AIMC Topic: Sensitivity and Specificity
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Differential diagnosis of ameloblastoma and odontogenic keratocyst by machine learning of panoramic radiographs.
International journal of computer assisted radiology and surgery
Feb 6, 2021
PURPOSE: The differentiation of the ameloblastoma and odontogenic keratocyst directly affects the formulation of surgical plans, while the results of differential diagnosis by imaging alone are not satisfactory. This paper aimed to propose an algorit...
PACIFIC: a lightweight deep-learning classifier of SARS-CoV-2 and co-infecting RNA viruses.
Scientific reports
Feb 5, 2021
Viral co-infections occur in COVID-19 patients, potentially impacting disease progression and severity. However, there is currently no dedicated method to identify viral co-infections in patient RNA-seq data. We developed PACIFIC, a deep-learning alg...
Differentiation of Pseudoprogression from True Progressionin Glioblastoma Patients after Standard Treatment: A Machine Learning Strategy Combinedwith Radiomics Features from T-weighted Contrast-enhanced Imaging.
BMC medical imaging
Feb 3, 2021
BACKGROUND: Based on conventional MRI images, it is difficult to differentiatepseudoprogression from true progressionin GBM patients after standard treatment, which isa critical issue associated with survival. The aim of this study was to evaluate th...
Artificial Intelligence-Enabled Assessment of the Heart Rate Corrected QT Interval Using a Mobile Electrocardiogram Device.
Circulation
Feb 1, 2021
BACKGROUND: Heart rate-corrected QT interval (QTc) prolongation, whether secondary to drugs, genetics including congenital long QT syndrome, and/or systemic diseases including SARS-CoV-2-mediated coronavirus disease 2019 (COVID-19), can predispose to...
Diagnostic accuracy of stress-only myocardial perfusion SPECT improved by deep learning.
European journal of nuclear medicine and molecular imaging
Jan 29, 2021
PURPOSE: Deep convolutional neural networks (CNN) for single photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) has been used to improve the diagnostic accuracy of coronary artery disease (CAD). This study was to design an...
Further evaluation and validation of the VETSCAN IMAGYST: in-clinic feline and canine fecal parasite detection system integrated with a deep learning algorithm.
Parasites & vectors
Jan 29, 2021
BACKGROUND: Fecal examinations in pet cats and dogs are key components of routine veterinary practice; however, their accuracy is influenced by diagnostic methodologies and the experience level of personnel performing the tests. The VETSCAN IMAGYST s...
An Artificial Neural Networks Model for Early Predicting In-Hospital Mortality in Acute Pancreatitis in MIMIC-III.
BioMed research international
Jan 28, 2021
BACKGROUND: Early and accurate evaluation of severity and prognosis in acute pancreatitis (AP), especially at the time of admission is very significant. This study was aimed to develop an artificial neural networks (ANN) model for early prediction of...
Application of deep learning algorithm to detect and visualize vertebral fractures on plain frontal radiographs.
PloS one
Jan 28, 2021
BACKGROUND: Identification of vertebral fractures (VFs) is critical for effective secondary fracture prevention owing to their association with the increasing risks of future fractures. Plain abdominal frontal radiographs (PARs) are a common investig...
Screening of Alzheimer's disease by facial complexion using artificial intelligence.
Aging
Jan 25, 2021
Despite the increasing incidence and high morbidity associated with dementia, a simple, non-invasive, and inexpensive method of screening for dementia is yet to be discovered. This study aimed to examine whether artificial intelligence (AI) could dis...
Batch Similarity Based Triplet Loss Assembled into Light-Weighted Convolutional Neural Networks for Medical Image Classification.
Sensors (Basel, Switzerland)
Jan 24, 2021
In many medical image classification tasks, there is insufficient image data for deep convolutional neural networks (CNNs) to overcome the over-fitting problem. The light-weighted CNNs are easy to train but they usually have relatively poor classific...
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