AIMC Topic: Deep Learning

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An effective COVID-19 classification in X-ray images using a new deep learning framework.

Journal of X-ray science and technology
BackgroundThe global concern regarding the diagnosis of lung-related diseases has intensified due to the rapid transmission of coronavirus disease 2019 (COVID-19). Artificial Intelligence (AI) based methods are emerging technologies that help to iden...

An in-depth review of AI-powered advancements in cancer drug discovery.

Biochimica et biophysica acta. Molecular basis of disease
The convergence of artificial intelligence (AI) and genomics is redefining cancer drug discovery by facilitating the development of personalized and effective therapies. This review examines the transformative role of AI technologies, including deep ...

Deep self-representation learning with hyper-laplacian regularization for brain imaging genetics association analysis.

Methods (San Diego, Calif.)
Brain imaging genetics aims to explore the association between genetic factors such as single nucleotide polymorphisms (SNPs) and brain imaging quantitative traits (QTs). However, most existing methods do not consider the nonlinear correlations betwe...

Deep learning in surgical process modeling: A systematic review of workflow recognition.

Journal of biomedical informatics
OBJECTIVE: The application of artificial intelligence (AI) in health care has led to a surge of interest in surgical process modeling (SPM). The objective of this study is to investigate the role of deep learning in recognizing surgical workflows and...

A Deep Learning Approach for Mental Fatigue State Assessment.

Sensors (Basel, Switzerland)
This study investigates mental fatigue in sports activities by leveraging deep learning techniques, deviating from the conventional use of heart rate variability (HRV) feature analysis found in previous research. The study utilizes a hybrid deep neur...

Role of Artificial Intelligence in the Detection and Management of Premalignant and Malignant Lesions of the Esophagus and Stomach.

Gastrointestinal endoscopy clinics of North America
The advent of artificial intelligence (AI) and deep learning algorithms, particularly convolutional neural networks, promises to address pitfalls, bridging the care for patients at high risk with improved detection (computer-aided detection [CADe]) a...

PIDGN: An explainable multimodal deep learning framework for early prediction of Parkinson's disease.

Journal of neuroscience methods
BACKGROUND: Parkinson's disease (PD), the second most common neurodegenerative disease in the world, is usually not diagnosed until the later stages of the disease, when patients might have already missed the best treatment period. Therefore, more ef...

Deep learning-based defect detection in film-coated tablets using a convolutional neural network.

International journal of pharmaceutics
Film-coating is a critical step in pharmaceutical manufacturing. Traditional visual inspections for film-coated tablet defect assessment are subjective, inefficient, and labor-intensive. We propose a novel approach utilizing machine learning and imag...

Advancements in Frank's sign Identification using deep learning on 3D brain MRI.

Scientific reports
Frank's sign (FS) is a diagnostic marker associated with aging and various health conditions. Despite its clinical significance, there lacks a standardized method for its identification. This study aimed to develop a deep learning model for automated...

Predicting metabolite response to dietary intervention using deep learning.

Nature communications
Due to highly personalized biological and lifestyle characteristics, different individuals may have different metabolite responses to specific foods and nutrients. In particular, the gut microbiota, a collection of trillions of microorganisms living ...