AIMC Topic: Deep Learning

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Quantifying the calcification of abdominal aorta and major side branches with deep learning.

Clinical radiology
AIM: To explore the possibility of a neural network-based method for quantifying calcifications of the abdominal aorta and its branches.

Towards a diagnostic tool for neurological gait disorders in childhood combining 3D gait kinematics and deep learning.

Computers in biology and medicine
Gait abnormalities are frequent in children and can be caused by different pathologies, such as cerebral palsy, neuromuscular disease, toe walker syndrome, etc. Analysis of the "gait pattern" (i.e., the way the person walks) using 3D analysis provide...

Deep learning-based algorithms for low-dose CT imaging: A review.

European journal of radiology
The computed tomography (CT) technique is extensively employed as an imaging modality in clinical settings. The radiation dose of CT, however, is significantly high, thereby raising concerns regarding the potential radiation damage it may cause. The ...

Monitoring carbon emissions using deep learning and statistical process control: a strategy for impact assessment of governments' carbon reduction policies.

Environmental monitoring and assessment
Across the globe, governments are developing policies and strategies to reduce carbon emissions to address climate change. Monitoring the impact of governments' carbon reduction policies can significantly enhance our ability to combat climate change ...

A deep learning-based theoretical protocol to identify potentially isoform-selective PI3Kα inhibitors.

Molecular diversity
Phosphoinositide 3-kinase alpha (PI3Kα) is one of the most frequently dysregulated kinases known for their pivotal role in many oncogenic diseases. While the side effects linked to existing drugs against PI3Kα-induced cancers provide an avenue for fu...

CIRI-Deep Enables Single-Cell and Spatial Transcriptomic Analysis of Circular RNAs with Deep Learning.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Circular RNAs (circRNAs) are a crucial yet relatively unexplored class of transcripts known for their tissue- and cell-type-specific expression patterns. Despite the advances in single-cell and spatial transcriptomics, these technologies face difficu...

Deep learning and atlas-based models to streamline the segmentation workflow of total marrow and lymphoid irradiation.

La Radiologia medica
PURPOSE: To improve the workflow of total marrow and lymphoid irradiation (TMLI) by enhancing the delineation of organs at risk (OARs) and clinical target volume (CTV) using deep learning (DL) and atlas-based (AB) segmentation models.

Utilizing 3D Point Cloud Technology with Deep Learning for Automated Measurement and Analysis of Dairy Cows.

Sensors (Basel, Switzerland)
This paper introduces an approach to the automated measurement and analysis of dairy cows using 3D point cloud technology. The integration of advanced sensing techniques enables the collection of non-intrusive, precise data, facilitating comprehensiv...

Validating the accuracy of deep learning for the diagnosis of pneumonia on chest x-ray against a robust multimodal reference diagnosis: a post hoc analysis of two prospective studies.

European radiology experimental
BACKGROUND: Artificial intelligence (AI) seems promising in diagnosing pneumonia on chest x-rays (CXR), but deep learning (DL) algorithms have primarily been compared with radiologists, whose diagnosis can be not completely accurate. Therefore, we ev...

Deep learning in water protection of resources, environment, and ecology: achievement and challenges.

Environmental science and pollution research international
The breathtaking economic development put a heavy toll on ecology, especially on water pollution. Efficient water resource management has a long-term influence on the sustainable development of the economy and society. Economic development and ecolog...