AI Medical Compendium

Explore the latest research on artificial intelligence and machine learning in medicine.

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Neural network procedures for the cholera disease system with public health mediations.

Computers in biology and medicine
Severe gastrointestinal infections and watery diseases like cholera are still a major worldwide medical concern in the developing nations. A mathematical system contains some necessary dynamics based on the cholera spread to investigate the influence...

Fair and explainable Myocardial Infarction (MI) prediction: Novel strategies for feature selection and class imbalance correction.

Computers in biology and medicine
The rising incidences of myocardial infarction (MI), often affecting individuals without traditional risk factors, highlight the urgent need for improved early detection using personal health data. However, health surveys and electronic health record...

Multiple-Instance Learning for thyroid gland disease classification: A hands-on experience.

Computers in biology and medicine
The morphological diagnosis of thyroid gland neoplasms presents a dual challenge: it requires the expertise of highly trained specialists and considerable time, particularly when evaluating multiple whole slide images (WSIs) from a single patient. Th...

CMINNs: Compartment model informed neural networks - Unlocking drug dynamics.

Computers in biology and medicine
In the field of pharmacokinetics and pharmacodynamics (PKPD) modeling, which plays a pivotal role in the drug development process, traditional models frequently encounter difficulties in fully encapsulating the complexities of drug absorption, distri...

DeepCTG® 2.0: Development and validation of a deep learning model to detect neonatal acidemia from cardiotocography during labor.

Computers in biology and medicine
Cardiotocography (CTG) is the main tool available to detect neonatal acidemia during delivery. Presently, obstetricians and midwives primarily rely on visual interpretation, leading to a significant intra-observer variability. In this paper, we build...

RS-MOCO: A deep learning-based topology-preserving image registration method for cardiac T1 mapping.

Computers in biology and medicine
Cardiac T1 mapping can evaluate various clinical symptoms of myocardial tissue. However, there is currently a lack of effective, robust, and efficient methods for motion correction in cardiac T1 mapping. In this paper, we propose a deep learning-base...

Fusing CNNs and attention-mechanisms to improve real-time indoor Human Activity Recognition for classifying home-based physical rehabilitation exercises.

Computers in biology and medicine
Physical rehabilitation plays a critical role in enhancing health outcomes globally. However, the shortage of physiotherapists, particularly in developing countries where the ratio is approximately ten physiotherapists per million people, poses a sig...

Detection of retinal diseases using an accelerated reused convolutional network.

Computers in biology and medicine
Convolutional neural networks are continually evolving; with some efforts aimed at improving accuracy, others at increasing speed, and some at enhancing accessibility. Improving accessibility broadens the application of neural networks across a wider...

Novel design of fractional cholesterol dynamics and drug concentrations model with analysis on machine predictive networks.

Computers in biology and medicine
Within the intricate fabric of human physiology, cholesterol, a lipid present in cell membranes exerts a discernible effect on the concentration of the drug in human body that influence the aspects of drug pharmacokinetics. The objective of this work...

Implementing deep learning on edge devices for snoring detection and reduction.

Computers in biology and medicine
This study introduces MinSnore, a novel deep learning model tailored for real-time snoring detection and reduction, specifically designed for deployment on low-configuration edge devices. By integrating MobileViTV3 blocks into the Dynamic MobileNetV3...