AIMC Topic: Reproducibility of Results

Clear Filters Showing 1631 to 1640 of 5908 articles

Evaluation of an artificial intelligence-based algorithm for automated localization of craniofacial landmarks.

Clinical oral investigations
OBJECTIVES: Due to advancing digitalisation, it is of interest to develop standardised and reproducible fully automated analysis methods of cranial structures in order to reduce the workload in diagnosis and treatment planning and to generate objecti...

Pushing the limits of low-cost ultra-low-field MRI by dual-acquisition deep learning 3D superresolution.

Magnetic resonance in medicine
PURPOSE: Recent development of ultra-low-field (ULF) MRI presents opportunities for low-power, shielding-free, and portable clinical applications at a fraction of the cost. However, its performance remains limited by poor image quality. Here, a compu...

Serverless Prediction of Peptide Properties with Recurrent Neural Networks.

Journal of chemical information and modeling
We present three deep learning sequence-based prediction models for peptide properties including hemolysis, solubility, and resistance to nonspecific interactions that achieve comparable results to the state-of-the-art models. Our sequence-based solu...

Performance analysis of pretrained convolutional neural network models for ophthalmological disease classification.

Arquivos brasileiros de oftalmologia
PURPOSE: This study aimed to evaluate the classification performance of pretrained convolutional neural network models or architectures using fundus image dataset containing eight disease labels.

Reproducibility of linear and angular cephalometric measurements obtained by an artificial-intelligence assisted software (WebCeph) in comparison with digital software (AutoCEPH) and manual tracing method.

Dental press journal of orthodontics
INTRODUCTION: It has been suggested that human errors during manual tracing of linear/angular cephalometric parameters can be eliminated by using computer-aided analysis. The landmarks, however, are located manually and the computer system completes ...

GOGCN: Graph Convolutional Network on Gene Ontology for Functional Similarity Analysis of Genes.

IEEE/ACM transactions on computational biology and bioinformatics
The measurement of gene functional similarity plays a critical role in numerous biological applications, such as gene clustering, the construction of gene similarity networks. However, most existing approaches still rely heavily on traditional comput...

Transfer Learning Based Lightweight Ensemble Model for Imbalanced Breast Cancer Classification.

IEEE/ACM transactions on computational biology and bioinformatics
Automated classification of breast cancer can often save lives, as manual detection is usually time-consuming & expensive. Since the last decade, deep learning techniques have been most widely used for the automatic classification of breast cancer us...

Deep learning based automated quantification of urethral plate characteristics using the plate objective scoring tool (POST).

Journal of pediatric urology
INTRODUCTION: The plate objective scoring tool (POST) was recently introduced as a reproducible and precise approach to quantifying urethral plate (UP) characteristics and guide to selecting particular surgical techniques. However, defining the landm...

Evaluating the accuracy of automated cephalometric analysis based on artificial intelligence.

BMC oral health
BACKGROUND: The purpose of this study was to evaluate the accuracy of automatic cephalometric landmark localization and measurements using cephalometric analysis via artificial intelligence (AI) compared with computer-assisted manual analysis.

Enzyme function prediction using contrastive learning.

Science (New York, N.Y.)
Enzyme function annotation is a fundamental challenge, and numerous computational tools have been developed. However, most of these tools cannot accurately predict functional annotations, such as enzyme commission (EC) number, for less-studied protei...