AIMC Topic: Deep Learning

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Lightweight deep learning model for embedded systems efficiently predicts oil and protein content in rapeseed.

Food chemistry
Conventional methods for determining protein and oil content in rapeseed are often time-consuming, labor-intensive, and costly. In this study, a mobile application was developed using an optimized deep learning method for low-cost, non-destructive an...

A deep learning model could screen for coronary heart disease from a "pseudo-normal" electrocardiogram.

Medicine
BACKGROUND: This study aimed to develop a deep learning model (DLM) for rapid screening of coronary heart disease (CHD) using "pseudo-normal" electrocardiograms (ECGs), particularly focusing on patients who present with normal or near-normal ECGs at ...

Deployment of an Artificial Intelligence Histology Tool to Aid Qualitative Assessment of Histopathology Using the Nancy Histopathology Index in Ulcerative Colitis.

Inflammatory bowel diseases
BACKGROUND: Ulcerative colitis (UC) is a chronic inflammatory bowel disease characterized by increased stool frequency, rectal bleeding, and urgency. To streamline the quantitative assessment of histopathology using the Nancy Index in UC patients, we...

Knowledge Graph-Enhanced Deep Learning Model (H-SYSTEM) for Hypertensive Intracerebral Hemorrhage: Model Development and Validation.

Journal of medical Internet research
BACKGROUND: Although much progress has been made in artificial intelligence (AI), several challenges remain substantial obstacles to the development and translation of AI systems into clinical practice. Even large language models, which show excellen...

Cyclic Peptide Therapeutic Agents Discovery: Computational and Artificial Intelligence-Driven Strategies.

Journal of medicinal chemistry
Cyclic peptides have emerged as promising modulators of protein-protein interactions due to their unique pharmacological properties and ability to target extensive flat binding interfaces. However, traditional strategies for developing cyclic peptide...

Interpretable Multimodal Deep Ensemble Framework Dissecting Bloodbrain Barrier Permeability with Molecular Features.

The journal of physical chemistry letters
Blood-brain barrier permeability (BBBP) prediction plays a critical role in the drug discovery process, particularly for compounds targeting the central nervous system. While machine learning (ML) has significantly advanced the prediction of BBBP, th...

Real-time integrated modeling of soft tissue deformation and stress based on deep learning.

Physics in medicine and biology
. Accurately and in real-time simulating soft tissue deformation and visualizing stress distribution are crucial for advancing surgical simulators closer to real surgical environments. The concept of using neural networks to accelerate the finite ele...

Integrating Deep Learning Derived Morphological Traits and Molecular Data for Total-Evidence Phylogenetics: Lessons from Digitized Collections.

Systematic biology
Deep learning has previously shown success in automatically generating morphological traits that carry a phylogenetic signal. In this paper, we explore combining molecular data with deep learning derived morphological traits from images of pinned ins...

A Deep Learning Model for Identifying the Risk of Mesenteric Malperfusion in Acute Aortic Dissection Using Initial Diagnostic Data: Algorithm Development and Validation.

Journal of medical Internet research
BACKGROUND: Mesenteric malperfusion (MMP) is an uncommon but devastating complication of acute aortic dissection (AAD) that combines 2 life-threatening conditions-aortic dissection and acute mesenteric ischemia. The complex pathophysiology of MMP pos...