Bioengineering最新文献

Utilizing information from multiple sources is a preferred and more precise method for medical experts to confirm a diagnosis. Each source provides critical information about the disease that might otherwise be absent in other modalities. Combining information from various medical sources boosts confidence in the diagnosis process, enabling the creation of an effective treatment plan for the patient. The scarcity of medical experts to diagnose diseases motivates the development of automatic diagnoses relying on multimodal data. With the progress in artificial intelligence technology, automated diagnosis using multimodal fusion techniques is now possible. Nevertheless, the concept of multimodal medical diagnosis is still new and requires an understanding of the diverse aspects of multimodal data and its related challenges. This review article examines the various aspects of multimodal medical diagnosis to equip readers, academicians, and researchers with necessary knowledge to advance multimodal medical research. The chosen articles in the study underwent thorough screening from reputable journals and publishers to offer high-quality content to readers, who can then apply the knowledge to produce quality research. Besides, the need for multimodal information and the associated challenges are discussed with solutions. Additionally, ethical issues of using artificial intelligence in medical diagnosis is also discussed.

Bladder cancer is a prevalent and highly recurrent malignancy within the urinary tract. The accurate segmentation of the bladder wall and tumor in magnetic resonance imaging (MRI) is a crucial step in distinguishing between non-muscle-invasive and muscle-invasive types of bladder cancer, which plays a pivotal role in guiding clinical treatment decisions and influencing postoperative quality of life. The performance of data-driven methods is highly dependent on the quality of the annotations and datasets, however the amount of high-quality annotated data is very limited given the difficulty of professional radiologists to distinguish the mixed regions between the bladder wall and the tumor. The performance of the data-driven approach is highly dependent on the quality of the annotation and datasets, Therefore, in order to alleviate these problems and take full advantage of the potential of limited annotated and unlabeled data, we designed a semi-supervised multi-region framework for bladder wall and tumor segmentation. Our framework incorporates wall-enhanced self-supervised pre-training, designed to enhance discrimination of the bladder wall, and a semi-supervised segmentation network that utilizes both limited high-quality annotated data and unlabeled data. Contrast consistency and reconstruction observation losses are introduced to constrain the model to enhance the bladder walls, and adaptive learning rate and post-processing techniques are implemented to further improve segmentation performance. Extensive experimental validation demonstrated that our proposed method achieves promising results in the segmentation of both the bladder wall and the tumor. The average Dice Similarity Coefficients (DSCs) of the proposed method for the bladder wall and tumor were 0.8351 and 0.9175, respectively. Visualization results indicated that our method can effectively reduce excessive segmentation artifacts outside the bladder, and improve the clinical significance of the segmentation results.

The pivotal role of sleep has led to extensive research endeavors aimed at automatic sleep stage classification. However, existing methods perform poorly when classifying small groups or individuals, and these results are often considered outliers in terms of overall performance. These outliers may introduce bias during model training, adversely affecting feature selection and diminishing model performance. To address the above issues, this paper proposes an ensemble-based sequential convolutional neural network (E-SCNN) that incorporates a clustering module and neural networks. E-SCNN effectively ensembles machine learning and deep learning techniques to minimize outliers, thereby enhancing model robustness at the individual level. Specifically, the clustering module categorizes individuals based on similarities in feature distribution and assigns personalized weights accordingly. Subsequently, by combining these tailored weights with the robust feature extraction capabilities of convolutional neural networks, the model generates more accurate sleep stage classifications. The proposed model was verified on two public datasets, and experimental results demonstrate that the proposed method obtains overall accuracies of 84.8% on the Sleep-EDF Expanded dataset and 85.5% on the MASS dataset. E-SCNN can alleviate the outlier problem, which is important for improving sleep quality monitoring for individuals.

Cardiovascular diseases (CVDs) are the most common health threats worldwide. 2D X-ray invasive coronary angiography (ICA) remains the most widely adopted imaging modality for CVD assessment during real-time cardiac interventions. However, it is often difficult for the cardiologists to interpret the 3D geometry of coronary vessels based on 2D planes. Moreover, due to the radiation limit, often only two angiographic projections are acquired, providing limited information of the vessel geometry and necessitating 3D coronary tree reconstruction based only on two ICA projections. In this paper, we propose a self-supervised deep learning method called NeCA, which is based on neural implicit representation using the multiresolution hash encoder and differentiable cone-beam forward projector layer, in order to achieve 3D coronary artery tree reconstruction from two 2D projections. We validate our method using six different metrics on a dataset generated from coronary computed tomography angiography of right coronary artery and left anterior descending artery. The evaluation results demonstrate that our NeCA method, without requiring 3D ground truth for supervision or large datasets for training, achieves promising performance in both vessel topology and branch-connectivity preservation compared to the supervised deep learning model.

A mammalian breath-hold (BH) mechanism can induce vasoconstriction in the limbs, altering blood flow and oxygenation flow changes in a wound site. Our objective was to utilize a BH paradigm as a stimulus to induce peripheral tissue oxygenation changes via studies on control and diabetic foot ulcer (DFU) subjects. Subjects were imaged under a breath-hold paradigm (including 20 s BH) using a non-contact spatio-temporal-based NIRS device. Oxygenated flow changes were similar between darker and lighter skin colors but differed between wound site and normal background tissues. Thus, the ability of peripheral vasculature to response to oxygenation demand can be assessed in DFUs.

Pulsed Electromagnetic Fields (PEMF) are widely used, with excellent clinical outcomes. However, their mechanism of action has not yet been completely understood. The purpose of this review is to describe current observations on the mechanisms of PEMF, together with its clinical efficacy. Osteoblast responsiveness to PEMF is described on several scales, from the cell membrane to clinically relevant bone formation. PEMF has been shown to activate membrane adenosine receptors. The role of adenosine receptors in activating intracellular second messenger pathways, such as the canonical Wnt/β-catenin pathway and the mitogen-activated protein kinases (MAPK) pathway, is described. The responsiveness of osteoblasts and the synthesis of structural and signaling proteins constitute the role of PEMFs in promoting osteogenesis and bone matrix synthesis, and they are described. Multiple studies, ranging from observational and randomized to meta-analyses that investigate the clinical efficacy of PEMF, are described. This review presents a favorable conclusion on the clinical effects of PEMF while unlocking the "black box" of PEMF's mechanism of action, thus improving confidence in the clinical utility of PEMF in bone repair.

Heart failure is characterized by intricate myocardial remodeling that impairs the heart's pumping and/or relaxation capacity, ultimately reducing cardiac output. It represents a major public health burden, given its high prevalence and associated morbidity and mortality rates, which continue to challenge healthcare systems worldwide. Despite advancements in medical science, there are no treatments that address the disease at its core. The development of three-dimensional engineered in vitro models that closely mimic the (patho)physiology and drug responses of the myocardium has the potential to revolutionize our insights and uncover new therapeutic avenues. Key aspects of these models include the precise replication of the extracellular matrix structure, cell composition, micro-architecture, mechanical and electrical properties, and relevant physiological and pathological stimuli, such as fluid flow, mechanical load, electrical signal propagation, and biochemical cues. Additionally, to fully capture heart failure and its diversity in vivo, it is crucial to consider factors such as age, gender, interactions with other organ systems and external influences-thereby recapitulating unique patient and disease phenotypes. This review details these model features and their significance in heart failure research, with the aim of enhancing future platforms that will deepen our understanding of the disease and facilitate the development of novel, effective therapies.

To optimize microbubble formulations for clinical applications, the size distribution, concentration, and acoustic intensity must be rapidly measurable to allow for the successful iteration of microbubble design. In this paper, a comprehensive method was developed to compare microbubble formulations with different lipid shell compositions using optical and acoustic methods of measurement to collect the size distribution, concentration, and mean scattering intensity. An open-source ImageJ macro code was modified for the selective counting and sizing of brightfield microbubble images. A high-throughput agarose phantom was designed to collect multiple scattering reflections of microbubble samples to estimate the echogenicity of each microbubble solution. The information contained in the size distribution and concentration, combined with the instantaneous scattering power, can identify modifications needed for prototyping specific microbubble formulations.

Nanoparticle (NP)-based drug delivery systems have received widespread attention due to the excellent physicochemical properties of nanomaterials. Different types of NPs such as lipid NPs, poly(lactic-co-glycolic) acid (PLGA) NPs, inorganic NPs (e.g., iron oxide and Au), carbon NPs (graphene and carbon nanodots), 2D nanomaterials, and biomimetic NPs have found favor as drug delivery vehicles. In this review, we discuss the different types of customized NPs for intravascular drug delivery, nanoparticle behaviors (margination, adhesion, and endothelium uptake) in blood vessels, and nanomaterial compatibility for successful drug delivery. Additionally, cell surface protein targets play an important role in targeted drug delivery, and various vascular drug delivery studies using nanoparticles conjugated to these proteins are reviewed. Finally, limitations, challenges, and potential solutions for translational research regarding NP-based vascular drug delivery are discussed.

Parkinson's Disease (PD) is a progressive neurodegenerative disorder affecting millions worldwide. Early detection is crucial for improving patient outcomes. Spiral drawing analysis has emerged as a non-invasive tool to detect early motor impairments associated with PD. This study examines the performance of hybrid deep learning and machine learning models in detecting PD using spiral drawings, with a focus on the impact of data augmentation techniques. We compare the accuracy of Vision Transformer (ViT) with K-Nearest Neighbors (KNN), Convolutional Neural Networks (CNN) with Support Vector Machines (SVM), and Residual Neural Networks (ResNet-50) with Logistic Regression, evaluating their performance on both augmented and non-augmented data. Our findings reveal that ViT with KNN, initially achieving 96.77% accuracy on unaugmented data, experienced a notable decline across all augmentation techniques, suggesting it relies heavily on global patterns in spiral drawings. In contrast, ResNet-50 with Logistic Regression showed consistent improvement with data augmentation, reaching 93.55% accuracy when rotation and flipping techniques were applied. These results highlight that hybrid models respond differently to augmentation, and careful selection of augmentation strategies is necessary for optimizing model performance. Our study provides important insights into the development of reliable diagnostic tools for early PD detection, emphasizing the need for appropriate augmentation techniques in medical image analysis.