OBJECTIVE: To review biomarker discovery studies using omics data for patient stratification which led to clinically validated FDA-cleared tests or laboratory developed tests, in order to identify common characteristics and derive recommendations for...
OBJECTIVES: We aimed at identifying the important variables for labour induction intervention and assessing the predictive performance of machine learning algorithms.
INTRODUCTION: Nipple-sparing mastectomy (NSM) can be performed for the treatment of breast cancer and risk reduction, but total mammary glandular excision in NSM can be technically challenging. Minimally invasive robot-assisted NSM (RNSM) has the pot...
INTRODUCTION: Deep learning techniques are gaining momentum in medical research. Evidence shows that deep learning has advantages over humans in image identification and classification, such as facial image analysis in detecting people's medical cond...
OBJECTIVES: This systematic review aimed to assess the performance and clinical feasibility of machine learning (ML) algorithms in prediction of in-hospital mortality for medical patients using vital signs at emergency departments (EDs).
INTRODUCTION: The application of artificial intelligence (AI) technologies as a diagnostic aid in healthcare is increasing. Benefits include applications to improve health systems, such as rapid and accurate interpretation of medical images. This may...
INTRODUCTION: Social isolation is a significant issue in aged care settings (eg, long-term care (LTC) and hospital) and is associated with adverse outcomes such as reduced well-being and loneliness. Loneliness is linked with depression, anxiety, cogn...
OBJECTIVES: Obstructive sleep apnoea (OSA) has received much attention as a risk factor for perioperative complications and 68.5% of OSA patients remain undiagnosed before surgery. Faciocervical characteristics may screen OSA for Asians due to smalle...
OBJECTIVES: The purpose of this scoping review is to: (1) identify existing supervised machine learning (ML) approaches on the prediction of cancer in asymptomatic adults; (2) to compare the performance of ML models with each other and (3) to identif...
OBJECTIVES: The aim of this study was to evaluate the performance of deep learning-based detection and classification of carotid plaque (DL-DCCP) in carotid plaque contrast-enhanced ultrasound (CEUS).