AIMC Topic: Humans

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Manual data labeling, radiology, and artificial intelligence: It is a dirty job, but someone has to do it.

Magnetic resonance imaging
In this letter to the editor, authors highlight the key role of data labeling in training AI models for medical imaging, discussing the complexities, resource demands, costs, and the relevance of quality control in the labeling process including the ...

Assessing operator stress in collaborative robotics: A multimodal approach.

Applied ergonomics
In the era of Industry 4.0, the study of Human-Robot Collaboration (HRC) in advancing modern manufacturing and automation is paramount. An operator approaching a collaborative robot (cobot) may have feelings of distrust, and experience discomfort and...

Keeping pace in the age of innovation: The perspective of Dutch pharmaceutical science students on the position of machine learning training in an undergraduate curriculum.

Currents in pharmacy teaching & learning
BACKGROUND: Over the years, approaches of the pharmaceutical industry to discover and develop drugs have changed rapidly due to new scientific trends. Among others, they have started to explore Machine Learning (ML), a subset of Artificial Intelligen...

Assessment of simulated snoring sounds with artificial intelligence for the diagnosis of obstructive sleep apnea.

Sleep medicine
BACKGROUND: Performing simulated snoring (SS) is a commonly used method to evaluate the source of snoring in obstructive sleep apnea (OSA). SS sounds is considered as a potential biomarker for OSA. SS sounds can be easily recorded, which is a cost-ef...

Application of Machine Learning to Osteoporosis and Osteopenia Screening Using Hand Radiographs.

The Journal of hand surgery
PURPOSE: Fragility fractures associated with osteoporosis and osteopenia are a common cause of morbidity and mortality. Current methods of diagnosing low bone mineral density require specialized dual x-ray absorptiometry (DXA) scans. Plain hand radio...

hERGBoost: A gradient boosting model for quantitative IC prediction of hERG channel blockers.

Computers in biology and medicine
The human ether-a-go-go-related gene (hERG) potassium channel is pivotal in drug discovery due to its susceptibility to blockage by drug candidate molecules, which can cause severe cardiotoxic effects. Consequently, identifying and excluding potentia...

Augmenting a spine CT scans dataset using VAEs, GANs, and transfer learning for improved detection of vertebral compression fractures.

Computers in biology and medicine
In recent years, deep learning has become a popular tool to analyze and classify medical images. However, challenges such as limited data availability, high labeling costs, and privacy concerns remain significant obstacles. As such, generative models...

Explainable machine learning versus known nomogram for predicting non-sentinel lymph node metastases in breast cancer patients: A comparative study.

Computers in biology and medicine
INTRODUCTION: Axillary lymph node dissection (ALND) is the standard of care for breast cancer patients with positive sentinel lymph nodes (SLN), which are the first lymph nodes that drain the breast. However, many patients with positive SLNs may not ...

Decoding face identity: A reverse-correlation approach using deep learning.

Cognition
Face recognition is crucial for social interactions. Traditional approaches primarily rely on subjective judgment, utilizing a pre-selected set of facial features based on literature or intuition to identify critical facial features for face recognit...

Natural Language Processing to Adjudicate Heart Failure Hospitalizations in Global Clinical Trials.

Circulation. Heart failure
BACKGROUND: Medical record review by a physician clinical events committee is the gold standard for identifying cardiovascular outcomes in clinical trials, but is labor-intensive and poorly reproducible. Automated outcome adjudication by artificial i...