Methicillin-Resistant Staphylococcus aureus Nasal Screening With Polymerase Chain Reaction for Early De-escalation of Empiric Vancomycin in the Treatment of Suspected/Confirmed Respiratory Infection in Critically Ill Patients.

Background: Vancomycin empirically for methicillin-resistant Staphylococcus aureus (MRSA) pneumonia coverage often is prolonged. With high negative predictive value for MRSA pneumonia, we evaluated the efficacy of MRSA nasal screening with polymerase chain reaction for early de-escalation of empiric vancomycin for treatment of respiratory infections in patients admitted to the intensive care units.

Study Question: The impact of MRSA nasal screening on early de-escalation of vancomycin for respiratory infections.

Predicting Colloidal Interaction Parameters from Small-Angle X-ray Scattering Curves Using Artificial Neural Networks and Markov Chain Monte Carlo Sampling.

Small-angle X-ray scattering (SAXS) is a characterization technique that allows for the study of colloidal interactions by fitting the structure factor of the SAXS profile with a selected model and closure relation. However, the applicability of this approach is constrained by the limited number of existing models that can be fitted analytically, as well as the narrow operating range for which the models are valid. In this work, we demonstrate a proof of concept for using an artificial neural network (ANN) trained on SAXS curves obtained from Monte Carlo (MC) simulations to predict values of the effective macroion valency ( ) and the Debye length (κ) for a given SAXS profile.

Explainable coronary artery disease prediction model based on AutoGluon from AutoML framework.

Objective: This study focuses on the innovative application of Automated Machine Learning (AutoML) technology in cardiovascular medicine to construct an explainable Coronary Artery Disease (CAD) prediction model to support the clinical diagnosis of CAD.

Methods: This study utilizes a combined data set of five public data sets related to CAD. An ensemble model is constructed using the AutoML open-source framework AutoGluon to evaluate the feasibility of AutoML in constructing a disease prediction model in cardiovascular medicine.

Automated cardiac vortex ring identification and characterization based on Recurrent All-Pairs Field Transforms and Lagrangian Averaged Vorticity Deviation.

Automated identification of cardiac vortices is a formidable task due to the complex nature of blood flow within the heart chambers. This study proposes a novel approach that algorithmically characterizes the identification criteria of these cardiac vortices based on Lagrangian Averaged Vorticity Deviation (LAVD). For this purpose, the Recurrent All-Pairs Field Transforms (RAFT) is employed to assess the optical flow over the Phase Contrast Magnetic Resonance Imaging (PC-MRI), and to construct a continuous blood flow velocity field and reduce errors that arise from the integral process of LAVD.

On Automated Object Grasping for Intelligent Prosthetic Hands Using Machine Learning.

Prosthetic technology has witnessed remarkable advancements, yet challenges persist in achieving autonomous grasping control while ensuring the user's experience is not compromised. Current electronic prosthetics often require extensive training for users to gain fine motor control over the prosthetic fingers, hindering their usability and acceptance. To address this challenge and improve the autonomy of prosthetics, this paper proposes an automated method that leverages computer vision-based techniques and machine learning algorithms.

Non-invasive mid-term electrocardiogram patch monitoring is effective in detecting atrial fibrillation.

Background: Atrial fibrillation (AF) is a cause of serious morbidity such as stroke. Early detection and treatment of AF is important. Current guidelines recommend screening via opportunistic pulse taking or 12‑lead electrocardiogram.

Detecting and Characterizing Inferior Vena Cava Filters on Abdominal Computed Tomography with Data-Driven Computational Frameworks.

Two data-driven algorithms were developed for detecting and characterizing Inferior Vena Cava (IVC) filters on abdominal computed tomography to assist healthcare providers with the appropriate management of these devices to decrease complications: one based on 2-dimensional data and transfer learning (2D + TL) and an augmented version of the same algorithm which accounts for the 3-dimensional information leveraging recurrent convolutional neural networks (3D + RCNN). The study contains 2048 abdominal computed tomography studies obtained from 439 patients who underwent IVC filter placement during the 10-year period from January 1st, 2009, to January 1st, 2019. Among these, 399 patients had retrievable filters, and 40 had non-retrievable filter types.

Instinct to insight: Neural correlates of ethological strategy learning.

In ethological behaviors like parenting, animals innately follow stereotyped patterns of choices to decide between uncertain outcomes but can learn to modify their strategies to incorporate new information. For example, female mice in a T-maze instinctively use spatial-memory to search for pups where they last found them but can learn more efficient strategies employing pup-associated acoustic cues. We uncovered neural correlates for transitioning between these innate and learned strategies.

HViT: Hybrid vision inspired transformer for the assessment of carotid artery plaque by addressing the cross-modality domain adaptation problem in MRI.

Background: Medical image classification is crucial for accurate and efficient diagnosis, and deep learning frameworks have shown significant potential in this area. When a general learning deep model is directly deployed to a new dataset with heterogeneous features, the effect of domain shifts is usually ignored, which degrades the performance of deep learning models and leads to inaccurate predictions.

Purpose: This study aims to propose a framework that utilized the cross-modality domain adaptation and accurately diagnose and classify MRI scans and domain knowledge into stable and vulnerable plaque categories by a modified Vision Transformer (ViT) model for the classification of MRI scans and transformer model for domain knowledge classification.

End to end vision transformer architecture for brain stroke assessment based on multi-slice classification and localization using computed tomography.

Background: Brain stroke is a leading cause of disability and death worldwide, and early diagnosis and treatment are critical to improving patient outcomes. Current stroke diagnosis methods are subjective and prone to errors, as radiologists rely on manual selection of the most important CT slice. This highlights the need for more accurate and reliable automated brain stroke diagnosis and localization methods to improve patient outcomes.

The synergy of cybernetical intelligence with medical image analysis for deep medicine: A methodological perspective.

Conceptual Introduction: To introduce the concept of cybernetical intelligence, deep learning, development history, international research, algorithms, and the application of these models in smart medical image analysis and deep medicine are reviewed in this paper. This study also defines the terminologies for cybernetical intelligence, deep medicine, and precision medicine.

Review Of Methods: Through literature research and knowledge reorganization, this review explores the fundamental concepts and practical applications of various deep learning techniques and cybernetical intelligence by conducting extensive literature research and reorganizing existing knowledge in medical imaging and deep medicine.

Automated catheter segmentation and tip detection in cerebral angiography with topology-aware geometric deep learning.

Background: Visual perception of catheters and guidewires on x-ray fluoroscopy is essential for neurointervention. Endovascular robots with teleoperation capabilities are being developed, but they cannot 'see' intravascular devices, which precludes artificial intelligence (AI) augmentation that could improve precision and autonomy. Deep learning has not been explored for neurointervention and prior works in cardiovascular scenarios are inadequate as they only segment device tips, while neurointervention requires segmentation of the entire structure due to coaxial devices.

Brain image segmentation of the corpus callosum by combining Bi-Directional Convolutional LSTM and U-Net using multi-slice CT and MRI.

Background And Objective: Traditional disease diagnosis is usually performed by experienced physicians, but misdiagnosis or missed diagnosis still exists. Exploring the relationship between changes in the corpus callosum and multiple brain infarcts requires extracting corpus callosum features from brain image data, which requires addressing three key issues. (1) automation, (2) completeness, and (3) accuracy.

Reliability of the National Institutes of Health (NIH) Stroke Scale Between Emergency Room and Neurology Physicians for Initial Stroke Severity Scoring.

Introduction: In patients with acute ischemic stroke (AIS), the National Institutes of Health Stroke Scale (NIHSS) is essential to establishing a patient's initial stroke severity. While previous research has validated NIHSS scoring reliability between neurologists and other clinicians, it has not specifically evaluated NIHSS scoring reliability between emergency room (ER) and neurology physicians within the same clinical scenario and timeframe in a large cohort of patients. This study specifically addresses the key question: does an ER physician's NIHSS score agree with the neurologist's NIHSS score in the same patient at the same time in a real-world context?

Methods: Data was retrospectively collected from 1,946 patients being evaluated for AIS at Houston Methodist Hospital from 05/2016 - 04/2018.

BBox-Guided Segmentor: Leveraging expert knowledge for accurate stroke lesion segmentation using weakly supervised bounding box prior.

Stroke is one of the leading causes of death and disability in the world. Despite intensive research on automatic stroke lesion segmentation from non-invasive imaging modalities including diffusion-weighted imaging (DWI), challenges remain such as a lack of sufficient labeled data for training deep learning models and failure in detecting small lesions. In this paper, we propose BBox-Guided Segmentor, a method that significantly improves the accuracy of stroke lesion segmentation by leveraging expert knowledge.

Functional magnetic resonance imaging providing the brain effect mechanism of acupuncture and moxibustion treatment for depression.

The efficacy of acupuncture and moxibustion in the treatment of depression has been fully recognized internationally. However, its central mechanism is still not developed into a unified standard, and it is generally believed that the central mechanism is regulation of the cortical striatum thalamic neural pathway of the limbic system. In recent years, some scholars have applied functional magnetic resonance imaging (fMRI) to study the central mechanism and the associated brain effects of acupuncture and moxibustion treatment for depression.

KFPredict: An ensemble learning prediction framework for diabetes based on fusion of key features.

Background And Objective: Diabetes is a disease that requires early detection and early treatment, and complications are likely to occur in late stages of the disease, threatening the life of patients. Therefore, in order to diagnose diabetic patients as early as possible, it is necessary to establish a model that can accurately predict diabetes.

Methodology: This paper proposes an ensemble learning framework: KFPredict, which combines multi-input models with key features and machine learning algorithms.

Medical image fusion using enhanced cross-visual cortex model based on artificial selection and impulse-coupled neural network.

Objective: The traditional ICM is widely used in applications, such as image edge detection and image segmentation. However, several model parameters must be set, which tend to lead to reduced accuracy and increased cost. As medical images have more complex edges, contours and details, more suitable combinatorial algorithms are needed to handle the pathological diagnosis of multiple cerebral infarcts and acute strokes, resulting in the findings being more applicable, as well as having good clinical value.

DE-MRI myocardial fibrosis segmentation and classification model based on multi-scale self-supervision and transformer.

Objective: The segmentation and categorization of fibrotic tissue in time-lapse enhanced MRI scanning are quite challenging, and it is mainly done manually for myocardial DE-MRI images. On the other hand, DE-MRI instructions for segmenting and classifying cardiac hypertrophy are complex and prone to inaccuracy. Developing cardiac DE-MRI classification and prediction methods is crucial.

Unsupervised domain adaptive myocardial infarction MRI classification diagnostics model based on target domain confused sample resampling.

Objective: Inefficient circulatory system due to blockage of blood vessels leads to myocardial infarction and acute blockage. Myocardial infarction is frequently classified and diagnosed in medical treatment using MRI, yet this method is ineffective and prone to error. As a result, there are several implementation scenarios and clinical significance for employing deep learning to develop computer-aided algorithms to aid cardiologists in the routine examination of cardiac MRI.

Deep learning-based medical image segmentation of the aorta using XR-MSF-U-Net.

Purpose: This paper proposes a CT images and MRI segmentation technology of cardiac aorta based on XR-MSF-U-Net model. The purpose of this method is to better analyze the patient's condition, reduce the misdiagnosis and mortality rate of cardiovascular disease in inhabitants, and effectively avoid the subjectivity and unrepeatability of manual segmentation of heart aorta, and reduce the workload of doctors.

Method: We implement the X ResNet (XR) convolution module to replace the different convolution kernels of each branch of two-layer convolution XR of common model U-Net, which can make the model extract more useful features more efficiently.

Classification of myocardial fibrosis in DE-MRI based on semi-supervised semantic segmentation and dual attention mechanism.

Objective: It is essential to utilize cardiac delayed-enhanced magnetic resonance imaging (DE-MRI) to diagnose cardiovascular disease. By segmenting myocardium DE-MRI images, it provides critical information for the evaluation and treatment of myocardial infarction. As a consequence, it is vital to investigate the segmentation and classification technique of myocardial DE-MRI.

DeepStroke: An efficient stroke screening framework for emergency rooms with multimodal adversarial deep learning.

In an emergency room (ER) setting, stroke triage or screening is a common challenge. A quick CT is usually done instead of MRI due to MRI's slow throughput and high cost. Clinical tests are commonly referred to during the process, but the misdiagnosis rate remains high.