The purpose of this study was to evaluate the performance of the deep convolutional neural
network (DCNN) in differentiating between tuberculous and pyogenic spondylitis on …

Purpose To explore the application of deep learning (DL) methods based on T2 sagittal MR
images for discriminating between spinal tuberculosis (STB) and spinal metastases (SM) …

Background: Ankylosing spondylitis (AS) is a chronic, painful, progressive disease usually
seen in the spine. Traditional diagnostic methods have limitations in detecting the early …

Study Design. Multicenter retrospective observational study. Objective. This study aimed to
distinguish tuberculous spondylitis (TS) from pyogenic spondylitis (PS) using magnetic …

Aim: We evaluated the potential of magnetic resonance imaging (MRI) in the diagnosis of
spinal infections and specifically its accuracy in differentiating tubercular and pyogenic …

Background Pyogenic spondylitis (PS) and Brucella spondylitis (BS) are commonly seen
spinal infectious diseases. Both types can lead to vertebral destruction, kyphosis, and long …

Background Spinal tuberculosis (TB) has the highest incidence in remote plateau areas,
particularly in Tibet, China, due to inadequate local healthcare services, which not only …

Objectives This study aims to develop a deep learning algorithm, Pneumonia-Plus, based
on computed tomography (CT) images for accurate classification of bacterial, fungal, and …

Background Deep learning (DL) is an advanced machine learning approach used in
different areas such as image analysis, bioinformatics, and natural language processing. A …

Purpose To evaluate the efficacy of deep convolutional neural networks (DCNNs) for
detecting tuberculosis (TB) on chest radiographs. Materials and Methods Four deidentified …