Accurate vertebrae segmentation is crucial for modern surgical technologies, and deep learning networks provide valuable tools for this task. This study explores the application of advanced deep learning-based methods for segmenting vertebrae in comp...
AIM: To assess the predictive performance, risk stratification capabilities, and auxiliary diagnostic utility of radiomics, deep learning, and fusion models in identifying visceral pleural invasion (VPI) in lung adenocarcinoma.
This study aimed to compare two deep learning reconstruction (DLR) techniques (AiCE mild; AiCE strong) with two established methods-iterative reconstruction (IR) and filtered back projection (FBP)-for the detection of monosodium urate (MSU) in dual-e...
Chronic obstructive pulmonary disease (COPD) is considered to be one of the most commonly occurring respiratory disorders and is proliferating at an extremely high rate in the recent years. The proposed system aims to classify the various stages of C...
One possible adverse effect of breast irradiation is the development of pulmonary fibrosis. The aim of this study was to determine whether planning CT scans can predict which patients are more likely to develop lung lesions after treatment. A retrosp...
In recent years, scientists have been trying to convert magnetic resonance imaging (MRI) images into computed tomography (CT) images for dose calculations while taking advantage of the benefits of MRI images. The main approaches for image conversion ...
INTRODUCTION: Immune checkpoint inhibitor-related interstitial pneumonia (CIP) poses a diagnostic challenge due to its radiographic similarity to other pneumonias. We developed a non-invasive model using CT imaging to differentiate CIP from other pne...
BACKGROUND: Deep learning has been utilized to segment teeth from computed tomography (CT) or cone-beam CT (CBCT). However, the performance of deep learning is unknown due to multiple models and diverse evaluation metrics. This systematic review and ...
BACKGROUND: The risks and prognosis of mild intracerebral hemorrhage (ICH) patients were easily overlooked by clinicians. Our goal was to use machine learning (ML) methods to predict mild ICH patients' neurological deterioration (ND) and 90-day progn...
This study is aimed to clarify the effectiveness of the image-rotation technique and zooming augmentation to improve the accuracy of the deep learning (DL)-based dose conversion from pencil beam (PB) to Monte Carlo (MC) in proton beam therapy (PBT). ...
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