AIMC Topic: Deep Learning

Clear Filters Showing 24671 to 24680 of 28423 articles

[Research Progress and Prospects of Minimally Invasive Surgical Instrument Segmentation Methods Based on Artificial Intelligence].

Zhongguo yi liao qi xie za zhi = Chinese journal of medical instrumentation
With the development of artificial intelligence technology and the growing demand for minimally invasive surgery, the intelligentization of minimally invasive surgery has become a current research hotspot. Surgical instrument segmentation is a highly...

Use of artificial intelligence to detect dental caries on intraoral photos.

Quintessence international (Berlin, Germany : 1985)
Dental caries is one of the most common diseases globally. It affects children and adults living in poverty, who have the most limited access to dental care. Left unexamined and untreated in the early stages, treatments for late-stage and severe cari...

Analysis of TEM micrographs with deep learning reveals APOE genotype-specific associations between HDL particle diameter and Alzheimer's dementia.

Cell reports methods
High-density lipoprotein (HDL) particle diameter distribution is informative in the diagnosis of many conditions, including Alzheimer's disease (AD). However, obtaining an accurate HDL size measurement is challenging. We demonstrated the utility of m...

Artificial intelligence in dermatopathology: a systematic review.

Clinical and experimental dermatology
Medical research, driven by advancing technologies like artificial intelligence (AI), is transforming healthcare. Dermatology, known for its visual nature, benefits from AI, especially in dermatopathology with digitized slides. This review explores A...

A fusion model of manually extracted visual features and deep learning features for rebleeding risk stratification in peptic ulcers.

Nan fang yi ke da xue xue bao = Journal of Southern Medical University
OBJECTIVES: We propose a multi-feature fusion model based on manually extracted features and deep learning features from endoscopic images for grading rebleeding risk of peptic ulcers.

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...

EnsembleSE: identification of super-enhancers based on ensemble learning.

Briefings in functional genomics
Super-enhancers (SEs) are typically located in the regulatory regions of genes, driving high-level gene expression. Identifying SEs is crucial for a deeper understanding of gene regulatory networks, disease mechanisms, and the development and physiol...

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...

A comprehensive review of deep learning-based approaches for drug-drug interaction prediction.

Briefings in functional genomics
Deep learning models have made significant progress in the biomedical field, particularly in the prediction of drug-drug interactions (DDIs). DDIs are pharmacodynamic reactions between two or more drugs in the body, which may lead to adverse effects ...

Using artificial intelligence and statistics for managing peritoneal metastases from gastrointestinal cancers.

Briefings in functional genomics
OBJECTIVE: The primary objective of this study is to investigate various applications of artificial intelligence (AI) and statistical methodologies for analyzing and managing peritoneal metastases (PM) caused by gastrointestinal cancers.