AIMC Topic: Adult

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Deep Learning in Knee MRI: A Prospective Study to Enhance Efficiency, Diagnostic Confidence and Sustainability.

Academic radiology
RATIONALE AND OBJECTIVES: The objective of this study was to evaluate a combination of deep learning (DL)-reconstructed parallel acquisition technique (PAT) and simultaneous multislice (SMS) acceleration imaging in comparison to conventional knee ima...

Deep reinforcement learning for Type 1 Diabetes: Dual PPO controller for personalized insulin management.

Computers in biology and medicine
BACKGROUND: Managing blood glucose levels in Type 1 Diabetes Mellitus (T1DM) is essential to prevent complications. Traditional insulin delivery methods often require significant patient involvement, limiting automation. Reinforcement Learning (RL)-b...

Automatic cough detection via a multi-sensor smart garment using machine learning.

Computers in biology and medicine
Coughing behavior is associated with conditions such as sleep apnea, asthma, and chronic obstructive pulmonary disorder and can severely affect quality of life in those affected. In this context, coughing quantification is often important, but routin...

Artificial Intelligence-Enabled Prediction of Heart Failure Risk From Single-Lead Electrocardiograms.

JAMA cardiology
IMPORTANCE: Despite the availability of disease-modifying therapies, scalable strategies for heart failure (HF) risk stratification remain elusive. Portable devices capable of recording single-lead electrocardiograms (ECGs) may enable large-scale com...

Whole Brain 3D T1 Mapping in Multiple Sclerosis Using Standard Clinical Images Compared to MP2RAGE and MR Fingerprinting.

NMR in biomedicine
Quantitative T1 and T2 mapping is a useful tool to assess properties of healthy and diseased tissues. However, clinical diagnostic imaging remains dominated by relaxation-weighted imaging without direct collection of relaxation maps. Dedicated resear...

Radiation oncology patients' perceptions of artificial intelligence and machine learning in cancer care: A multi-centre cross-sectional study.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
AIM: The use of artificial intelligence (AI) and machine learning (ML) is increasingly widespread in radiation oncology. However, patient engagement to date has been poor. Respect for persons in the healthcare setting and the principle of informed co...

Role of artificial intelligence -based machine learning model in predicting HER2/neu gene status in breast cancer.

Pathology, research and practice
Our study investigated the predictive efficacy of AI-based Machine Learning (ML) model for determining HER2 status in a population of 3424 breast cancer patients. Multivariate logistic regression analysis identified several independent variables that...

Temporal validation of machine learning models for pre-eclampsia prediction using routinely collected maternal characteristics: A validation study.

Computers in biology and medicine
BACKGROUND: Pre-eclampsia (PE) contributes to more than one-fourth of all maternal deaths and half a million newborn deaths worldwide every year. Early screening and interventions can reduce PE incidence and related complications. We aim to 1) tempor...

Quantifying axonal features of human superficial white matter from three-dimensional multibeam serial electron microscopy data assisted by deep learning.

NeuroImage
Short-range association fibers located in the superficial white matter play an important role in mediating higher-order cognitive function in humans. Detailed morphological characterization of short-range association fibers at the microscopic level p...

A machine learning approach to differentiate stage IV from stage I colorectal cancer.

Computers in biology and medicine
BACKGROUND AND AIM: The stage at which Colorectal cancer (CRC) diagnosed is a crucial prognostic factor. Our study proposed a novel approach to aid in the diagnosis of stage IV CRC by utilizing supervised machine learning, analyzing clinical history,...