AIMC Topic: Artificial Intelligence

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Daily Human Activity Recognition Using Non-Intrusive Sensors.

Sensors (Basel, Switzerland)
In recent years, Artificial Intelligence Technologies (AIT) have been developed to improve the quality of life of the elderly and their safety in the home. This work focuses on developing a system capable of recognising the most usual activities in t...

Development of novel artificial intelligence systems to predict facial morphology after orthognathic surgery and orthodontic treatment in Japanese patients.

Scientific reports
From a socio-psychological standpoint, improving the morphology of the facial soft-tissues is regarded as an important therapeutic goal in modern orthodontic treatment. Currently, many of the algorithms used in commercially available software program...

Real-world artificial intelligence-based opportunistic screening for diabetic retinopathy in endocrinology and indigenous healthcare settings in Australia.

Scientific reports
This study investigated the diagnostic performance, feasibility, and end-user experiences of an artificial intelligence (AI)-assisted diabetic retinopathy (DR) screening model in real-world Australian healthcare settings. The study consisted of two c...

Visual Information Features and Machine Learning for Wushu Arts Tracking.

Journal of healthcare engineering
Martial arts tracking is an important research topic in computer vision and artificial intelligence. It has extensive and vital applications in video monitoring, interactive animation and 3D simulation, motion capture, and advanced human-computer int...

Accuracy of automated machine learning in classifying retinal pathologies from ultra-widefield pseudocolour fundus images.

The British journal of ophthalmology
AIMS: Automated machine learning (AutoML) is a novel tool in artificial intelligence (AI). This study assessed the discriminative performance of AutoML in differentiating retinal vein occlusion (RVO), retinitis pigmentosa (RP) and retinal detachment ...

Parallel and Recurrent Cascade Models as a Unifying Force for Understanding Subcellular Computation.

Neuroscience
Neurons are very complicated computational devices, incorporating numerous non-linear processes, particularly in their dendrites. Biophysical models capture these processes directly by explicitly modelling physiological variables, such as ion channel...

Machine Learning-Assisted Sampling of Surfance-Enhanced Raman Scattering (SERS) Substrates Improve Data Collection Efficiency.

Applied spectroscopy
Surface-enhanced Raman scattering (SERS) is a powerful technique for sensitive label-free analysis of chemical and biological samples. While much recent work has established sophisticated automation routines using machine learning and related artific...

Applications of artificial intelligence to enzyme and pathway design for metabolic engineering.

Current opinion in biotechnology
Metabolic engineering for developing industrial strains capable of overproducing bioproducts requires good understanding of cellular metabolism, including metabolic reactions and enzymes. However, metabolic pathways and enzymes involved are still unk...

Artificial Intelligence in PET: An Industry Perspective.

PET clinics
Artificial intelligence (AI) has significant potential to positively impact and advance medical imaging, including positron emission tomography (PET) imaging applications. AI has the ability to enhance and optimize all aspects of the PET imaging chai...

Artificial Intelligence-Based Prediction of Lung Cancer Risk Using Nonimaging Electronic Medical Records: Deep Learning Approach.

Journal of medical Internet research
BACKGROUND: Artificial intelligence approaches can integrate complex features and can be used to predict a patient's risk of developing lung cancer, thereby decreasing the need for unnecessary and expensive diagnostic interventions.