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Amino acid metabolomics and machine learning-driven assessment of future liver remnant growth after hepatectomy in livers of various backgrounds.

Accurate assessment of future liver remnant growth after partial hepatectomy (PH) in patients with d...

Machine Learning Predicts Unplanned Care Escalations for Post-Anesthesia Care Unit Patients during the Perioperative Period: A Single-Center Retrospective Study.

BACKGROUND:  Despite low mortality for elective procedures in the United States and developed countr...

EfficientQ: An efficient and accurate post-training neural network quantization method for medical image segmentation.

Model quantization is a promising technique that can simultaneously compress and accelerate a deep n...

Uncertainty quantification via localized gradients for deep learning-based medical image assessments.

Deep learning models that aid in medical image assessment tasks must be both accurate and reliable t...

Predicting Post-surgery Discharge Time in Pediatric Patients Using Machine Learning.

BACKGROUND: Prolonged hospital stays after pediatric surgeries, such as tonsillectomy and adenoidect...

AI-enabled ECG index for predicting left ventricular dysfunction in patients with ST-segment elevation myocardial infarction.

Electrocardiogram (ECG) changes after primary percutaneous coronary intervention (PCI) in ST-segment...

Finite element models with automatic computed tomography bone segmentation for failure load computation.

Bone segmentation is an important step to perform biomechanical failure load simulations on in-vivo ...

Direct Comparisons of Upper-Limb Motor Learning Performance Among Three Types of Haptic Guidance With Non-Assisted Condition in Spiral Drawing Task.

In robot-assisted rehabilitation, it is unclear which type of haptic guidance is effective for regai...

Predictive Modeling of Long-Term Prognosis After Resection in Typical Pulmonary Carcinoid: A Machine Learning Perspective.

Typical Pulmonary Carcinoid (TPC) is defined by its slow growth, frequently necessitating surgical i...

Advancing medical imaging: detecting polypharmacy and adverse drug effects with Graph Convolutional Networks (GCN).

Polypharmacy involves an individual using many medications at the same time and is a frequent health...

Regularized ensemble learning for prediction and risk factors assessment of students at risk in the post-COVID era.

The COVID-19 pandemic has had a significant impact on students' academic performance. The effects of...

Uveal melanoma distant metastasis prediction system: A retrospective observational study based on machine learning.

Uveal melanoma (UM) patients face a significant risk of distant metastasis, closely tied to a poor p...

Clinician perceptions of a novel wearable robotic hand orthosis for post-stroke hemiparesis.

PURPOSE: Wearable robotic devices are currently being developed to improve upper limb function for i...

Surface electromyography vs clinical outcome measures after robot-assisted gait training in patients with spinal cord injury after post-acute phase of rehabilitation.

INTRODUCTION AND OBJECTIVE: Surface electromyography (sEMG) measurements are a valid method for subl...

Machine Learning Based Prediction of Post-operative Infrarenal Endograft Apposition for Abdominal Aortic Aneurysms.

OBJECTIVE: Challenging infrarenal aortic neck characteristics have been associated with an increased...

Deep Learning-Enhanced Internet of Things for Activity Recognition in Post-Stroke Rehabilitation.

Wearable sensors provide a more effective means of activity monitoring and management by recording p...

Predicting recovery following stroke: Deep learning, multimodal data and feature selection using explainable AI.

Machine learning offers great potential for automated prediction of post-stroke symptoms and their r...

A comparative analysis of deep learning and hybrid iterative reconstruction algorithms with contrast-enhancement-boost post-processing on the image quality of indirect computed tomography venography of the lower extremities.

PURPOSE: To examine whether there is a significant difference in image quality between the deep lear...

Development and validation of an interpretable machine learning model for predicting post-stroke epilepsy.

BACKGROUND: Epilepsy is a serious complication after an ischemic stroke. Although two studies have d...

Commentary on "develop a radiomics-based machine learning model to predict the stone-free rate post-percutaneous nephrolithotomy".

A novel virtual robotic platform for controlling six degrees of freedom assistive devices with body-machine interfaces.

Body-machine interfaces (BoMIs)-systems that control assistive devices (e.g., a robotic manipulator)...