Devices and Vaccines

Automatic skull reconstruction by deep learnable symmetry enforcement.

BACKGROUND AND OBJECTIVE: Every year, thousands of people suffer from skull damage and require personalized implants to fill the cranial cavity. Unfortunately, the waiting time for reconstruction surgery can extend to several weeks or even months, especially in less developed countries. One factor contributing to the extended waiting period is the intricate process of personalized implant modeling...

Combining machine learning models and rule engines in clinical decision systems: Exploring optimal aggregation methods for vaccine hesitancy prediction.

BACKGROUND: With the increasing application of artificial intelligence (AI) technologies in the healthcare sector and the emergence of new solutions, such as large language models, there is a growing need to combine medical knowledge, often expressed as clinical rules, with advances in machine learning (ML) offering higher prediction accuracy at the expense of decision-making transparency.

Protocol for AI-based segmentation and quantification of interstitial cells of Cajal in murine gastric muscle.

Interstitial cells of Cajal (ICCs), pacemaker and neuromodulator cells in the gastrointestinal (GI) tract, play an important role in GI motility. Howe...

Deep learning-based video-level view classification of two-dimensional transthoracic echocardiography.

In recent years, deep learning (DL)-based automatic view classification of 2D transthoracic echocardiography (TTE) has demonstrated strong performance...

Deep learning based coronary vessels segmentation in X-ray angiography using temporal information.

Invasive coronary angiography (ICA) is the gold standard imaging modality during cardiac interventions. Accurate segmentation of coronary vessels in I...

Depth-sensor-based shared control assistance for mobility and object manipulation: toward long-term home-use of BCI-controlled assistive robotic devices.

Assistive robots can be developed to restore or provide more autonomy for individuals with motor impairments. In particular, power wheelchairs can com...

[Digital innovations in vaccination communication].

Despite the significant success of vaccinations, increasing vaccine hesitancy poses a threat to public health, making effective vaccination communicat...

Precise multi-factor immediate implant placement decision models based on machine learning.

This study aims to explore the effect of implant apex design, osteotomy preparation, intraosseous depth and bone quality on immediate implant placemen...

Deep learning-based prediction of possibility for immediate implant placement using panoramic radiography.

In this study, we investigated whether deep learning-based prediction of immediate implant placement is possible. Panoramic radiographs of 201 patient...

Of Lyme disease and machine learning in a One Health world.

OBJECTIVE: Lyme disease is a vector-borne emerging zoonosis in Ontario driven by human population growth and climate change. Lyme disease is also a pr...

Understanding Citizens' Response to Social Activities on Twitter in US Metropolises During the COVID-19 Recovery Phase Using a Fine-Tuned Large Language Model: Application of AI.

BACKGROUND: The COVID-19 pandemic continues to hold an important place in the collective memory as of 2024. As of March 2024, >676 million cases, 6 mi...

SHASI-ML: a machine learning-based approach for immunogenicity prediction in vaccine development.

INTRODUCTION: Accurate prediction of immunogenic proteins is crucial for vaccine development and understanding host-pathogen interactions in bacterial...

Smart IoT-based snake trapping device for automated snake capture and identification.

The threat of snakebites to public health, particularly in tropical and subtropical regions, requires effective mitigation strategies to avoid human-s...

EEG Temporal-Spatial Feature Learning for Automated Selection of Stimulus Parameters in Electroconvulsive Therapy.

The risk of adverse effects in Electroconvulsive Therapy (ECT), such as cognitive impairment, can be high if an excessive stimulus is applied to induc...

ADR-DQPU: A Novel ADR Signal Detection Using Deep Reinforcement and Positive-Unlabeled Learning.

The medical community has grappled with the challenge of analysis and early detection of severe and unknown adverse drug reactions (ADRs) from Spontan...

Exploration of the optimal deep learning model for english-Japanese machine translation of medical device adverse event terminology.

BACKGROUND: In Japan, reporting of medical device malfunctions and related health problems is mandatory, and efforts are being made to standardize ter...

Improving entity recognition using ensembles of deep learning and fine-tuned large language models: A case study on adverse event extraction from VAERS and social media.

OBJECTIVE: Adverse event (AE) extraction following COVID-19 vaccines from text data is crucial for monitoring and analyzing the safety profiles of imm...

Systematic collection, annotation, and pattern analysis of viral vaccines in the VIOLIN vaccine knowledgebase.

BACKGROUND: Viral vaccines have been proven significant in protecting us against viral diseases such as COVID-19. To better understand and design vira...

Forecasting the Incidence of Mumps Based on the Baidu Index and Environmental Data in Yunnan, China: Deep Learning Model Study.

BACKGROUND: Mumps is a viral respiratory disease characterized by facial swelling and transmitted through respiratory secretions. Despite the availabi...

Shoulder Musculoskeletal Disorder Rehabilitation Using a Robotic Device Based on Electromyography (EMG) Biofeedback: A Retrospective Cohort Study.

: While shoulder injuries represent the musculoskeletal disorders (MSDs) most encountered in physical therapy, there is no consensus on their manageme...