AIMC Topic: Humans

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A machine learning method for predicting the probability of MODS using only non-invasive parameters.

Computer methods and programs in biomedicine
OBJECTIVES: Timely and accurate prediction of multiple organ dysfunction syndrome (MODS) is essential for the rescue and treatment of trauma patients However, existing methods are invasive, easily affected by artifacts and can be difficult to perform...

Temporomandibular joint segmentation in MRI images using deep learning.

Journal of dentistry
OBJECTIVES: Temporomandibular joint (TMJ) internal derangements (ID) represent the most prevalent temporomandibular joint disorder (TMD) in the population and its diagnosis typically relies on magnetic resonance imaging (MRI). TMJ articular discs in ...

Evaluation of machine learning methods for covariate data imputation in pharmacometrics.

CPT: pharmacometrics & systems pharmacology
Missing data create challenges in clinical research because they lead to loss of statistical power and potentially to biased results. Missing covariate data must be handled with suitable approaches to prepare datasets for pharmacometric analyses, suc...

Deep Learning-Based Multicapturer SERS Platform on Plasmonic Nanocube Metasurfaces for Multiplex Detection of Organophosphorus Pesticides in Environmental Water.

Analytical chemistry
In situ rapid detection of contaminants in environmental water is crucial for protecting the ecological environment and human health; however, it is always hindered by the complexity of sample matrices, trace content, and unknown species. Herein, we ...

Machine-Learning Classification of Pulse Waveform Quality.

Sensors (Basel, Switzerland)
Pulse measurements made using wearable devices can aid the monitoring of human physiological condition. Accurate estimation of waveforms is often difficult for nonexperts; motion artifacts may occur during tonometry measurements when the skin-sensor ...

Sagittal intervertebral rotational motion: a deep learning-based measurement on flexion-neutral-extension cervical lateral radiographs.

BMC musculoskeletal disorders
BACKGROUND: The analysis of sagittal intervertebral rotational motion (SIRM) can provide important information for the evaluation of cervical diseases. Deep learning has been widely used in spinal parameter measurements, however, there are few invest...

Effect of AI-assisted software on inter- and intra-observer variability for the X-ray bone age assessment of preschool children.

BMC pediatrics
BACKGROUND: With the rapid development of deep learning algorithms and the rapid improvement of computer hardware in the past few years, AI-assisted diagnosis software for bone age has achieved good diagnostic performance. The purpose of this study w...

Collectively encoding protein properties enriches protein language models.

BMC bioinformatics
Pre-trained natural language processing models on a large natural language corpus can naturally transfer learned knowledge to protein domains by fine-tuning specific in-domain tasks. However, few studies focused on enriching such protein language mod...

Multiple instance neural networks based on sparse attention for cancer detection using T-cell receptor sequences.

BMC bioinformatics
Early detection of cancers has been much explored due to its paramount importance in biomedical fields. Among different types of data used to answer this biological question, studies based on T cell receptors (TCRs) are under recent spotlight due to ...

Systematic tissue annotations of genomics samples by modeling unstructured metadata.

Nature communications
There are currently >1.3 million human -omics samples that are publicly available. This valuable resource remains acutely underused because discovering particular samples from this ever-growing data collection remains a significant challenge. The maj...