AIMC Topic: Deep Learning

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Emerging frontiers in virtual drug discovery: From quantum mechanical methods to deep learning approaches.

Current opinion in chemical biology
Virtual screening-based approaches to discover initial hit and lead compounds have the potential to reduce both the cost and time of early drug discovery stages, as well as to find inhibitors for even challenging target sites such as protein-protein ...

Deep learning-based framework for motion-compensated image fusion in catheterization procedures.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
OBJECTIVE: Augmenting X-ray (XR) fluoroscopy with 3D anatomic overlays is an essential technique to improve the guidance of the catheterization procedures. Unfortunately, cardiac and respiratory motion compromises the augmented fluoroscopy. Motion co...

Deep-learning-based seizure detection and prediction from electroencephalography signals.

International journal for numerical methods in biomedical engineering
Electroencephalography (EEG) is among the main tools used for analyzing and diagnosing epilepsy. The manual analysis of EEG must be conducted by highly trained clinicians or neuro-physiologists; a process that is considered to have a comparatively lo...

A Deep Learning Approach to Estimate the Incidence of Infectious Disease Cases for Routinely Collected Ambulatory Records: The Example of Varicella-Zoster.

International journal of environmental research and public health
The burden of infectious diseases is crucial for both epidemiological surveillance and prompt public health response. A variety of data, including textual sources, can be fruitfully exploited. Dealing with unstructured data necessitates the use of me...

Development and validation of an ensemble artificial intelligence model for comprehensive imaging quality check to classify body parts and contrast enhancement.

BMC medical imaging
BACKGROUND: Despite the dramatic increase in the use of medical imaging in various therapeutic fields of clinical trials, the first step of image quality check (image QC), which aims to check whether images are uploaded appropriately according to the...

A geometry-guided multi-beamlet deep learning technique for CT reconstruction.

Biomedical physics & engineering express
. Previous studies have proposed deep-learning techniques to reconstruct CT images from sinograms. However, these techniques employ large fully-connected (FC) layers for projection-to-image domain transformation, producing large models requiring subs...

A Multicriteria English Teaching Decision Model Based on Deep Learning.

Computational intelligence and neuroscience
In any stage of the teaching process, the teaching behavior depends on the decision of teachers. For English teachers, the English teaching decision level directly determines the course quality and the mining of learner potential. The existing studie...

A Stress Test of Artificial Intelligence: Can Deep Learning Models Trained From Formal Echocardiography Accurately Interpret Point-of-Care Ultrasound?

Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine
OBJECTIVES: To test if a deep learning (DL) model trained on echocardiography images could accurately segment the left ventricle (LV) and predict ejection fraction on apical 4-chamber images acquired by point-of-care ultrasound (POCUS).

Deep learning: Opening a third eye to myocardial perfusion imaging.

Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology

External validation of a deep-learning model to predict severe acute kidney injury based on urine output changes in critically ill patients.

Journal of nephrology
OBJECTIVES: The purpose of this study was to externally validate algorithms (previously developed and trained in two United States populations) aimed at early detection of severe oliguric AKI (stage 2/3 KDIGO) in intensive care units patients.