AIMC Topic: Sensitivity and Specificity

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Weakly Supervised Deep Learning Approach to Breast MRI Assessment.

Academic radiology Jun 6, 2021
RATIONALE AND OBJECTIVES: To evaluate a weakly supervised deep learning approach to breast Magnetic Resonance Imaging (MRI) assessment without pixel level segmentation in order to improve the specificity of breast MRI lesion classification.
Breast Humans Female Deep Learning Magnetic Resonance Imaging Sensitivity and Specificity Breast Neoplasms
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Artificial Neural Network-Based Deep Learning Model for COVID-19 Patient Detection Using X-Ray Chest Images.

Journal of healthcare engineering Jun 5, 2021
The world is experiencing an unprecedented crisis due to the coronavirus disease (COVID-19) outbreak that has affected nearly 216 countries and territories across the globe. Since the pandemic outbreak, there is a growing interest in computational mo...
Sensitivity and Specificity Humans Radiographic Image Interpretation, Computer-Assisted Lung COVID-19 Radiography, Thoracic SARS-CoV-2 Deep Learning
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AI-based improvement in lung cancer detection on chest radiographs: results of a multi-reader study in NLST dataset.

European radiology Jun 4, 2021
OBJECTIVE: Assess if deep learning-based artificial intelligence (AI) algorithm improves reader performance for lung cancer detection on chest X-rays (CXRs).
Humans Artificial Intelligence Algorithms Radiography Radiographic Image Interpretation, Computer-Assisted Radiography, Thoracic Sensitivity and Specificity Lung Lung Neoplasms
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Natural Language Processing to Identify Pulmonary Nodules and Extract Nodule Characteristics From Radiology Reports.

Chest Jun 4, 2021
BACKGROUND: There is an urgent need for population-based studies on managing patients with pulmonary nodules.
Dimensional Measurement Accuracy Quality Improvement Documentation Radiography, Thoracic Lung Neoplasms Multiple Pulmonary Nodules Solitary Pulmonary Nodule Tomography, X-Ray Computed Radiology Tumor Burden Sensitivity and Specificity Humans Calcinosis Lung Algorithms Natural Language Processing
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Detecting pelvic fracture on 3D-CT using deep convolutional neural networks with multi-orientated slab images.

Scientific reports Jun 3, 2021
Pelvic fracture is one of the leading causes of death in the elderly, carrying a high risk of death within 1 year of fracture. This study proposes an automated method to detect pelvic fractures on 3-dimensional computed tomography (3D-CT). Deep convo...
Humans Fractures, Bone Image Processing, Computer-Assisted Models, Theoretical Pelvic Bones Young Adult Reproducibility of Results Neural Networks, Computer Deep Learning Adult Aged Male Tomography, X-Ray Computed Imaging, Three-Dimensional Sensitivity and Specificity Aged, 80 and over Middle Aged Female
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A deep transfer learning framework for the automated assessment of corneal inflammation on in vivo confocal microscopy images.

PloS one Jun 3, 2021
PURPOSE: Infiltration of activated dendritic cells and inflammatory cells in cornea represents an important marker for defining corneal inflammation. Deep transfer learning has presented a promising potential and is gaining more importance in compute...
Area Under Curve Dry Eye Syndromes Ophthalmologists Pterygium ROC Curve Microscopy, Confocal Models, Theoretical Humans Diagnosis, Computer-Assisted Sensitivity and Specificity Cornea Deep Learning Keratitis Dendritic Cells
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Deep learning-based classification of blue light cystoscopy imaging during transurethral resection of bladder tumors.

Scientific reports Jun 2, 2021
Bladder cancer is one of the top 10 frequently occurring cancers and leads to most cancer deaths worldwide. Recently, blue light (BL) cystoscopy-based photodynamic diagnosis was introduced as a unique technology to enhance the detection of bladder ca...
Humans Sensitivity and Specificity Deep Learning Light Image Interpretation, Computer-Assisted Cystoscopy Urethra Urinary Bladder Neoplasms Neoplasm Invasiveness Neoplasm Grading
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Automated detection of Mycobacterium tuberculosis using transfer learning.

Journal of infection in developing countries May 31, 2021
INTRODUCTION: Quantitative analysis of Mycobacterium tuberculosis using microscope is very critical for diagnosing tuberculosis diseases. Microbiologist encounter several challenges which can lead to misdiagnosis. However, there are 3 main challenges...
Machine Learning Sensitivity and Specificity Humans Tuberculosis, Pulmonary Mycobacterium tuberculosis Diagnostic Tests, Routine
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Biased accuracy in multisite machine-learning studies due to incomplete removal of the effects of the site.

Psychiatry research. Neuroimaging May 29, 2021
Brain MRI researchers conducting multisite studies, such as within the ENIGMA Consortium, are very aware of the importance of controlling the effects of the site (EoS) in the statistical analysis. Conversely, authors of the novel machine-learning MRI...
Neuroimaging Humans Machine Learning Sensitivity and Specificity Magnetic Resonance Imaging
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LSTMCNNsucc: A Bidirectional LSTM and CNN-Based Deep Learning Method for Predicting Lysine Succinylation Sites.

BioMed research international May 28, 2021
Lysine succinylation is a typical protein post-translational modification and plays a crucial role of regulation in the cellular process. Identifying succinylation sites is fundamental to explore its functions. Although many computational methods wer...
Escherichia coli Humans Protein Processing, Post-Translational Area Under Curve Neural Networks, Computer Reproducibility of Results Animals Proteins Internet Algorithms Deep Learning Sensitivity and Specificity Computational Biology Succinic Acid
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