AIMC Topic: Neural Networks, Computer

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Constructing a Predictive Model for Psychological Distress of Young- and Middle-Aged Gynaecological Cancer Patients.

Journal of evaluation in clinical practice
BACKGROUND: Cancer patients experience substantial psychological distress which causes the reduction of the quality of life. However, the risk of psychological distress has not been well predicted especially in young- and middle-aged gynaecological c...

Preparing for downstream tasks in artificial intelligence for dental radiology: a baseline performance comparison of deep learning models.

Dento maxillo facial radiology
OBJECTIVES: To compare the performance of the convolutional neural network (CNN) with the vision transformer (ViT), and the gated multilayer perceptron (gMLP) in the classification of radiographic images of dental structures.

Ovarian-adnexal reporting and data system MRI scoring: diagnostic accuracy, interobserver agreement, and applicability to machine learning.

The British journal of radiology
OBJECTIVES: To evaluate the interobserver agreement and diagnostic accuracy of ovarian-adnexal reporting and data system magnetic resonance imaging (O-RADS MRI) and applicability to machine learning.

Evaluation of automated pediatric sleep stage classification using U-Sleep: a convolutional neural network.

Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine
STUDY OBJECTIVES: U-Sleep is a publicly available automated sleep stager, but has not been independently validated using pediatric data. We aimed to (1) test the hypothesis that U-Sleep performance is equivalent to trained humans, using a concordance...

Comparing Artificial Intelligence Guided Image Assessment to Current Methods of Burn Assessment.

Journal of burn care & research : official publication of the American Burn Association
Appropriate identification of burn depth and size is paramount. Despite the development of burn depth assessment aids [eg, laser Doppler imaging (LDI)], clinical assessment, which assesses partial-thickness burn depth with 67% accuracy, currently rem...

Quantitative and Morphology-Based Deep Convolutional Neural Network Approaches for Osteosarcoma Survival Prediction in the Neoadjuvant and Metastatic Settings.

Clinical cancer research : an official journal of the American Association for Cancer Research
PURPOSE: Necrosis quantification in the neoadjuvant setting using pathology slide review is the most important validated prognostic marker in conventional osteosarcoma. Herein, we explored three deep-learning strategies on histology samples to predic...

Advances in computer vision and deep learning-facilitated early detection of melanoma.

Briefings in functional genomics
Melanoma is characterized by its rapid progression and high mortality rates, making early and accurate detection essential for improving patient outcomes. This paper presents a comprehensive review of significant advancements in early melanoma detect...

SCREEN: A Graph-based Contrastive Learning Tool to Infer Catalytic Residues and Assess Enzyme Mutations.

Genomics, proteomics & bioinformatics
The accurate identification of catalytic residues contributes to our understanding of enzyme functions in biological processes and pathways. The increasing number of protein sequences necessitates computational tools for the automated prediction of c...

DeepMEns: an ensemble model for predicting sgRNA on-target activity based on multiple features.

Briefings in functional genomics
The CRISPR/Cas9 system developed from Streptococcus pyogenes (SpCas9) has high potential in gene editing. However, its successful application is hindered by the considerable variability in target efficiencies across different single guide RNAs (sgRNA...

Enhancement of structural and functional photoacoustic imaging based on a reference-inputted convolutional neural network.

Optics express
Photoacoustic microscopy has demonstrated outstanding performance in high-resolution functional imaging. However, in the process of photoacoustic imaging, the photoacoustic signals will be polluted by inevitable background noise. Besides, the image q...