AIMC Topic: Deep Learning

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Unsupervised deep learning model for correcting Nyquist ghosts of single-shot spatiotemporal encoding.

Magnetic resonance in medicine
PURPOSE: To design an unsupervised deep learning (DL) model for correcting Nyquist ghosts of single-shot spatiotemporal encoding (SPEN) and evaluate the model for real MRI applications.

Deep learning-based natural language processing for detecting medical symptoms and histories in emergency patient triage.

The American journal of emergency medicine
OBJECTIVE: The manual recording of electronic health records (EHRs) by clinicians in the emergency department (ED) is time-consuming and challenging. In light of recent advancements in large language models (LLMs) such as GPT and BERT, this study aim...

Deep learning-based dynamic ventilatory threshold estimation from electrocardiograms.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: The ventilatory threshold (VT) marks the transition from aerobic to anaerobic metabolism and is used to assess cardiorespiratory endurance. A conventional way to assess VT is cardiopulmonary exercise testing, which requires ...

Enhancing interpretability and generalizability of deep learning-based emulator in three-dimensional lake hydrodynamics using Koopman operator and transfer learning: Demonstrated on the example of lake Zurich.

Water research
Three-dimensional lake hydrodynamic model is a powerful tool widely used to assess hydrological condition changes of lake. However, its computational cost becomes problematic when forecasting the state of large lakes or using high-resolution simulati...

A deep learning model for Alzheimer's disease diagnosis based on patient clinical records.

Computers in biology and medicine
BACKGROUND: Dementia, with Alzheimer's disease (AD) being the most common type of this neurodegenerative disease, is an under-diagnosed health problem in older people. The creation of classification models based on AD risk factors using Deep Learning...

Human Walking Direction Detection Using Wireless Signals, Machine and Deep Learning Algorithms.

Sensors (Basel, Switzerland)
The use of wireless signals for device-free activity recognition and precise indoor positioning has gained significant popularity recently. By taking advantage of the characteristics of the received signals, it is possible to establish a mapping betw...

Generalizable synthetic MRI with physics-informed convolutional networks.

Medical physics
BACKGROUND: Magnetic resonance imaging (MRI) provides state-of-the-art image quality for neuroimaging, consisting of multiple separately acquired contrasts. Synthetic MRI aims to accelerate examinations by synthesizing any desirable contrast from a s...

Integrating QSAR modelling and deep learning in drug discovery: the emergence of deep QSAR.

Nature reviews. Drug discovery
Quantitative structure-activity relationship (QSAR) modelling, an approach that was introduced 60 years ago, is widely used in computer-aided drug design. In recent years, progress in artificial intelligence techniques, such as deep learning, the rap...

Fusion of laser-induced breakdown spectroscopy technology and deep learning: a new method to identify malignant and benign lung tumors with high accuracy.

Analytical and bioanalytical chemistry
Precisely distinguishing between malignant and benign lung tumors is pivotal for suggesting therapeutic strategies and enhancing prognosis, yet this differentiation remains a daunting task. The growth rates, metastatic potentials, and prognoses of be...

Deep-Interior: A new pathway to interior tomographic image reconstruction via a weighted backprojection and deep learning.

Medical physics
BACKGROUND: In recent years, deep learning strategies have been combined with either the filtered backprojection or iterative methods or the direct projection-to-image by deep learning only to reconstruct images. Some of these methods can be applied ...