AIMC Topic: Neural Networks, Computer

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Computer Vision and Videomics in Otolaryngology-Head and Neck Surgery: Bridging the Gap Between Clinical Needs and the Promise of Artificial Intelligence.

Otolaryngologic clinics of North America
This article discusses the role of computer vision in otolaryngology, particularly through endoscopy and surgery. It covers recent applications of artificial intelligence (AI) in nonradiologic imaging within otolaryngology, noting the benefits and ch...

Multi-Task ADME/PK prediction at industrial scale: leveraging large and diverse experimental datasets.

Molecular informatics
ADME (Absorption, Distribution, Metabolism, Excretion) properties are key parameters to judge whether a drug candidate exhibits a desired pharmacokinetic (PK) profile. In this study, we tested multi-task machine learning (ML) models to predict ADME a...

Efficient and scalable prediction of stochastic reaction-diffusion processes using graph neural networks.

Mathematical biosciences
The dynamics of locally interacting particles that are distributed in space give rise to a multitude of complex behaviours. However the simulation of reaction-diffusion processes which model such systems is highly computationally expensive, the cost ...

Integrating Radiomics and Neural Networks for Knee Osteoarthritis Incidence Prediction.

Arthritis & rheumatology (Hoboken, N.J.)
OBJECTIVE: Accurately predicting knee osteoarthritis (KOA) is essential for early detection and personalized treatment. We aimed to develop and test a magnetic resonance imaging (MRI)-based joint space (JS) radiomic model (RM) to predict radiographic...

Prediction of Human Liver Microsome Clearance with Chirality-Focused Graph Neural Networks.

Journal of chemical information and modeling
In drug candidate design, clearance is one of the most crucial pharmacokinetic parameters to consider. Recent advancements in machine learning techniques coupled with the growing accumulation of drug data have paved the way for the construction of co...

Neural Network Enables High Accuracy for Hepatitis B Surface Antigen Detection with a Plasmonic Platform.

Nano letters
The detection of hepatitis B surface antigen (HBsAg) is critical in diagnosing hepatitis B virus (HBV) infection. However, existing clinical detection technologies inevitably cause certain inaccuracies, leading to delayed or unwarranted treatment. He...

Utilizing Deep Feature Fusion for Automatic Leukemia Classification: An Internet of Medical Things-Enabled Deep Learning Framework.

Sensors (Basel, Switzerland)
Acute lymphoblastic leukemia, commonly referred to as ALL, is a type of cancer that can affect both the blood and the bone marrow. The process of diagnosis is a difficult one since it often calls for specialist testing, such as blood tests, bone marr...

Combined expert-in-the-loop-random forest multiclass segmentation U-net based artificial intelligence model: evaluation of non-small cell lung cancer in fibrotic and non-fibrotic microenvironments.

Journal of translational medicine
BACKGROUND: The tumor microenvironment (TME) plays a key role in lung cancer initiation, proliferation, invasion, and metastasis. Artificial intelligence (AI) methods could potentially accelerate TME analysis. The aims of this study were to (1) asses...

Clinical data-based modeling of IVF live birth outcome and its application.

Reproductive biology and endocrinology : RB&E
BACKGROUND: The low live birth rate and difficult decision-making of the in vitro fertilization (IVF) treatment regimen bring great trouble to patients and clinicians. Based on the retrospective clinical data of patients undergoing the IVF cycle, thi...

A comprehensive health assessment approach using ensemble deep learning model for remote patient monitoring with IoT.

Scientific reports
The goal of this research is to create an ensemble deep learning model for Internet of Things (IoT) applications that specifically target remote patient monitoring (RPM) by integrating long short-term memory (LSTM) networks and convolutional neural n...