AIMC Topic: Diagnosis, Computer-Assisted

Clear Filters Showing 481 to 490 of 1778 articles

Machine Learning Applications in Solid Organ Transplantation and Related Complications.

Frontiers in immunology
The complexity of transplant medicine pushes the boundaries of innate, human reasoning. From networks of immune modulators to dynamic pharmacokinetics to variable postoperative graft survival to equitable allocation of scarce organs, machine learning...

Deep Learning Computer-aided Polyp Detection Reduces Adenoma Miss Rate: A United States Multi-center Randomized Tandem Colonoscopy Study (CADeT-CS Trial).

Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association
BACKGROUND & AIMS: Artificial intelligence-based computer-aided polyp detection (CADe) systems are intended to address the issue of missed polyps during colonoscopy. The effect of CADe during screening and surveillance colonoscopy has not previously ...

ECG data dependency for atrial fibrillation detection based on residual networks.

Scientific reports
Atrial fibrillation (AF) is an arrhythmia that can cause blood clot and may lead to stroke and heart failure. To detect AF, deep learning-based detection algorithms have recently been developed. However, deep learning models were often trained with l...

A Systematic Literature Review on Particle Swarm Optimization Techniques for Medical Diseases Detection.

Computational and mathematical methods in medicine
Artificial Intelligence (AI) is the domain of computer science that focuses on the development of machines that operate like humans. In the field of AI, medical disease detection is an instantly growing domain of research. In the past years, numerous...

Skin cancer classification via convolutional neural networks: systematic review of studies involving human experts.

European journal of cancer (Oxford, England : 1990)
BACKGROUND: Multiple studies have compared the performance of artificial intelligence (AI)-based models for automated skin cancer classification to human experts, thus setting the cornerstone for a successful translation of AI-based tools into clinic...

Automatic identification of suspicious bone metastatic lesions in bone scintigraphy using convolutional neural network.

BMC medical imaging
BACKGROUND: We aimed to construct an artificial intelligence (AI) guided identification of suspicious bone metastatic lesions from the whole-body bone scintigraphy (WBS) images by convolutional neural networks (CNNs).

Interpretation of Electrocardiogram Heartbeat by CNN and GRU.

Computational and mathematical methods in medicine
The diagnosis of electrocardiogram (ECG) is extremely onerous and inefficient, so it is necessary to use a computer-aided diagnosis of ECG signals. However, it is still a challenging problem to design high-accuracy ECG algorithms suitable for the med...

A Deep Learning-Enabled Electrocardiogram Model for the Identification of a Rare Inherited Arrhythmia: Brugada Syndrome.

The Canadian journal of cardiology
BACKGROUND: Brugada syndrome is a major cause of sudden cardiac death in young people and has distinctive electrocardiographic (ECG) features. We aimed to develop a deep learning-enabled ECG model for automatic screening for Brugada syndrome to ident...

Hybrid Gradient Descent Grey Wolf Optimizer for Optimal Feature Selection.

BioMed research international
Feature selection is the process of decreasing the number of features in a dataset by removing redundant, irrelevant, and randomly class-corrected data features. By applying feature selection on large and highly dimensional datasets, the redundant fe...