Care of terminally ill / Palliative care

Research on Upper Limb Motion Intention Classification and Rehabilitation Robot Control Based on sEMG.

sEMG is a non-invasive biomedical engineering technique that can detect and record electrical signals generated by muscles, reflecting both motor intentions and the degree of muscle contraction. This study aims to classify and recognize nine types of upper limb motor intentions based on surface electromyography (sEMG) and apply them to the interactive control of an end-effector rehabilitation robo...

End-To-End Deep Learning Explains Antimicrobial Resistance in Peak-Picking-Free MALDI-MS Data.

Mass spectrometry is used to determine infectious microbial species in thousands of clinical laboratories across the world. The vast amount of data allows modern data analysis methods that harvest more information and potentially answer new questions. Here, we present an end-to-end deep learning model for predicting antibiotic resistance using raw matrix assisted laser desorption ionization mass s...

DO-GMA: An End-to-End Drug-Target Interaction Identification Framework with a Depthwise Overparameterized Convolutional Network and the Gated Multihead Attention Mechanism.

Identification of potential drug-target interactions (DTIs) is a crucial step in drug discovery and repurposing. Although deep learning effectively de...

Deep one-class probability learning for end-to-end image classification.

One-class learning has many application potentials in novelty, anomaly, and outlier detection systems. It aims to distinguish both positive and negati...

Perspective: Multiomics and Artificial Intelligence for Personalized Nutritional Management of Diabetes in Patients Undergoing Peritoneal Dialysis.

Managing diabetes in patients on peritoneal dialysis (PD) is challenging due to the combined effects of dietary glucose, glucose from dialysate, and o...

Clinical validation of explainable AI for fetal growth scans through multi-level, cross-institutional prospective end-user evaluation.

We aimed to develop and evaluate Explainable Artificial Intelligence (XAI) for fetal ultrasound using actionable concepts as feedback to end-users, us...

When multiple instance learning meets foundation models: Advancing histological whole slide image analysis.

Deep multiple instance learning (MIL) pipelines are the mainstream weakly supervised learning methodologies for whole slide image (WSI) classification...

Current state and promise of user-centered design to harness explainable AI in clinical decision-support systems for patients with CNS tumors.

In neuro-oncology, MR imaging is crucial for obtaining detailed brain images to identify neoplasms, plan treatment, guide surgical intervention, and m...

End-to-end deep-learning model for the detection of coronary artery stenosis on coronary CT images.

PURPOSE: We examined whether end-to-end deep-learning models could detect moderate (≥50%) or severe (≥70%) stenosis in the left anterior descending ar...

Haptic Shared Control Framework with Interaction Force Constraint Based on Control Barrier Function for Teleoperation.

Current teleoperated robotic systems for retinal surgery cannot effectively control subtle tool-to-tissue interaction forces. This limitation may lead...

Constructing a metadata knowledge graph as an atlas for demystifying AI pipeline optimization.

The emergence of advanced artificial intelligence (AI) models has driven the development of frameworks and approaches that focus on automating model t...

Evaluation of Machine Learning/Molecular Mechanics End-State Corrections with Mechanical Embedding to Calculate Relative Protein-Ligand Binding Free Energies.

The development of machine-learning (ML) potentials offers significant accuracy improvements compared to molecular mechanics (MM) because of the inclu...

An end-to-end implicit neural representation architecture for medical volume data.

Medical volume data are rapidly increasing, growing from gigabytes to petabytes, which presents significant challenges in organisation, storage, trans...

MRI-derived radiomics and end-to-end deep learning models for predicting glioma ATRX status: a systematic review and meta-analysis of diagnostic test accuracy studies.

We aimed to systematically review and meta-analyze the predictive value of magnetic resonance imaging (MRI)-derived radiomics/end-to-end deep learning...

The Potential of SHAP and Machine Learning for Personalized Explanations of Influencing Factors in Myopic Treatment for Children.

The rising prevalence of myopia is a significant global health concern. Atropine eye drops are commonly used to slow myopia progression in children, ...

Combination of deep learning reconstruction and quantification for dynamic contrast-enhanced (DCE) MRI.

Dynamic contrast-enhanced (DCE) MRI is an important imaging tool for evaluating tumor vascularity that can lead to improved characterization of tumor ...

ItpCtrl-AI: End-to-end interpretable and controllable artificial intelligence by modeling radiologists' intentions.

Using Deep Learning in computer-aided diagnosis systems has been of great interest due to its impressive performance in the general domain and medical...

End-to-end deep learning patient level classification of affected territory of ischemic stroke patients in DW-MRI.

PURPOSE: To develop an end-to-end DL model for automated classification of affected territory in DWI of stroke patients.

CTsynther: Contrastive Transformer Model for End-to-End Retrosynthesis Prediction.

Retrosynthesis prediction is a fundamental problem in organic chemistry and drug synthesis. We proposed an end-to-end deep learning model called CTsyn...

An End-to-End Knowledge Graph Fused Graph Neural Network for Accurate Protein-Protein Interactions Prediction.

Protein-protein interactions (PPIs) are essential to understanding cellular mechanisms, signaling networks, disease processes, and drug development, a...