Devices and Vaccines

Prediction of short-term adverse clinical outcomes of acute pulmonary embolism using conventional machine learning and deep Learning based on CTPA images.

To explore the predictive value of traditional machine learning (ML) and deep learning (DL) algorithms based on computed tomography pulmonary angiography (CTPA) images for short-term adverse outcomes in patients with acute pulmonary embolism (APE). This retrospective study enrolled 132 patients with APE confirmed by CTPA. Thrombus segmentation and texture feature extraction was performed using 3D-...

COVID-19 from symptoms to prediction: A statistical and machine learning approach.

During the COVID-19 pandemic, the analysis of patient data has become a cornerstone for developing effective public health strategies. This study leverages a dataset comprising over 10,000 anonymized patient records from various leading medical institutions to predict COVID-19 patient age groups using a suite of statistical and machine learning techniques. Initially, extensive statistical tests in...

COVID-19 vaccinations and their side effects: a scoping systematic review.

The COVID-19 virus has impacted people worldwide, causing significant changes in their lifestyles. Since the emergence of the epidemic, attempts have...

A Large Language Model to Detect Negated Expressions in Radiology Reports.

Natural language processing (NLP) is crucial to extract information accurately from unstructured text to provide insights for clinical decision-making...

Soft robotic artificial left ventricle simulator capable of reproducing myocardial biomechanics.

The heart's intricate myocardial architecture has been called the Gordian knot of anatomy, an impossible tangle of intricate muscle fibers. This compl...

Integrating IoT for Soil Monitoring and Hybrid Machine Learning in Predicting Tomato Crop Disease in a Typical South India Station.

This study develops a hybrid machine learning (ML) algorithm integrated with IoT technology to improve the accuracy and efficiency of soil monitoring ...

Predictive modeling of hepatitis B viral dynamics: a caputo derivative-based approach using artificial neural networks.

A fractional model for the kinetics of hepatitis B transmission was developed. The hepatitis B virus significantly affects the world's economic and he...

A Nanoparticle-Based Artificial Ear for Personalized Classification of Emotions in the Human Voice Using Deep Learning.

Artificial intelligence and human-computer interaction advances demand bioinspired sensing modalities capable of comprehending human affective states ...

Rational Design of Lipid Nanoparticles for Enhanced mRNA Vaccine Delivery via Machine Learning.

Since the coronavirus pandemic, mRNA vaccines have revolutionized the field of vaccinology. Lipid nanoparticles (LNPs) are proposed to enhance mRNA de...

Regulatory Aspects of Artificial Intelligence and Machine Learning.

In the realm of health care, numerous generative and nongenerative artificial intelligence and machine learning (AI-ML) tools have been developed and ...

A Preliminary Study to Evaluate the Immune Response to a Booster Dose of the Adult Tetanus-Diphtheria Vaccine (Td) Available in Iran.

BACKGROUND: Despite the availability of tetanus-diphtheria (TD) vaccines in Iran, the seroconversion rate of these products as a booster dose is unkno...

Application of Machine Learning in a Rodent Malaria Model for Rapid, Accurate, and Consistent Parasite Counts.

Rodent malaria models serve as important preclinical antimalarial and vaccine testing tools. Evaluating treatment outcomes in these models often requi...

Probing Nanotopography-Mediated Macrophage Polarization via Integrated Machine Learning and Combinatorial Biophysical Cue Mapping.

Inflammatory responses, leading to fibrosis and potential host rejection, significantly hinder the long-term success and widespread adoption of biomed...

Deep-Learning-Based Blood Glucose Detection Device Using Acetone Exhaled Breath Sensing Features of α-FeO-MWCNT Nanocomposites.

Owing to the correlation between acetone in human's exhaled breath (EB) and blood glucose, the development of EB acetone gas-sensing devices is import...

Two-step deep learning models for detection and identification of the manufacturers and types of dental implants on panoramic radiographs.

The purpose of this study is to develop two-step deep learning models that can automatically detect implant regions on panoramic radiographs and ident...

Development of an artificial intelligence model for predicting implant size in total knee arthroplasty using simple X-ray images.

BACKGROUND: Accurate estimation of implant size before surgery is crucial in preparing for total knee arthroplasty. However, this task is time-consumi...

Digital Biomarker for Muscle Function Assessment Using Surface Electromyography With Electrical Stimulation and a Non-Invasive Wearable Device.

Sarcopenia is a comprehensive degenerative disease with the progressive loss of skeletal muscle mass with age, accompanied by the loss of muscle stren...

COVID-19 Vaccine: Predicting Vaccine Types and Assessing Mortality Risk Through Ensemble Learning Algorithms.

BACKGROUND: There is no doubt that vaccination is crucial for preventing the spread of diseases; however, not every vaccine is perfect or will work fo...

LERCause: Deep learning approaches for causal sentence identification from nuclear safety reports.

Identifying causal sentences from nuclear incident reports is essential for advancing nuclear safety research and applications. Nonetheless, accuratel...

Feasibility of Artificial Intelligence Powered Adverse Event Analysis: Using a Large Language Model to Analyze Microwave Ablation Malfunction Data.

Determine if a large language model (LLM, GPT-4) can label and consolidate and analyze interventional radiology (IR) microwave ablation device safety...