Biomedical Physics & Engineering Express

Objectives
Current lung cancer screening protocols primarily evaluate pulmonary nodules, yet often neglect the malignancy risk associated with small nodules (≤10 mm). This study endeavors to optimize the management of pulmonary nodules in this population by devising and externally validating a Multimodal Integrated Feature Neural Network (MIFNN). We hypothesize that the fusion of deep learning algorithms with morphological nodule features will significantly enhance diagnostic accuracy.
Materials and Methods
Data were retrospectively collected from the Lung Nodule Analysis 2016 (LUNA16) dataset and four local centers in Beijing, China...

This study proposes a multiclass model to classify the severity of knee osteoarthritis (KOA) using bioimpedance measurements. The experimental setup considered three types of measurements using eight electrodes: global impedance with adjacent pattern, global impedance with opposite pattern, and direct impedance measurement, which were taken using an electronic device proposed by authors and based on the Analog Devices AD5933 impedance converter. The study comprised 37 participants, 25 with healthy knees and 13 with three different degrees of KOA...

The transdermal drug delivery based on microneedles (MNs) provides a suitable and painless self-administration for diabetic patients. In this work, the hydrogel-forming MNs were firstly fabricated using PVA and chitosan (CS) as matrix. A hypoglycemic drug, metformin (Met), had been loaded into MIL-100(Fe). Then, both of free Met and Met-loaded MIL-100(Fe) were integrated into hydrogel-forming MNs for regulation of BGLs on diabetic rats. After penetrated into the skin, the free Met could be firstly released from MNs...

In this paper, we propose a novel multi-scale 3D-CRU model, with the goal of extracting more discriminative emotion feature from EEG signals. By concurrently exploiting the relative electrode locations and different frequency subbands of EEG signals, a three-dimensional feature representation is reconstructed wherein the Delta (δ) frequency pattern is included. We employ a multi-scale approach, termed 3D-CRU, to concurrently extract frequency and spatial features at varying levels of granularity within each time segment...


New applications of 3D printing have recently appeared in the fields of radiotherapy and radiology, but the knowledge of many radiological characteristics of the compounds involved is still limited. Therefore, studies are needed to improve our understanding about the transport and interaction of ionizing radiation in these materials.
Purpose:
The purpose of this study is to perform an analysis of the most important radiation interaction parameters in thermoplastic materials used in Fused Deposition Modeling 3D printing...

The intricate nature of lung cancer treatment poses considerable challenges upon diagnosis. Early detection plays a pivotal role in mitigating its escalating global mortality rates. Consequently, there are pressing demands for robust and dependable early detection and diagnostic systems. However, the technological limitations and complexity of the disease make it challenging to implement an efficient lung cancer screening system. AI-based CT image analysis techniques are showing significant contributions to the development of computer-assisted detection (CAD) systems for lung cancer screening...

Radiomics is a promising valuable analysis tool consisting in extracting quantitative information from medical images. However, the extracted radiomics features are too sensitive to variations in used image acquisition and reconstruction parameters. This limited robustness hinders the generalizable validity of radiomics-assisted models. Our aim is to investigate a possible harmonization strategy based on matching image quality to improve feature robustness.
Approach: We acquired CT scans of a phantom with two scanners across different dose levels and percentages of Iterative Reconstruction algorithms...

Extracellular vesicles (EVs) have been recognized as one of the promising specific drugs for myocardial infarction (MI) prognosis. Nevertheless, low intramyocardial retention of EVs remains a major impediment to their clinical application. In this study, we developed a silk fibroin/hydroxypropyl cellulose (SF/HPC) composite hydrogel combined with AC16 cell-derived EVs targeted modification by folic acid for the treatment of acute myocardial infarction repair. EVs were functionalized by distearoylphosphatidyl ethanolamine-polyethylene glycol (DSPE-PEG-FA) via noncovalent interaction for targeting and accelerating myocardial infarction repair...

Cardiac electrical changes associated with ischemic heart disease (IHD) are subtle and could be detected even in rest condition in magnetocardiography (MCG) which measures weak cardiac magnetic fields. Cardiac features that are derived from MCG recorded from multiple locations on the chest of subjects and some conventional time domain indices are widely used in Machine learning (ML) classifiers to objectively distinguish IHD and control subjects. Most of the earlier studies have employed features that are derived from signal-averaged cardiac beats and have ignored inter-beat information...

Rapid and accurate electrocardiogram (ECG) signal classification is crucial in high-stakes healthcare settings. However, existing computational models often struggle to balance high performance with computational efficiency. This study introduces an innovative computational framework that combines transfer learning with traditional machine learning to optimize ECG classification. We use a pre-trained Stacked Convolutional Neural Network (SCNN) to generate high-dimensional feature embeddings, which are then evaluated by an array of machine learning classifiers...

Cervical cancer is a prevalent malignant tumor within the female reproductive system and is regarded as a prominent cause of female mortality on a global scale. Timely and precise detection of various phases of cervical cancer holds the potential to substantially enhance both the rate of successful treatment and the duration of patient survival. Fluorescence spectroscopy is a highly sensitive method for detecting the biochemical changes that arise during cancer progression. In our study, fluorescence spectral data is collected from a diverse group of 110 subjects...

INTRODUCTION: The currently available dosimetry techniques in computed tomography can be inaccurate which overestimate the absorbed dose. Therefore, we aimed to provide an automated and fast methodology to more accurately calculate the SSDE using D_w obtained by using CNN from thorax and abdominal CT study images. METHODS: The SSDE was determined from the 200 records files. For that purpose, patients' size was measured in two ways: a) by developing an algorithm following the AAPM Report No...

A novel fiber optic biosensor was purposed for a new approach to monitor amyloid beta protein fragment 1-42 (Aβ42) for Alzheimer's Disease (AD) early detection. The sensor was fabricated by etching a part of fiber from single mode fiber loop in pure hydrofluoric acid solution and utilized as a Local Optical Refractometer (LOR) to monitor the change Aβ42 concentration in Artificial Cerebrospinal Fluid (ACSF). The Fiber Loop Ringdown Spectroscopy (FLRDS) technique is an ultra-sensitive measurement technique with low-cost, high sensitivity, real-time measurement, continuous measurement and portability features that was utilized with a fiber optic sensor for the first time for the detection of a biological signature in an ACSF environment...

To localize the unusual cardiac activities non-invasively, one has to build a prior forward
model that relates the heart, torso, and detectors. This model has to be constructed to
mathematically relate the geometrical and functional activities of the heart. Several methods 
are available to model the prior sources in the forward problem, which results in the lead field 
matrix generation. In the conventional technique, the lead field assumed the fixed prior 
sources, and the source vector orientations were presumed to be parallel to the detector plane 
with the unit strength in all directions...

The aim of this study was to evaluate Cu-64 PET phantom image quality using Bayesian Penalized Likelihood (BPL) and Ordered Subset Expectation Maximum with point-spread function modeling (OSEM-PSF) reconstruction algorithms. In the BPL, the regularization parameter β was varied to identify the optimum value for image quality. In the OSEM-PSF, the effect of acquisition time was evaluated to assess the feasibility of shortened scan duration. 

Methods: A NEMA IEC PET body phantom was filled with known activities of water soluble Cu-64...

We have previously reported the design of a portable 109Cd x-ray fluorescence (XRF) skin iron measurement system with a low detection limit of 1.35 μg Fe per g of tissue, which delivers a skin surface dose of 1.1 mSv in iron overload and iron deficiency disease. The system has been found to be precise but must also provide accurate measurements of iron levels in the skin. 
This article describes testing of the system's accuracy. Ten pigskin samples were measured using both the portable XRF system and ICP-MS analysis...

A lot of underdeveloped nations, particularly in Africa, struggle with cancer-related, deadly diseases. Particularly in women, the incidence of breast cancer is rising daily because of ignorance and delayed diagnosis. Only by correctly identifying and diagnosing cancer in its very early stages of development can be effectively treated. The classification of cancer can be accelerated and automated with the aid of computer-aided diagnosis and medical image analysis techniques. This research provides the use of transfer learning from a Residual Network 18 (ResNet18) and Residual Network 34 (Resnet34) architectures to detect breast cancer...

Thoracoabdominal MRI is limited by respiratory motion, especially in neonates who cannot perform breath-holds. To reduce motion blurring, radially-acquired MRI can be reconstructed from data acquired during specific respiratory phases ("hard-gating"), but this reduces image SNR. Various "soft-gating" reconstruction schemes have been proposed that incorporate data acquired outside the period of interest into reconstruction with diminished weighting. However, the choice of soft-gated weighting algorithm and parameters, and effect on image SNR and motion blurring, has not previously been explored...

Prompt diagnosis of epilepsy relies on accurate classification of automated electroencephalogram (EEG) signals. Several approaches have been developed to characterize epileptic EEG data; however, none of them have exploited time-frequency data to evaluate the effect of tweaking parameters in pretrained frameworks for EEG data classification. This study compares the performance of several pretrained convolutional neural networks (CNNs) namely, AlexNet, GoogLeNet, MobileNetV2, ResNet-18 and SqueezeNet for the localization of epilepsy EEG data using various time-frequency data representation algorithms...

Ultrasound-assisted orthopaedic navigation holds promise due to its non-ionizing feature, portability, low cost, and real-time performance. To facilitate the applications, it is critical to have accurate and real-time bone surface segmentation. Nevertheless, the imaging artifacts and low signal-to-noise ratios in the tomographical B-mode ultrasound (B US) images-create substantial challenges in bone surface detection. In this study, we present an end-to-end lightweight US bone segmentation network (UBS-Net) for bone surface detection...