AIMC Topic: Deep Learning

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DTC-m6Am: A Framework for Recognizing N6,2'-O-dimethyladenosine Sites in Unbalanced Classification Patterns Based on DenseNet and Attention Mechanisms.

Frontiers in bioscience (Landmark edition)
BACKGROUND: mAm is a specific RNA modification that plays an important role in regulating mRNA stability, translational efficiency, and cellular stress response. mAm's precise identification is essential to gain insight into its functional mechanisms...

Detection of precancerous lesions in cervical images of perimenopausal women using U-net deep learning.

African journal of reproductive health
Due to physiological changes during the perimenopausal period, the morphology of cervical cells undergoes certain alterations. Accurate cell image segmentation and lesion identification are of great significance for the early detection of precancerou...

Role of artificial intelligence in predicting disease-related malnutrition - A narrative review.

Nutricion hospitalaria
Background: disease-related malnutrition (DRM) affects 30-50 % of hospitalized patients and is often underdiagnosed, increasing risks of complications and healthcare costs. Traditional DRM detection has relied on manual methods that lack accuracy and...

WMH-DualTasker: A Weakly Supervised Deep Learning Model for Automated White Matter Hyperintensities Segmentation and Visual Rating Prediction.

Human brain mapping
White matter hyperintensities (WMH) are neuroimaging markers linked to an elevated risk of cognitive decline. WMH severity is typically assessed via visual rating scales and through volumetric segmentation. While visual rating scales are commonly use...

Rapid pathologic grading-based diagnosis of esophageal squamous cell carcinoma Raman spectroscopy and a deep learning algorithm.

World journal of gastroenterology
BACKGROUND: Esophageal squamous cell carcinoma is a major histological subtype of esophageal cancer. Many molecular genetic changes are associated with its occurrence. Raman spectroscopy has become a new method for the early diagnosis of tumors becau...

Analysis of RNA translation with a deep learning architecture provides new insight into translation control.

Nucleic acids research
Accurate annotation of coding regions in RNAs is essential for understanding gene translation. We developed a deep neural network to directly predict and analyze translation initiation and termination sites from RNA sequences. Trained with human tran...

The Relevance of General Intelligence Measurement in Deep Learning for Healthcare.

Studies in health technology and informatics
The integration of artificial intelligence (AI) into medical informatics presents significant opportunities to enhance healthcare through data-driven diagnostics, predictive analytics, and personalized therapeutic recommendations. This paper examines...

Tackling a textbook example of multistep enzyme catalysis with deep learning-driven design.

Molecular cell
Enzyme design has struggled to emulate the complexity and catalytic proficiency of natural enzymes. Lauko et al. show that with the help of deep learning, the design of serine hydrolases that rival nature's ingenuity is possible.

Digital pathology and artificial intelligence in diagnostic pathology.

The Malaysian journal of pathology
Currently, digital pathology is a profound transformation in the field of pathology. Numerous artificial intelligence (AI) algorithms have demonstrated significant potential for the improvement of diagnostic efficiency, morphometric analysis of bioma...

Deep Learning-based Quantitative CT Myocardial Perfusion Imaging and Risk Stratification of Coronary Artery Disease.

Radiology
Background Precise assessment of myocardial ischemia burden and cardiovascular risk stratification based on dynamic CT myocardial perfusion imaging (MPI) is lacking. Purpose To develop and validate a deep learning (DL) model for automated quantificat...