AIMC Topic: Humans

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Chatbots talk Strabismus: Can AI become the new patient Educator?

International journal of medical informatics
BACKGROUND: Strabismus is a common eye condition affecting both children and adults. Effective patient education is crucial for informed decision-making, but traditional methods often lack accessibility and engagement. Chatbots powered by AI have eme...

Pseudo-random Number Generator Influences on Average Treatment Effect Estimates Obtained with Machine Learning.

Epidemiology (Cambridge, Mass.)
BACKGROUND: The use of machine learning to estimate exposure effects introduces a dependence between the results of an empirical study and the value of the seed used to fix the pseudo-random number generator.

Don't Let Your Analysis Go to Seed: On the Impact of Random Seed on Machine Learning-based Causal Inference.

Epidemiology (Cambridge, Mass.)
Machine learning techniques for causal effect estimation can enhance the reliability of epidemiologic analyses, reducing their dependence on correct model specifications. However, the stochastic nature of many machine learning algorithms implies that...

Altered dynamic large-scale brain networks and combined machine learning in primary angle-closure glaucoma.

Neuroscience
Primary angle-closure glaucoma (PACG) is a severe and irreversible blinding eye disease characterized by progressive retinal ganglion cell death. However, prior research has predominantly focused on static brain activity changes, neglecting the explo...

Predicting tremor improvement after MRgFUS thalamotomy in essential tremor from preoperative spontaneous brain activity: A machine learning approach.

Science bulletin
Magnetic resonance-guided focused ultrasound surgery (MRgFUS) thalamotomy is an emerging technique for medication-refractory essential tremor (ET), but with variable outcomes. This study used pattern regression analysis to identify brain signatures p...

Comparing the effectiveness of robotic plantarflexion resistance and biofeedback between overground and treadmill walking.

Journal of biomechanics
Individuals with diminished walking performance caused by neuromuscular impairments often lack plantar flexion muscle activity. Robotic devices have been developed to address these issues and increase walking performance. While these devices have sho...

A systematic review of few-shot learning in medical imaging.

Artificial intelligence in medicine
The lack of annotated medical images limits the performance of deep learning models, which usually need large-scale labelled datasets. Few-shot learning techniques can reduce data scarcity issues and enhance medical image analysis speed and robustnes...

Machine Learning Recognizes Frequency-Following Responses in American Adults: Effects of Reference Spectrogram and Stimulus Token.

Perceptual and motor skills
Electrophysiological research has been widely utilized to study brain responses to acoustic stimuli. The frequency-following response (FFR), a non-invasive reflection of how the brain encodes acoustic stimuli, is a particularly propitious electrophys...

Predicting drug resistance using artificial intelligence and clinical MALDI-TOF mass spectra.

mSystems
Matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS) is widely used in clinical microbiology laboratories for bacterial identification but its use for detection of antimicrobial resistance (AMR) remains limited....

Using Data-Driven Algorithms with Large-Scale Plasma Proteomic Data to Discover Novel Biomarkers for Diagnosing Depression.

Journal of proteome research
Given recent technological advances in proteomics, it is now possible to quantify plasma proteomes in large cohorts of patients to screen for biomarkers and to guide the early diagnosis and treatment of depression. Here we used CatBoost machine learn...