[HTML][HTML] The promise of artificial intelligence and deep learning in PET and SPECT imaging
This review sets out to discuss the foremost applications of artificial intelligence (AI),
particularly deep learning (DL) algorithms, in single-photon emission computed tomography …
particularly deep learning (DL) algorithms, in single-photon emission computed tomography …
Methods for clinical evaluation of artificial intelligence algorithms for medical diagnosis
Adequate clinical evaluation of artificial intelligence (AI) algorithms before adoption in
practice is critical. Clinical evaluation aims to confirm acceptable AI performance through …
practice is critical. Clinical evaluation aims to confirm acceptable AI performance through …
[HTML][HTML] Machine learning-based prognostic modeling using clinical data and quantitative radiomic features from chest CT images in COVID-19 patients
Objective To develop prognostic models for survival (alive or deceased status) prediction of
COVID-19 patients using clinical data (demographics and history, laboratory tests, visual …
COVID-19 patients using clinical data (demographics and history, laboratory tests, visual …
COVID-19 diagnosis: current and future techniques
COVID-19 pandemic continues to be a global threat, affecting more than 200
countries/territories at both human and economic level. This necessitates the rapid …
countries/territories at both human and economic level. This necessitates the rapid …
Artificial intelligence-driven assessment of radiological images for COVID-19
Y Bouchareb, PM Khaniabadi, F Al Kindi… - Computers in biology …, 2021 - Elsevier
Artificial Intelligence (AI) methods have significant potential for diagnosis and prognosis of
COVID-19 infections. Rapid identification of COVID-19 and its severity in individual patients …
COVID-19 infections. Rapid identification of COVID-19 and its severity in individual patients …
On the role of artificial intelligence in medical imaging of COVID-19
Although a plethora of research articles on AI methods on COVID-19 medical imaging are
published, their clinical value remains unclear. We conducted the largest systematic review …
published, their clinical value remains unclear. We conducted the largest systematic review …
A review on deep learning approaches for low-dose computed tomography restoration
Computed Tomography (CT) is a widely use medical image modality in clinical medicine,
because it produces excellent visualizations of fine structural details of the human body. In …
because it produces excellent visualizations of fine structural details of the human body. In …
Deep learning-based auto-segmentation of organs at risk in high-dose rate brachytherapy of cervical cancer
Background and purpose Delineation of organs at risk (OARs), such as the bladder, rectum
and sigmoid, plays an important role in the delivery of optimal absorbed dose to the target …
and sigmoid, plays an important role in the delivery of optimal absorbed dose to the target …
COLI‐Net: deep learning‐assisted fully automated COVID‐19 lung and infection pneumonia lesion detection and segmentation from chest computed tomography …
We present a deep learning (DL)‐based automated whole lung and COVID‐19 pneumonia
infectious lesions (COLI‐Net) detection and segmentation from chest computed tomography …
infectious lesions (COLI‐Net) detection and segmentation from chest computed tomography …
Robust-Deep: a method for increasing brain imaging datasets to improve deep learning models' performance and robustness
A small dataset commonly affects generalization, robustness, and overall performance of
deep neural networks (DNNs) in medical imaging research. Since gathering large clinical …
deep neural networks (DNNs) in medical imaging research. Since gathering large clinical …