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VGX: VGG19-Based Gradient Explainer Interpretable Architecture for Brain Tumor Detection in Microscopy Magnetic Resonance Imaging (MMRI).
Microsc Res Tech
January 2025
AIDA Lab. College of Computer and Information Sciences (CCIS), Prince Sultan University, Riyadh, Saudi Arabia.
The development of deep learning algorithms has transformed medical image analysis, especially in brain tumor recognition. This research introduces a robust automatic microbrain tumor identification method utilizing the VGG16 deep learning model. Microscopy magnetic resonance imaging (MMRI) scans extract detailed features, providing multi-modal insights.
View Article and Find Full Text PDFEnhancing prehospital decision-making: exploring user needs and design considerations for clinical decision support systems.
BMC Med Inform Decis Mak
January 2025
School of Medicine, University of Colorado, Aurora, CO, USA.
Background: In prehospital emergency care, providers face significant challenges in making informed decisions due to factors such as limited cognitive support, high-stress environments, and lack of experience with certain patient conditions. Effective Clinical Decision Support Systems (CDSS) have great potential to alleviate these challenges. However, such systems have not yet been widely adopted in real-world practice and have been found to cause workflow disruptions and usability issues.
View Article and Find Full Text PDFInCoLoTransNet: An Involution-Convolution and Locality Attention-Aware Transformer for Precise Colorectal Polyp Segmentation in GI Images.
J Imaging Inform Med
January 2025
Faculty of Medicine and Pharmacy of Rabat, Mohammed V University of Rabat, Rabat, 10000, Morocco.
Gastrointestinal (GI) disease examination presents significant challenges to doctors due to the intricate structure of the human digestive system. Colonoscopy and wireless capsule endoscopy are the most commonly used tools for GI examination. However, the large amount of data generated by these technologies requires the expertise and intervention of doctors for disease identification, making manual analysis a very time-consuming task.
View Article and Find Full Text PDFExplainable deep learning and virtual evolution identifies antimicrobial peptides with activity against multidrug-resistant human pathogens.
Nat Microbiol
January 2025
School of Environmental and Chemical Engineering, Shanghai University, Shanghai, China.
Artificial intelligence (AI) is a promising approach to identify new antimicrobial compounds in diverse microbial species. Here we developed an AI-based, explainable deep learning model, EvoGradient, that predicts the potency of antimicrobial peptides (AMPs) and virtually modifies peptide sequences to produce more potent AMPs, akin to in silico directed evolution. We applied this model to peptides encoded in low-abundance human oral bacteria, resulting in the virtual evolution of 32 peptides into potent AMPs.
View Article and Find Full Text PDFGastroHUN an Endoscopy Dataset of Complete Systematic Screening Protocol for the Stomach.
Sci Data
January 2025
Universidad Nacional de Colombia, Bogotá, 1100111, Colombia.
Endoscopy is vital for detecting and diagnosing gastrointestinal diseases. Systematic examination protocols are key to enhancing detection, particularly for the early identification of premalignant conditions. Publicly available endoscopy image databases are crucial for machine learning research, yet challenges persist, particularly in identifying upper gastrointestinal anatomical landmarks to ensure effective and precise endoscopic procedures.
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