Recent scholarship has highlighted the value of therapists adopting a multicultural orientation (MCO) within psychotherapy. A newly developed performance-based measure of MCO capacities exists (MCO-performance task [MCO-PT]) in which therapists respo...
OBJECTIVES: This study aims to develop computer-aided detection (CAD) for colorectal cancer (CRC) using abdominal CT based on a deep convolutional neural network.
RATIONALE AND OBJECTIVES: Using different machine learning models CT-based radiomics to integrate clinical radiological features to discriminating the risk stratification of pheochromocytoma/paragangliomas (PPGLs).
OBJECTIVE: Increased subcutaneous and visceral adipose tissue (SAT/VAT) volume is associated with risk for cardiometabolic diseases. This work aimed to develop and evaluate automated abdominal SAT/VAT segmentation on longitudinal MRI in adults with o...
BACKGROUND AND OBJECTIVE: Machine learning (ML) is a subset of artificial intelligence that uses data to build algorithms to predict specific outcomes. Few ML studies have examined percutaneous nephrolithotomy (PCNL) outcomes. Our objective was to bu...
INTRODUCTION: Coexisting multiple health conditions is common among older people, a population that is increasing globally. The potential for polypharmacy, adverse events, drug interactions and development of additional health conditions complicates ...
BACKGROUND/OBJECTIVES: Study of retinal structure based on optical coherence tomography (OCT) data can facilitate early diagnosis of relapsing-remitting multiple sclerosis (RRMS). Although artificial intelligence can provide highly reliable diagnoses...
Journal of applied clinical medical physics
Jan 31, 2024
PURPOSE: This study aimed to develop an automated method that uses a convolutional neural network (CNN) for calculating size-specific dose estimates (SSDEs) based on the corrected effective diameter (D ) in thoracic computed tomography (CT).
International journal of legal medicine
Jan 30, 2024
BACKGROUND: Radiological age assessment using reference studies is inherently limited in accuracy due to a finite number of assignable skeletal maturation stages. To overcome this limitation, we present a deep learning approach for continuous age ass...
Dental age estimation, a cornerstone in forensic age assessment, has been extensively tried and tested, yet manual methods are impeded by tedium and interobserver variability. Automated approaches using deep transfer learning encounter challenges lik...
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.