AIMC Topic: Deep Learning

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Prognostic impact of deep learning-based quantification in clinical stage 0-I lung adenocarcinoma.

European radiology
OBJECTIVES: To evaluate the performance of automatic deep learning (DL) algorithm for size, mass, and volume measurements in predicting prognosis of lung adenocarcinoma (LUAD) and compared with manual measurements.

Multi-institutional PET/CT image segmentation using federated deep transformer learning.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Generalizable and trustworthy deep learning models for PET/CT image segmentation necessitates large diverse multi-institutional datasets. However, legal, ethical, and patient privacy issues challenge sharing of datasets betw...

A deep learning-based drug repurposing screening and validation for anti-SARS-CoV-2 compounds by targeting the cell entry mechanism.

Biochemical and biophysical research communications
The recent outbreak of Corona Virus Disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been a severe threat to the global public health and economy, however, effective drugs to treat COVID-19 are still ...

Pectoralis muscle predicts distant metastases in breast cancer by deep learning radiomics.

Acta radiologica (Stockholm, Sweden : 1987)
BACKGROUND: Sarcopenia is associated with a poor prognosis in patients with breast cancer (BC). Currently, there are few quantitative assessments carried out between muscle biomarkers and distant metastasis using existing methods.

CRISPR-Cas-Based Biomonitoring for Marine Environments: Toward CRISPR RNA Design Optimization Via Deep Learning.

The CRISPR journal
Almost all of Earth's oceans are now impacted by multiple anthropogenic stressors, including the spread of nonindigenous species, harmful algal blooms, and pathogens. Early detection is critical to manage these stressors effectively and to protect ma...

Deep learning image analysis models pretrained on daily objects are useful for the preliminary characterization of particulate pharmaceutical samples.

Biotechnology and bioengineering
Visible and subvisible particles are a quality attribute in sterile pharmaceutical samples. A common method for characterizing and quantifying pharmaceutical samples containing particulates is imaging many individual particles using high-throughput i...

PhysVENeT: a physiologically-informed deep learning-based framework for the synthesis of 3D hyperpolarized gas MRI ventilation.

Scientific reports
Functional lung imaging modalities such as hyperpolarized gas MRI ventilation enable visualization and quantification of regional lung ventilation; however, these techniques require specialized equipment and exogenous contrast, limiting clinical adop...

Turnover number predictions for kinetically uncharacterized enzymes using machine and deep learning.

Nature communications
The turnover number k, a measure of enzyme efficiency, is central to understanding cellular physiology and resource allocation. As experimental k estimates are unavailable for the vast majority of enzymatic reactions, the development of accurate comp...

Multi-batch single-cell comparative atlas construction by deep learning disentanglement.

Nature communications
Cell state atlases constructed through single-cell RNA-seq and ATAC-seq analysis are powerful tools for analyzing the effects of genetic and drug treatment-induced perturbations on complex cell systems. Comparative analysis of such atlases can yield ...

Deep Learning for Discrimination of Hypertrophic Cardiomyopathy and Hypertensive Heart Disease on MRI Native T1 Maps.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Native T1 and radiomics were used for hypertrophic cardiomyopathy (HCM) and hypertensive heart disease (HHD) differentiation previously. The current problem is that global native T1 remains modest discrimination performance and radiomics ...