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Machine learning-driven national analysis for predicting adverse outcomes in intramedullary spinal cord tumor surgery.

UNLABELLED: Spinal tumors represent 15% of all central nervous system malignancies, with intramedull...

h-calibration: Rethinking Classifier Recalibration with Probabilistic Error-Bounded Objective.

Deep neural networks have demonstrated remarkable performance across numerous learning tasks but oft...

A 0.66-mm 0.49 pJ/SOP SNN Processor with Temporal-Spatial Post-Neuron-Processing and Model-Adaptive Crossbar in 40-nm CMOS.

This paper presents a Spiking Neural Network (SNN) processor specifically designed to overcome the l...

Ultrasound Displacement Tracking Techniques for Post-Stroke Myofascial Shear Strain Quantification.

OBJECTIVE: Ultrasound shear strain is a potential biomarker of myofascial dysfunction. However, the ...

Patient acceptability of CITOBOT for cervical cancer screening: A mixed-method study.

This study assessed the acceptability of CITOBOT, a device for early cervical cancer screening in a ...

The value of multimodal neuroimaging in the diagnosis and treatment of post-traumatic stress disorder: a narrative review.

Post-traumatic stress disorder (PTSD) is a delayed-onset or prolonged persistent psychiatric disorde...

An In-depth overview of artificial intelligence (AI) tool utilization across diverse phases of organ transplantation.

Artificial Intelligence (AI) offers a revolutionary approach to improve decision-making in medicine ...

Long-term COVID-19 symptoms and post-vaccination reactions among prolonged COVID-19 patients in the Kurdistan region of Iraq.

Vaccination has long been recognized as the most effective means for disease prevention, yet concern...

A Post-Quantum Blockchain and Autonomous AI-Enabled Scheme for Secure Healthcare Information Exchange.

Secure healthcare information exchange (HIE) is critical to improving medical services, enabling dat...

Precision-Optimised Post-Stroke Prognoses.

BACKGROUND: Current medicine cannot confidently predict who will recover from post-stroke impairment...

SCAI-Net: An AI-driven framework for optimized, fast, and resource-efficient skull implant generation for cranioplasty using CT images.

Skull damage caused by craniectomy or trauma necessitates accurate and precise Patient-Specific Impl...

The illusion of safety: A report to the FDA on AI healthcare product approvals.

Artificial intelligence is rapidly transforming healthcare, offering promising advancements in diagn...

A Synergistic Approach Using Photoacoustic Spectroscopy and AI-Based Image Analysis for Post-Harvest Quality Assessment of Conference Pears.

This study presents a non-invasive approach to monitoring post-harvest fruit quality by applying CO ...

IDEA: Image database for earthquake damage annotation.

The data article presents the "Image Database for Earthquake damage Annotation (IDEA)", an extended ...

Incorporating the STOP-BANG questionnaire improves prediction of cardiovascular events during hospitalization after myocardial infarction.

Obstructive sleep apnea (OSA) may impact outcomes in acute coronary syndrome (ACS) patients. The Glo...

Predicting Resistance and Survival of HCC Patients Post-HAIC: Based on Shapley Additive exPlanations and Machine Learning.

PURPOSE: To establish prediction models using Shapley Additive exPlanations (SHAP) and multiple mach...

Identifying and predicting headache trajectories among those with acute post-traumatic headache.

OBJECTIVES/BACKGROUND: Post-traumatic headache (PTH) is a common symptom following mild traumatic br...

Machine Learning-Based Prediction of Post-Operative Systemic Inflammatory Response Syndrome Following Pediatric Percutaneous Nephrolithotripsy.

OBJECTIVE: This study aimed to develop and validate a machine learning-based model for predicting sy...

Toward Faithful Neural Network Intrinsic Interpretation With Shapley Additive Self-Attribution.

Self-interpreting neural networks have attracted significant attention from the research community. ...

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