AIMC Topic: Humans

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Automated transtibial prosthesis alignment: A systematic review.

Artificial intelligence in medicine
This comprehensive systematic review critically analyzes the current progress and challenges in automating transtibial prosthesis alignment. The manual identification of alignment changes in prostheses has been found to lack reliability, necessitatin...

PhosBERT: A self-supervised learning model for identifying phosphorylation sites in SARS-CoV-2-infected human cells.

Methods (San Diego, Calif.)
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a single-stranded RNA virus, which mainly causes respiratory and enteric diseases and is responsible for the outbreak of coronavirus disease 19 (COVID-19). Numerous studies have demonstr...

Separating group- and individual-level brain signatures in the newborn functional connectome: A deep learning approach.

NeuroImage
Recent studies indicate that differences in cognition among individuals may be partially attributed to unique brain wiring patterns. While functional connectivity (FC)-based fingerprinting has demonstrated high accuracy in identifying adults, early s...

Osteoinductive biomaterials: Machine learning for prediction and interpretation.

Acta biomaterialia
Biomaterials with osteoinductivity are widely used for bone defect repair due to their unique structures and functions. Machine learning (ML) is pivotal in analyzing osteoinductivity and accelerating new material design. However, challenges include c...

Skeleton-guided multi-scale dual-coordinate attention aggregation network for retinal blood vessel segmentation.

Computers in biology and medicine
Deep learning plays a pivotal role in retinal blood vessel segmentation for medical diagnosis. Despite their significant efficacy, these techniques face two major challenges. Firstly, they often neglect the severe class imbalance in fundus images, wh...

Depth estimation from monocular endoscopy using simulation and image transfer approach.

Computers in biology and medicine
Obtaining accurate distance or depth information in endoscopy is crucial for the effective utilization of navigation systems. However, due to space constraints, incorporating depth cameras into endoscopic systems is often impractical. Our goal is to ...

XDL-ESI: Electrophysiological Sources Imaging via explainable deep learning framework with validation on simultaneous EEG and iEEG.

NeuroImage
Electroencephalography (EEG) or Magnetoencephalography (MEG) source imaging aims to estimate the underlying activated brain sources to explain the observed EEG/MEG recordings. Solving the inverse problem of EEG/MEG Source Imaging (ESI) is challenging...

Machine learning models for temporally precise lapse prediction in alcohol use disorder.

Journal of psychopathology and clinical science
We developed three machine learning models that predict hour-by-hour probabilities of a future lapse back to alcohol use with increasing temporal precision (i.e., lapses in the next week, next day, and next hour). Model features were based on raw sco...

Artificial Intelligence-Assisted Speech Therapy for /ɹ/: A Single-Case Experimental Study.

American journal of speech-language pathology
PURPOSE: This feasibility trial describes changes in rhotic production in residual speech sound disorder following ten 40-min sessions including artificial intelligence (AI)-assisted motor-based intervention with ChainingAI, a version of Speech Motor...

Accuracy of Speech Sound Analysis: Comparison of an Automatic Artificial Intelligence Algorithm With Clinician Assessment.

Journal of speech, language, and hearing research : JSLHR
PURPOSE: Automatic speech analysis (ASA) and automatic speech recognition systems are increasingly being used in the treatment of speech sound disorders (SSDs). When utilized as a home practice tool or in the absence of the clinician, the ASA system ...