BACKGROUND: Identifying patients eligible for clinical trials through eligibility screening is time and resource-intensive. Natural Language Processing (NLP) models may enhance clinical trial screening by extracting data from Electronic Health Record...
BACKGROUND: Neurological disorders, particularly Parkinson's Disease (PD), are serious and progressive conditions that significantly impact patients' motor functions and overall quality of life. Accurate and timely diagnosis is still crucial, but it ...
PURPOSE: To develop three novel Vision Transformer (ViT) frameworks for the specific diagnosis of bacterial and fungal keratitis using different types of anterior segment images and compare their performances.
Noise reduction is essential to improve the diagnostic quality of low-dose CT (LDCT) images. In this regard, data-driven denoising methods based on generative adversarial networks (GAN) have shown promising results. However, custom designs with 2D co...
Impaired wound healing is a significant clinical challenge. Standard wound analysis approaches are macroscopic, with limited histological assessments that rely on visual inspection of haematoxylin and eosin (H&E)-stained sections of biopsies. The ana...
Cardiac ultrasound (US) scanning is one of the most commonly used techniques in cardiology to diagnose the health of the heart and its proper functioning. During a typical US scan, medical professionals take several images of the heart to be classifi...
PURPOSE: This study aimed to compare a conventional three-dimensional (3-D) magnetic resonance cholangiopancreatography (MRCP) sequence with a deep learning (DL)-accelerated MRCP sequence (hereafter, MRCP) regarding acquisition time and image quality...
PURPOSE: Low-dose CT protocols are widely used for emergency imaging, follow-ups, and attenuation correction in hybrid PET/CT and SPECT/CT imaging. However, low-dose CT images often suffer from reduced quality depending on acquisition and patient att...
With the rapid advancement of artificial intelligence in health care, large language models (LLMs) demonstrate increasing potential in medical applications. However, their performance in specialized oncology remains limited. This study evaluates the...
BACKGROUND: The integration of artificial intelligence (AI) into medical practice opens up new frontiers for decision support, especially in intricate surgical procedures like one-anastomosis gastric bypass (OAGB). This study was designed to showcase...
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