AIMC Topic: Deep Learning
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Rapid detection and quantitative analysis of thiram in fruits using a shape-adaptable flexible SERS substrate combined with deep learning.
Analytical methods : advancing methods and applications
Feb 20, 2025
Ensuring food safety necessitates rapid identification of pesticide residues on fruits. Herein, we developed a shape-adaptable flexible surface-enhanced Raman scattering (SERS) substrate, combined with a deep learning algorithm, to quickly detect and...
Radiomics and Deep Learning Prediction of Immunotherapy-Induced Pneumonitis From Computed Tomography.
JCO clinical cancer informatics
Feb 20, 2025
PURPOSE: Primary barriers to application of immune checkpoint inhibitor (ICI) therapy for cancer include severe side effects (such as potentially life threatening pneumonitis [PN]), which can cause the discontinuation of treatment. Predicting which p...
Comparison of different dental age estimation methods with deep learning: Willems, Cameriere-European, London Atlas.
International journal of legal medicine
Feb 19, 2025
This study aimed to compare dental age estimates using Willems, Cameriere-Europe, London Atlas, and deep learning methods on panoramic radiographs of Turkish children. The dental ages of 1169 children (613 girls, 556 boys) who agreed to participate i...
Robust and generalizable artificial intelligence for multi-organ segmentation in ultra-low-dose total-body PET imaging: a multi-center and cross-tracer study.
European journal of nuclear medicine and molecular imaging
Feb 19, 2025
PURPOSE: Positron Emission Tomography (PET) is a powerful molecular imaging tool that visualizes radiotracer distribution to reveal physiological processes. Recent advances in total-body PET have enabled low-dose, CT-free imaging; however, accurate o...
Prediction of adverse pathology in prostate cancer using a multimodal deep learning approach based on [F]PSMA-1007 PET/CT and multiparametric MRI.
European journal of nuclear medicine and molecular imaging
Feb 19, 2025
PURPOSE: Accurate prediction of adverse pathology (AP) in prostate cancer (PCa) patients is crucial for formulating effective treatment strategies. This study aims to develop and evaluate a multimodal deep learning model based on [F]PSMA-1007 PET/CT ...
Advertising or adversarial? AdvSign: Artistic advertising sign camouflage for target physical attacking to object detector.
Neural networks : the official journal of the International Neural Network Society
Feb 19, 2025
Deep learning models are often vulnerable to adversarial attacks in both digital and physical environments. Particularly challenging are physical attacks that involve subtle, unobtrusive modifications to objects, such as patch-sticking or light-shoot...
Learning from leading indicators to predict long-term dynamics of hourly electricity generation from multiple resources.
Neural networks : the official journal of the International Neural Network Society
Feb 19, 2025
Electricity is generated through various resources and then flows between regions via a complex system (grid). Imbalances in electricity generation can lead to the waste of renewable energy. As renewable energy is becoming a larger part of the grid, ...
Electrocardiographic-Driven artificial intelligence Model: A new approach to predicting One-Year mortality in heart failure with reduced ejection fraction patients.
International journal of medical informatics
Feb 19, 2025
BACKGROUND: Despite the proliferation of heart failure (HF) mortality prediction models, their practical utility is limited. Addressing this, we utilized a significant dataset to develop and validate a deep learning artificial intelligence (AI) model...
Deep learning-driven semi-rational design in phenylalanine ammonia-lyase for enhanced catalytic efficiency.
International journal of biological macromolecules
Feb 19, 2025
Phenylalanine ammonia-lyase (PAL) possesses significant potential in agriculture, industry, and the treatment of various diseases, including cancer. In particular, PAL derived from Anabaena variabilis (AvPAL) has been successfully utilized in clinica...
Detection of Ocular Surface Squamous Neoplasia Using Artificial Intelligence With Anterior Segment Optical Coherence Tomography.
American journal of ophthalmology
Feb 19, 2025
PURPOSE: To develop and validate a deep learning (DL) model to differentiate ocular surface squamous neoplasia (OSSN) from pterygium and pinguecula using high-resolution anterior segment optical coherence tomography (AS-OCT).
Female
Carcinoma, Squamous Cell
Reproducibility of Results
Pterygium
Adult
Conjunctival Neoplasms
Sensitivity and Specificity
Diagnosis, Differential
Aged
Male
Humans
Tomography, Optical Coherence
Anterior Eye Segment
Aged, 80 and over
Artificial Intelligence
Middle Aged
Corneal Diseases
Deep Learning
Retrospective Studies
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