AIMC Topic: Artificial Intelligence

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The Use of Artificial Intelligence and Machine Learning in Surgery: A Comprehensive Literature Review.

The American surgeon
Interest in the use of artificial intelligence (AI) and machine learning (ML) in medicine has grown exponentially over the last few years. With its ability to enhance speed, precision, and efficiency, AI has immense potential, especially in the field...

FDA-regulated AI Algorithms: Trends, Strengths, and Gaps of Validation Studies.

Academic radiology
RATIONALE AND OBJECTIVES: To assess key trends, strengths, and gaps in validation studies of the Food and Drug Administration (FDA)-regulated imaging-based artificial intelligence/machine learning (AI/ML) algorithms.

Leveraging artificial intelligence to advance the understanding of chemical neurotoxicity.

Neurotoxicology
Neurotoxicology is a specialty that aims to understand and explain the impact of chemicals, xenobiotics and physical conditions on nervous system function throughout the life span. Herein, we point to the need for integration of novel translational b...

Plant Factory: A New Playground of Industrial Communication and Computing.

Sensors (Basel, Switzerland)
Plant Factory is a newly emerging industry aiming at transforming crop production to an unprecedented model by leveraging industrial automation and informatics. However, today's plant factory and vertical farming industry are still in a primitive pha...

NeuroVis: Real-Time Neural Information Measurement and Visualization of Embodied Neural Systems.

Frontiers in neural circuits
Understanding the real-time dynamical mechanisms of neural systems remains a significant issue, preventing the development of efficient neural technology and user trust. This is because the mechanisms, involving various neural spatial-temporal ingred...

A Rapid Artificial Intelligence-Based Computer-Aided Diagnosis System for COVID-19 Classification from CT Images.

Behavioural neurology
The excessive number of COVID-19 cases reported worldwide so far, supplemented by a high rate of false alarms in its diagnosis using the conventional polymerase chain reaction method, has led to an increased number of high-resolution computed tomogra...

A multi-stage machine learning model for diagnosis of esophageal manometry.

Artificial intelligence in medicine
High-resolution manometry (HRM) is the primary procedure used to diagnose esophageal motility disorders. Its manual interpretation and classification, including evaluation of swallow-level outcomes and then derivation of a study-level diagnosis based...

Non-Linear Regression Models with Vibration Amplitude Optimization Algorithms in a Microturbine.

Sensors (Basel, Switzerland)
Machinery condition monitoring and failure analysis is an engineering problem to pay attention to among all those being studied. Excessive vibration in a rotating system can damage the system and cannot be ignored. One option to prevent vibrations in...

A cross-scanner and cross-tracer deep learning method for the recovery of standard-dose imaging quality from low-dose PET.

European journal of nuclear medicine and molecular imaging
PURPOSE: A critical bottleneck for the credibility of artificial intelligence (AI) is replicating the results in the diversity of clinical practice. We aimed to develop an AI that can be independently applied to recover high-quality imaging from low-...

Attention Autoencoder for Generative Latent Representational Learning in Anomaly Detection.

Sensors (Basel, Switzerland)
Today, accurate and automated abnormality diagnosis and identification have become of paramount importance as they are involved in many critical and life-saving scenarios. To accomplish such frontiers, we propose three artificial intelligence models ...