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Efficient Seizure Detection by Complementary Integration of Convolutional Neural Network and Vision Transformer.

Epilepsy, as a prevalent neurological disorder, is characterized by its high incidence, sudden onset...

Circulating microRNA profiles are associated with acute pain and stress in castrated and tail docked lambs.

Maintaining animal welfare is an essential component of animal production systems. However, multiple...

SGCLMD: Signed graph-based contrastive learning model for predicting somatic mutation-drug association.

Somatic mutations could influence critical cellular processes, leading to uncontrolled cell growth a...

Automated segmentation of brain metastases in T1-weighted contrast-enhanced MR images pre and post stereotactic radiosurgery.

BACKGROUND AND PURPOSE: Accurate segmentation of brain metastases on Magnetic Resonance Imaging (MRI...

Post-Anesthesia Care Unit (PACU) readiness predictions using machine learning: a comparative study of algorithms.

INTRODUCTION: Accurate and timely discharge from the Post-Anesthesia Care Unit (PACU) is essential t...

Personalized prediction of psoriasis relapse post-biologic discontinuation: a machine learning-driven population cohort study.

BACKGROUND: Identifying the risk of psoriasis relapse after discontinuing biologics can help optimiz...

Artificial intelligence-based incisive canal visualization for preventing and detecting post-implant injury, using cone beam computed tomography.

The aim of this study was to clinically validate an artificial intelligence (AI)-based tool for auto...

Predicting orthognathic surgery results as postoperative lateral cephalograms using graph neural networks and diffusion models.

Orthognathic surgery, or corrective jaw surgery, is performed to correct severe dentofacial deformit...

Semi-Automated Multi-Label Classification of Autistic Mannerisms by Machine Learning on Post Hoc Skeletal Tracking.

Mannerisms describe repetitive or unconventional body movements like arm flapping. These movements a...

Risk of bias assessment of post-stroke mortality machine learning predictive models: Systematic review.

BACKGROUND: Stroke is a major cause of mortality and permanent disability worldwide. Precise predict...

Using machine learning methods to predict the outcome of psychological therapies for post-traumatic stress disorder: A systematic review.

BACKGROUND: A number of treatments are available for post-traumatic stress disorder (PTSD), however,...

Unsupervised machine learning identifies biomarkers of disease progression in post-kala-azar dermal leishmaniasis in Sudan.

BACKGROUND: Post-kala-azar dermal leishmaniasis (PKDL) appears as a rash in some individuals who hav...

Post-Bariatric Hypoglycemia After Gastric Bypass: Clinical Characteristics, Risk Factors, and Future Directions-A Response to Grover et al.

BACKGROUND: Post-bariatric hypoglycemia (PBH) after Roux-en-Y gastric bypass (RYGB) is a complex com...

How Can Robotic Devices Help Clinicians Determine the Treatment Dose for Post-Stroke Arm Paresis?

Upper limb training dose after stroke is usually quantified by time and repetitions. This study anal...

Deep learning based estimation of heart surface potentials.

Electrocardiographic imaging (ECGI) aims to noninvasively estimate heart surface potentials starting...

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...

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