AIMC Topic: Sensitivity and Specificity

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Olfactory Testing in Parkinson Disease and REM Behavior Disorder: A Machine Learning Approach.

Neurology
OBJECTIVE: We sought to identify an abbreviated test of impaired olfaction amenable for use in busy clinical environments in prodromal (isolated REM sleep behavior disorder [iRBD]) and manifest Parkinson disease (PD).

Machining learning predicts the need for escalated care and mortality in COVID-19 patients from clinical variables.

International journal of medical sciences
This study aimed to develop a machine learning algorithm to identify key clinical measures to triage patients more effectively to general admission versus intensive care unit (ICU) admission and to predict mortality in COVID-19 pandemic. This retro...

Sensitivity analysis for interpretation of machine learning based segmentation models in cardiac MRI.

BMC medical imaging
BACKGROUND: Image segmentation is a common task in medical imaging e.g., for volumetry analysis in cardiac MRI. Artificial neural networks are used to automate this task with performance similar to manual operators. However, this performance is only ...

Deep learning detection of subtle fractures using staged algorithms to mimic radiologist search pattern.

Skeletal radiology
OBJECTIVE: To develop and evaluate a two-stage deep convolutional neural network system that mimics a radiologist's search pattern for detecting two small fractures: triquetral avulsion fractures and Segond fractures.

Technical Note: An embedding-based medical note de-identification approach with sparse annotation.

Medical physics
PURPOSE: Medical note de-identification is critical for the protection of private information and the security of data sharing in collaborative research. The task demands the complete removal of all patient names and other sensitive information such ...

Discrimination of malignant from benign thyroid lesions through neural networks using FTIR signals obtained from tissues.

Analytical and bioanalytical chemistry
The current gold standard in cancer diagnosis-the microscopic examination of hematoxylin and eosin (H&E)-stained biopsies-is prone to bias since it greatly relies on visual examination. Hence, there is a need to develop a more sensitive and specific ...

Ensemble machine learning prediction and variable importance analysis of 5-year mortality after cardiac valve and CABG operations.

Scientific reports
Despite having a similar post-operative complication profile, cardiac valve operations are associated with a higher mortality rate compared to coronary artery bypass grafting (CABG) operations. For long-term mortality, few predictors are known. In th...

Machine learning for image-based detection of patients with obstructive sleep apnea: an exploratory study.

Sleep & breathing = Schlaf & Atmung
PURPOSE: In 2-dimensional lateral cephalometric radiographs, patients with severe obstructive sleep apnea (OSA) exhibit a more crowded oropharynx in comparison with non-OSA. We tested the hypothesis that machine learning, an application of artificial...

A deep-learning semantic segmentation approach to fully automated MRI-based left-ventricular deformation analysis in cardiotoxicity.

Magnetic resonance imaging
Left-ventricular (LV) strain measurements with the Displacement Encoding with Stimulated Echoes (DENSE) MRI sequence provide accurate estimates of cardiotoxicity damage related to breast cancer chemotherapy. This study investigated an automated LV ch...