AI Medical Compendium Topic

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Automation

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Machine learning-based automated classification of headache disorders using patient-reported questionnaires.

Scientific reports
Classification of headache disorders is dependent on a subjective self-report from patients and its interpretation by physicians. We aimed to apply objective data-driven machine learning approaches to analyze patient-reported symptoms and test the fe...

Automated quantification of myocardial tissue characteristics from native T mapping using neural networks with uncertainty-based quality-control.

Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance
BACKGROUND: Tissue characterisation with cardiovascular magnetic resonance (CMR) parametric mapping has the potential to detect and quantify both focal and diffuse alterations in myocardial structure not assessable by late gadolinium enhancement. Nat...

Complementary Deep and Shallow Learning with Boosting for Public Transportation Safety.

Sensors (Basel, Switzerland)
To monitor road safety, billions of records can be generated by Controller Area Network bus each day on public transportation. Automation to determine whether certain driving behaviour of drivers on public transportation can be considered safe on the...

Multi-to-binary network (MTBNet) for automated multi-organ segmentation on multi-sequence abdominal MRI images.

Physics in medicine and biology
Fully convolutional neural network (FCN) has achieved great success in semantic segmentation. However, the performance of the FCN is generally compromised for multi-object segmentation. Multi-organ segmentation is very common while challenging in the...

BMIVPOT, a Fully Automated Version of the Intravenous Pole: Simulation, Design, and Evaluation.

Journal of healthcare engineering
Robotic intravenous poles are automated supportive instrument that needs to be triggered by patients to hold medications and needed supplies. Healthcare engineering of robotic intravenous poles is advancing in order to improve the quality of health s...

Technical Note: Deep Learning approach for automatic detection and identification of patient positioning devices for radiation therapy.

Medical physics
PURPOSE: Automatic detection and identification of setup devices, using a deep convolutional neural network (CNN) for real-time multiclass object detection, has the potential to reduce errors in the treatment delivery process by avoiding documentatio...

Accuracy of Trained Physicians is Inferior to Deep Learning-Based Algorithm for Determining Angles in Ultrasound of the Newborn Hip.

Ultraschall in der Medizin (Stuttgart, Germany : 1980)
PURPOSE:  Sonographic diagnosis of developmental dysplasia of the hip allows treatment with a flexion-abduction orthosis preventing hip luxation. Accurate determination of alpha and beta angles according to Graf is crucial for correct diagnosis. It i...

A fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis.

The European respiratory journal
Coronavirus disease 2019 (COVID-19) has spread globally, and medical resources become insufficient in many regions. Fast diagnosis of COVID-19 and finding high-risk patients with worse prognosis for early prevention and medical resource optimisation ...

A high throughput machine-learning driven analysis of Ca spatio-temporal maps.

Cell calcium
High-resolution Ca imaging to study cellular Ca behaviors has led to the creation of large datasets with a profound need for standardized and accurate analysis. To analyze these datasets, spatio-temporal maps (STMaps) that allow for 2D visualization ...

Automatic characterization of myocardial perfusion imaging polar maps employing deep learning and data augmentation.

Hellenic journal of nuclear medicine
OBJECTIVE: To investigate a deep learning technique, more specifically state-of-the-art convolutional neural networks (CNN), for automatic characterization of polar maps derived from myocardial perfusion imaging (MPI) studies for the diagnosis of cor...