor load. This study proposes a computational framework of identification and quantification of cancerous nodules in soft

This study outlines the first investigation of application of machine learning to distinguish "skilled" and "novice" psychomotor performance during a virtual reality (VR) brain tumor resection task. Tumor resection task participants included 23 neurosurgeons and senior neurosurgery residents as the "skilled" group and 92 junior neurosurgery residents and medical students as the "novice" group. The

Despite all the efforts to optimize the meniscus prosthesis system (geometry, material, and fixation type), the success of the prosthesis in clinical practice will depend on surgical factors such as intra-operative positioning of the prosthesis. In this study, the aim was therefore to assess the implications of positional changes of the medial meniscus prosthesis for knee biomechanics. A detailed validated

The purpose of this study is to investigate the feasibility and performance of a stationary, non-focused dual-sectored tubular transurethral ultrasound applicator for thermal exposure of tissue regions adjacent to the urethra for treatment of stress urinary incontinence (SUI) through acoustic and biothermal simulations on 3D anatomical models. Parametric studies in a generalized tissue model over dual-sectored

Speckle tracking is the most prominent technique used to estimate the regional movement of the heart based on echocardiograms. In this study, we propose an optimised-based block matching algorithm to perform speckle tracking iteratively. The proposed technique was evaluated using a publicly available synthetic echocardiographic dataset with known ground-truth from several major vendors and for healthy/ischaemic

In the original publication of the article the affiliation of Kamen Ivanov was inaccurate. The correct affiliation of Kamen Ivanov is given below.

Survival of pediatric patients with brain tumor has increased over the past 20 years, and increasing evidence of iatrogenic toxicities has been reported. In follow-ups, images are acquired at different time points where substantial changes of brain morphology occur, due to childhood physiological development and treatment effects. To address the image registration complexity, we propose two multi-metric

Esophageal squamous cell carcinoma is the most predominant malignancy of the esophagus. Its histological precursors (dysplasia) emerge in the esophageal epithelium that their progression into the underlying layers leads to cancer. The epithelium is the origin of many solid cancers and, accordingly, the focus of numerous computational models. In this work, we proposed a framework to establish a two-dimensional

Interstitial lung disease (ILD) refers to a group of various abnormal inflammations of lung tissues and early diagnosis of these disease patterns is crucial for the treatment. Yet it is difficult to make an accurate diagnosis due to the similarity among the clinical manifestations of these diseases. In order to assist the radiologists, computer-aided diagnosis systems have been developed. Besides,

The accurate modeling of the liver vessel network structure is an important prerequisite for developing a preoperative plan for the liver. Considering that extracting liver blood vessels from patient's abdominal computed tomography(CT) images requires several manual operations, this study proposed an automatic segmentation method of liver vessels based on graph cut, thinning, and vascular combination

Deep brain stimulation (DBS) is an established therapy for movement disorders such as essential tremor (ET). Positioning of the DBS lead in the patient's brain is crucial for effective treatment. Extensive evaluations of improvement and adverse effects of stimulation at different positions for various current amplitudes are performed intraoperatively. However, to choose the optimal position of the

Bicuspid aortic valve (BAV) anatomy has routinely been considered an exclusion in the setting of transcatheter aortic valve implantation (TAVI) because of the large dimension of the aortic annulus having a more calcified, bulky, and irregular shape. The study aims to develop a patient-specific computational framework to virtually simulate TAVI in stenotic BAV patients using the Edwards SAPIEN 3 valve

Obstructive Eustachian tube dysfunction (OETD) is a common condition resulting from inadequate opening of the Eustachian tube (ET). A new surgical treatment involves high-pressure inflation of a balloon within the ET, with the aim of dilating the soft tissue structure. However, the mechanical effects of this intervention have not been established, nor the impact of changing device size or other technical

Magnetic induction tomography (MIT) is a non-invasive modality for imaging the complex conductivity (σ) or the magnetic permeability (μ) of a target under investigation. The critical issue in the clinical application of the detection of cerebral hemorrhage is the determination of intracranial hematoma status, including the location and volume of intracranial hematoma. In MIT, the reconstruction image

Blood-oxygen-level-dependent (BOLD) signal has been commonly used in functional magnetic resonance imaging (fMRI) to observe the activity in different areas of the brain or other organs. This signal is difficult to simulate, because its amplitude is nearly 1~3% and it is influenced by multiple factors. This study aimed to design and construct an active BOLD simulation phantom and test its stability

In vivo dosimetry of the patients treated by I-131 is important from patient dosimetry and radiation protection points of view. Knowledge of delivered dose to the target volume and adjacent normal tissues can improve the effectiveness of radioiodine treatment. Herein, design, fabrication, and assessment processes of an iodine radionuclide dosimeter (IRD) are explained. Two CsI(Tl) scintillator crystals

Electrical stimulus is one of the common stimulating methods, and Galvanic vestibular stimulation (GVS) is the oldest form as an electrical stimulation. Nevertheless, GVS is still considered as a secondary stimulating tool for the medical purposes. Even though some unarguable findings have made using GVS, its use has been limited because of its ambiguity as an input source. For better understanding

Breast cancer is the leading killer of Chinese women. Immunohistochemistry index has great significance in the treatment strategy selection and prognosis analysis for breast cancer patients. Currently, histopathological examination of the tumor tissue through surgical biopsy is the gold standard to determine immunohistochemistry index. However, this examination is invasive and commonly causes discomfort

Red blood cell (RBC) deformation is the consequence of several diseases, including sickle cell anemia, which causes recurring episodes of pain and severe pronounced anemia. Monitoring patients with these diseases involves the observation of peripheral blood samples under a microscope, a time-consuming procedure. Moreover, a specialist is required to perform this technique, and owing to the subjective

High-quality annotations for medical images are always costly and scarce. Many applications of deep learning in the field of medical image analysis face the problem of insufficient annotated data. In this paper, we present a semi-supervised learning method for chronic gastritis classification using gastric X-ray images. The proposed semi-supervised learning method based on tri-training can leverage

In medicine, white blood cells (WBCs) play an important role in the human immune system. The different types of WBC abnormalities are related to different diseases so that the total number and classification of WBCs are critical for clinical diagnosis and therapy. However, the traditional method of white blood cell classification is to segment the cells, extract features, and then classify them. Such

Narrow-band imaging (NBI) laryngoscopy is an optical-biopsy technique used for screening and diagnosing cancer of the laryngeal tract, reducing the biopsy risks but at the cost of some drawbacks, such as large amount of data to review to make the diagnosis. The purpose of this paper is to develop a deep-learning-based strategy for the automatic selection of informative laryngoscopic-video frames, reducing

In recent years, microarray technology and gene expression profiles have been widely used to detect, predict, or classify the samples of various diseases. The presence of large genes in these profiles and the small number of samples are known challenges in this field and are widely considered in previous papers. In previous studies, other topics such as the noise of microarray data or the dependence

Breast cancer has the second highest frequency of death rate among women worldwide. Early-stage prevention becomes complex due to reasons unknown. However, some typical signatures like masses and micro-calcifications upon investigating mammograms can help diagnose women better. Manual diagnosis is a hard task the radiologists carry out frequently. For their assistance, many computer-aided diagnosis

X-ray images play an important role in providing physicians with satisfactory information correlated to fractures and diseases; unfortunately, most of these images suffer from low contrast and poor quality. Thus, enhancement of the image will increase the accuracy of correct information on pathologies for an autonomous diagnosis system. In this paper, a new approach for low-contrast X-ray image enhancement

The automatic cell analysis method is capable of segmenting the cells and can detect the number of live/dead cells present in the body. This study proposed a novel non-linear segmentation model (NSM) for the segmentation and quantification of live/dead cells present in the body. This work also reveals the aspects of electromagnetic radiation on the cell body. The bright images of the hippocampal CA3

The instantaneous helical axis (IHA) is a characteristic of neck movement that is very sensitive to changes in coordination and that has potential in the assessment of functional alterations. For its application in the clinical setting, normative patterns must be available, and its reliability must be established. The purpose of this work is to describe the continuous paths of the IHA during cyclic

Control of musculoskeletal yy system through functional electrical stimulation (FES) still remains a complex and a challenging process. Indeed, the used musculoskeletal models are often complex and highly nonlinear, which makes their control and inversion (getting appropriate inputs from a desired outputs) very difficult. On the other hand, the system flatness has been proved to be an efficient method

This study aims to analyse the stress distributions and initial displacements of teeth during the space closing stage through a three-dimensional finite element method. Computed tomography images of a patient were used to reconstruct the detailed teeth and alveolar bone, and brackets with stainless steel archwire were modelled according to the orthodontic prescriptions. The second premolars and first

The application of Poincaré plot analysis to characterize inter-beat interval dynamics has been successfully proposed in the scientific literature for the assessment of humans' physiological states and related aberrations. In this study, we proposed novel descriptors to trace the evolution of Poincaré plot shape over the lags. Their reliability in ultra-short cardiovascular series analysis was validated

Stress urinary incontinence (SUI) or urine leakage from urethra occurs due to an increase in abdominal pressure resulting from stress like a cough or jumping height. SUI is more frequent among post-menopausal women. In the absence of bladder contraction, vesical pressure exceeds urethral pressure leading to urine leakage. The main aim of this study is to utilize fluid-structure interaction techniques

Holter recordings are widely used to detect cardiac events that occur transiently, such as ischemic events. Much effort has been made to detect early ischemia, thus preventing myocardial infarction. However, after detection, classification of ischemia has still not been fully solved. The main difficulty relies on the false positives produced because of non-ischemic events, such as changes in the heart

Sperm morphology, as an indicator of fertility, is a critical tool in semen analysis. In this study, a smartphone-based hybrid system that fully automates the sperm morphological analysis is introduced with the aim of eliminating unwanted human factors. Proposed hybrid system consists of two progressive steps: automatic segmentation of possible sperm shapes and classification of normal/ab-normal sperms

Histopathological whole slide images of haematoxylin and eosin (H&E)-stained biopsies contain valuable information with relation to cancer disease and its clinical outcomes. Still, there are no highly accurate automated methods to correlate histolopathological images with brain cancer patients' survival, which can help in scheduling patients therapeutic treatment and allocate time for preclinical studies

A series of short events, called A-phases, can be observed in the human electroencephalogram (EEG) during Non-Rapid Eye Movement (NREM) sleep. These events can be classified in three groups (A1, A2, and A3) according to their spectral contents, and are thought to play a role in the transitions between the different sleep stages. A-phase detection and classification is usually performed manually by

The common CT imaging signs of lung diseases (CISLs) which frequently appear in lung CT images are widely used in the diagnosis of lung diseases. Computer-aided diagnosis (CAD) based on the CISLs can improve radiologists' performance in the diagnosis of lung diseases. Since similarity measure is important for CAD, we propose a multi-level method to measure the similarity between the CISLs. The CISLs

Prediabetes is a type of hyperglycemia in which patients have blood glucose levels above normal but below the threshold for type 2 diabetes mellitus (T2DM). Prediabetic patients are considered to be at high risk for developing T2DM, but not all will eventually do so. Because it is difficult to identify which patients have an increased risk of developing T2DM, we developed a model of several clinical

The understanding of cardiac arrhythmia under genetic mutations has grown in interest among researchers. Previous studies focused on the effect of the D172N mutation on electrophysiological behavior. In this study, we analyzed not only the electrophysiological activity but also the mechanical responses during normal sinus rhythm and reentry conditions by using computational modeling. We simulated four

The latest revision of multiple sclerosis diagnosis guidelines emphasizes the role of oligoclonal band detection in isoelectric focusing images of cerebrospinal fluid. Recent studies suggest tears as a promising noninvasive alternative to cerebrospinal fluid. We are developing the first automatic method for isoelectric focusing image analysis and oligoclonal band detection in cerebrospinal fluid and

Post-void alarm systems to monitor bedwetting in nocturnal enuresis (NE) have been deemed unsatisfactory. The aim of this study is to develop a safe, comfortable and non-invasive pre-void wearable alarm and associated technologies using advanced mechatronics. Each stage of development includes patient and public involvement and engagement (PPI). The early stages of the development involved children

Since more and more elderly people suffer from lower extremity movement problems, it is of great social significance to assist persons with motor dysfunction to walk independently again and reduce the burden on caregivers. The self-paced walking intention, which could increase the ability of self-control on the start and stop of motion, was studied by applying brain-computer interface (BCI) technology