AIMC Topic: Healthy Volunteers
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Correlation of choroidal thickness with age in healthy subjects: automatic detection and segmentation using a deep learning model.
International ophthalmology
Apr 5, 2022
PROPOSE: The proposed deep learning model with a mask region-based convolutional neural network (Mask R-CNN) can predict choroidal thickness automatically. Changes in choroidal thickness with age can be detected with manual measurements. In this stud...
Evaluation of the pharmacokinetic drug-drug interaction between the antiretroviral agents fostemsavir and maraviroc: a single-sequence crossover study in healthy participants.
HIV research & clinical practice
Mar 14, 2022
BACKGROUND: Fostemsavir is an oral prodrug of temsavir, a first-in-class attachment inhibitor that binds HIV-1 gp120, preventing initial HIV attachment and entry into host immune cells.
A machine learning-based on-demand sweat glucose reporting platform.
Scientific reports
Feb 14, 2022
Diabetes is a chronic endocrine disease that occurs due to an imbalance in glucose levels and altering carbohydrate metabolism. It is a leading cause of morbidity, resulting in a reduced quality of life even in developed societies, primarily affected...
Multimodal deep learning applied to classify healthy and disease states of human microbiome.
Scientific reports
Jan 17, 2022
Metagenomic sequencing methods provide considerable genomic information regarding human microbiomes, enabling us to discover and understand microbial diseases. Compositional differences have been reported between patients and healthy people, which co...
A deep learning framework identifies dimensional representations of Alzheimer's Disease from brain structure.
Nature communications
Dec 3, 2021
Heterogeneity of brain diseases is a challenge for precision diagnosis/prognosis. We describe and validate Smile-GAN (SeMI-supervised cLustEring-Generative Adversarial Network), a semi-supervised deep-clustering method, which examines neuroanatomical...
Brain oscillatory correlates of visuomotor adaptive learning.
NeuroImage
Oct 21, 2021
Sensorimotor adaptation involves the recalibration of the mapping between motor command and sensory feedback in response to movement errors. Although adaptation operates within individual movements on a trial-to-trial basis, it can also undergo learn...
Cutoff criteria for the placebo response: a cluster and machine learning analysis of placebo analgesia.
Scientific reports
Sep 28, 2021
Computations of placebo effects are essential in randomized controlled trials (RCTs) for separating the specific effects of treatments from unspecific effects associated with the therapeutic intervention. Thus, the identification of placebo responder...
Generative Adversarial Networks-Based Data Augmentation for Brain-Computer Interface.
IEEE transactions on neural networks and learning systems
Aug 31, 2021
The performance of a classifier in a brain-computer interface (BCI) system is highly dependent on the quality and quantity of training data. Typically, the training data are collected in a laboratory where the users perform tasks in a controlled envi...
Is machine learning and automatic classification of swimming data what unlocks the power of inertial measurement units in swimming?
Journal of sports sciences
May 9, 2021
Researchers have heralded the power of inertial sensors as a reliable swimmer-centric monitoring technology, however, regular uptake of this technology has not become common practice. Twenty-six elite swimmers participated in this study. An IMU (100H...
A robot-aided visuomotor wrist training induces gains in proprioceptive and movement accuracy in the contralateral wrist.
Scientific reports
Mar 5, 2021
Proprioceptive training is a neurorehabilitation approach known to improve proprioceptive acuity and motor performance of a joint/limb system. Here, we examined if such learning transfers to the contralateral joints. Using a robotic exoskeleton, 15 h...
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