AIMC Topic: Reproducibility of Results

Clear Filters Showing 1651 to 1660 of 5908 articles

Automatic deep learning-based assessment of spinopelvic coronal and sagittal alignment.

Diagnostic and interventional imaging
PURPOSE: The purpose of this study was to evaluate an artificial intelligence (AI) solution for estimating coronal and sagittal spinopelvic alignment on conventional uniplanar two-dimensional whole-spine radiograph.

Automated assembly of molecular mechanisms at scale from text mining and curated databases.

Molecular systems biology
The analysis of omic data depends on machine-readable information about protein interactions, modifications, and activities as found in protein interaction networks, databases of post-translational modifications, and curated models of gene and protei...

Deep learning pose estimation for multi-cattle lameness detection.

Scientific reports
The objective of this study was to develop a fully automated multiple-cow real-time lameness detection system using a deep learning approach for cattle detection and pose estimation that could be deployed across dairy farms. Utilising computer vision...

Deep Learning for Improved Precision and Reproducibility of Left Ventricular Strain in Echocardiography: A Test-Retest Study.

Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography
AIMS: Assessment of left ventricular (LV) function by echocardiography is hampered by modest test-retest reproducibility. A novel artificial intelligence (AI) method based on deep learning provides fully automated measurements of LV global longitudin...

Convolutional Neural Network Quantification of Gleason Pattern 4 and Association With Biochemical Recurrence in Intermediate-Grade Prostate Tumors.

Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc
Differential classification of prostate cancer grade group (GG) 2 and 3 tumors remains challenging, likely because of the subjective quantification of the percentage of Gleason pattern 4 (%GP4). Artificial intelligence assessment of %GP4 may improve ...

Waste valorization as low-cost media engineering for auxin production from the newly isolated Streptomyces rubrogriseus AW22: Model development.

Chemosphere
Indole-3-acetic acid (IAA) represents a crucial phytohormone regulating specific tropic responses in plants and functions as a chemical signal between plant hosts and their symbionts. The Actinobacteria strain of AW22 with high IAA production ability...

Evaluation of boundaries between mood and psychosis disorder using dynamic functional network connectivity (dFNC) via deep learning classification.

Human brain mapping
The validity and reliability of diagnoses in psychiatry is a challenging topic in mental health. The current mental health categorization is based primarily on symptoms and clinical course and is not biologically validated. Among multiple ongoing eff...

Technical Advancements in Abdominal Diffusion-weighted Imaging.

Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine
Since its first observation in the 18th century, the diffusion phenomenon has been actively studied by many researchers. Diffusion-weighted imaging (DWI) is a technique to probe the diffusion of water molecules and create a MR image with contrast bas...

MedViT: A robust vision transformer for generalized medical image classification.

Computers in biology and medicine
Convolutional Neural Networks (CNNs) have advanced existing medical systems for automatic disease diagnosis. However, there are still concerns about the reliability of deep medical diagnosis systems against the potential threats of adversarial attack...

Deep learning intravoxel incoherent motion modeling: Exploring the impact of training features and learning strategies.

Magnetic resonance in medicine
PURPOSE: The development of advanced estimators for intravoxel incoherent motion (IVIM) modeling is often motivated by a desire to produce smoother parameter maps than least squares (LSQ). Deep neural networks show promise to this end, yet performanc...