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A Multicenter Cohort Study on Ultrasound-based Deep Learning Nomogram for Predicting Post-Neoadjuvant Chemotherapy Axillary Lymph Node Status in Breast Cancer Patients.

RATIONALE AND OBJECTIVES: The aim of this study was to evaluate the capability of an ultrasound (US)...

A novel mean shape based post-processing method for enhancing deep learning lower-limb muscle segmentation accuracy.

This study aims at improving the lower-limb muscle segmentation accuracy of deep learning approaches...

A Lightweight Convolutional Neural Network-Reformer Model for Efficient Epileptic Seizure Detection.

A real-time and reliable automatic detection system for epileptic seizures holds significant value i...

Machine-learning-based models for the optimization of post-cervical spinal laminoplasty outpatient follow-up schedules.

BACKGROUND: Patients undergo regular clinical follow-up after laminoplasty for cervical myelopathy. ...

Machine learning for post-liver transplant survival: Bridging the gap for long-term outcomes through temporal variation features.

BACKGROUND: The long-term survival of liver transplant (LT) recipients is essential for optimizing o...

Safeguarding Patients in the AI Era: Ethics at the Forefront of Pharmacovigilance.

Artificial intelligence is increasingly being used in pharmacovigilance. However, the use of artific...

Developing machine learning models for personalized treatment strategies in early breast cancer patients undergoing neoadjuvant systemic therapy based on SEER database.

This study aimed to compare the long-term outcomes of breast-conserving surgery plus radiotherapy (B...

Paying attention to uncertainty: A stochastic multimodal transformers for post-traumatic stress disorder detection using video.

BACKGROUND AND OBJECTIVES: Post-traumatic stress disorder is a debilitating psychological condition ...

Predicting Outcomes of Preterm Neonates Post Intraventricular Hemorrhage.

Intraventricular hemorrhage (IVH) in preterm neonates presents a high risk for developing posthemorr...

Optimizing Real-Time MI-BCI Performance in Post-Stroke Patients: Impact of Time Window Duration on Classification Accuracy and Responsiveness.

Brain-computer interfaces (BCIs) are promising tools for motor neurorehabilitation. Achieving a bala...

Exploration of fractional flow reservation score based on artificial intelligence post-processing for coronary artery lesions in patients with diabetes and coronary heart disease.

In order to evaluate the relationship between coronary heart disease (CHD) and fractional flow reser...

Evaluation and analysis of risk factors for adverse events of the fractured vertebra post-percutaneous kyphoplasty: a retrospective cohort study using multiple machine learning models.

BACKGROUND: Adverse events of the fractured vertebra (AEFV) post-percutaneous kyphoplasty (PKP) can ...

Adaptive Machine Learning Systems in Medicine: The Post-Authorization Phase.

A Novel Bilateral Underactuated Upper Limb Exoskeleton for Post-Stroke Bimanual ADL Training.

This paper introduces a lightweight bilateral underactuated upper limb exoskeleton (UULE) designed t...

Regional and National Trends in Tuberculosis Research in South Asian Association for Regional Cooperation Countries: Post-COVID-19 Pandemic Machine Learning Factorial Analysis.

BACKGROUND: The number of tuberculosis (TB)-related morbidities and mortalities is still high in the...

Logistic regression analysis and machine learning for predicting post-stroke gait independence: a retrospective study.

This study investigated whether machine learning (ML) has better predictive accuracy than logistic r...

A Pre-Voiding Alarm System Using Wearable Ultrasound and Machine Learning Algorithms for Children With Nocturnal Enuresis.

Nocturnal enuresis is a bothersome condition that affects many children and their caregivers. Post-v...

Enhancing Upper Limb Function and Motor Skills Post-Stroke Through an Upper Limb Rehabilitation Robot.

Cerebrovascular accidents, commonly known as strokes, represent a prevalent neurological event leadi...

MAPRS: An intelligent approach for post-prescription review based on multi-label learning.

Antimicrobial resistance (AMR) is a major threat to public health worldwide. It is a promising way t...

Multi-metric comparison of machine learning imputation methods with application to breast cancer survival.

Handling missing data in clinical prognostic studies is an essential yet challenging task. This stud...

Development and Internal-External Validation of a Post-Operative Mortality Risk Calculator for Pediatric Surgical Patients in Low- and Middle- Income Countries Using Machine Learning.

BACKGROUND: The purpose of this study was to develop and validate a mortality risk algorithm for ped...