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Deep learning based estimation of heart surface potentials.

Electrocardiographic imaging (ECGI) aims to noninvasively estimate heart surface potentials starting from body surface potentials. This is classically based on geometric information on the torso and the heart from imaging, which complicates clinical application. In this study, we aim to develop a deep learning framework to estimate heart surface potentials solely from body surface potentials, enab...

Deep learning for hepatocellular carcinoma recurrence before and after liver transplantation: a multicenter cohort study.

Hepatocellular carcinoma (HCC) recurrence after liver transplantation (LT) is a major contributor to mortality. We developed a recurrence prediction system for HCC patients before and after LT. Data from patients with HCC who underwent LT were retrospectively collected from three specialist centres in China. Pre- and post-operative variables were selected using support vector machine, random fores...

Efficacy and Safety of a Medical Robot for Non-Face-to-Face Nasopharyngeal Swab Specimen Collection: Nonclinical and Clinical Trial Findings for COVID-19 Testing.

ObjectivesTo meet the high demand for polymerase chain reaction (PCR) tests to diagnose COVID-19 and rapidly control the outbreak, an efficient and sa...

Artificial intelligence models predicting abnormal uterine bleeding after COVID-19 vaccination.

The rapid deployment of COVID-19 vaccines has necessitated the ongoing surveillance of adverse events, with abnormal uterine bleeding (AUB) emerging a...

The EU project Real4Reg: unlocking real-world data with AI.

BACKGROUND: The use of real-world data is established in post-authorization regulatory processes such as pharmacovigilance of drugs and medical device...

Post-Mortem imaging biobanks: Building data for reproducibility, standardization, and AI integration.

In recent years, post-mortem imaging has advanced with techniques such as Post-Mortem Computed Tomography (PMCT) and Post-Mortem Magnetic Resonance im...

Role of artificial intelligence in data-centric additive manufacturing processes for biomedical applications.

The role of additive manufacturing (AM) for healthcare applications is growing, particularly in the aspiration to meet subject-specific requirements. ...

Machine-learning random forest algorithms predict post-cycloplegic myopic corrections from noncycloplegic clinical data.

SIGNIFICANCE: Machine learning random forest algorithms were used to predict objective refractive outcomes after cycloplegic refraction using noncyclo...

Novel Robotic Balloon-Based Device for Wrist-Extension Therapy of Hemiparesis Stroke Patients.

Upper-limb paresis is one of the main complications after stroke. It is commonly associated with impaired wrist-extension function. Upper-limb paresis...

ADFQ-ViT: Activation-Distribution-Friendly post-training Quantization for Vision Transformers.

Vision Transformers (ViTs) have exhibited exceptional performance across diverse computer vision tasks, while their substantial parameter size incurs ...

MERIT: Multi-view evidential learning for reliable and interpretable liver fibrosis staging.

Accurate staging of liver fibrosis from magnetic resonance imaging (MRI) is crucial in clinical practice. While conventional methods often focus on a ...

Predicting PTSD development with early post-trauma assessments: a proof-of-concept for a concise tree-based classification method.

Approximately 70% of individuals globally experience at least one traumatic event in their lifetimes, potentially leading to posttraumatic stress dis...

Identifying plastic materials in post-consumer food containers and packaging waste using terahertz spectroscopy and machine learning.

Accurate identification of plastic materials from post-consumer food container and packaging waste is crucial for enhancing the purity and added value...

Artificial intelligence for individualized treatment of persistent atrial fibrillation: a randomized controlled trial.

Although pulmonary vein isolation (PVI) has become the cornerstone ablation procedure for atrial fibrillation (AF), the optimal ablation procedure for...

A unique AI-based tool for automated segmentation of pulp cavity structures in maxillary premolars on CBCT.

To develop and validate an artificial intelligence (AI)-driven tool for the automatic segmentation of pulp cavity structures in maxillary premolars te...

Exploring Potential Medications for Alzheimer's Disease with Psychosis by Integrating Drug Target Information into Deep Learning Models: A Data-Driven Approach.

Approximately 50% of Alzheimer's disease (AD) patients develop psychotic symptoms, leading to a subtype known as psychosis in AD (AD + P), which is as...

Artificial intelligence (AI) in pharmacovigilance: A systematic review on predicting adverse drug reactions (ADR) in hospitalized patients.

INTRODUCTION: Adverse drug reactions (ADRs) significantly impact healthcare systems, leading to increased hospitalization rates and costs. With the gr...

Comparison of time-to-event machine learning models in predicting biliary complication and mortality rate in liver transplant patients.

Post-Liver transplantation (LT) survival rates stagnate, with biliary complications (BC) as a major cause of death. We analyzed longitudinal data with...

Deep Learning Model Compression and Hardware Acceleration for High-Performance Foreign Material Detection on Poultry Meat Using NIR Hyperspectral Imaging.

Ensuring the safety and quality of poultry products requires efficient detection and removal of foreign materials during processing. Hyperspectral ima...

Machine Learning-Based Pediatric Early Warning Score: Patient Outcomes in a Pre- Versus Post-Implementation Study, 2019-2023.

OBJECTIVES: To describe the deployment of pediatric Calculated Assessment of Risk and Triage (pCART), a machine learning (ML) model to predict the ris...