Results 191 to 200 of about 50,658,620 (345)
Automatic analysis of speech representations to assess psychological distress. [PDF]
Fernández-Velasco S +4 more
europepmc +1 more source
Automatic analysis of cochlear response using electrocochleography signals during cochlear implant surgery. [PDF]
Wijewickrema S +4 more
europepmc +1 more source
Interstitial lung disease (ILD) is a significant cause of morbidity and mortality in patients with inflammatory rheumatic disorders (IRDs). High‐resolution computed tomography (HRCT) is widely considered the gold standard for the noninvasive assessment of ILD; however, its interpretation is constrained by substantial interobserver variability and the ...
Alexander Pfeil +7 more
wiley +1 more source
The Tool for Automatic Analysis of Decoding Ambiguity (TAADA). [PDF]
Crossley S, Choi JS, Tang K, Cutting L.
europepmc +1 more source
CTGNet: Automatic Analysis of Fetal Heart Rate from Cardiotocograph Using Artificial Intelligence. [PDF]
Zhong M +10 more
europepmc +1 more source
Automated Hand Flexor Tendon–Thickness Measurement in Systemic Sclerosis
Objective Systemic sclerosis (SSc) can affect flexor tendons, contributing to hand function problems and reduced quality of life. Tendon changes are currently assessed with ultrasonography and measured manually, a time‐consuming process prone to interobserver variability.
Mark Greveling +4 more
wiley +1 more source
Automatic analysis of eyelid movement in de-novo Parkinson's disease. [PDF]
Kälble L +5 more
europepmc +1 more source
Objective The aim of this study was to characterize the prevalence and correlates of dietary supplement use, identify commonly used supplements, and evaluate potential supplement–medication interactions among adults with systemic lupus erythematosus (SLE).
Sarah L. Patterson +7 more
wiley +1 more source
Nerve Echogenicity in Polyneuropathies of Various Etiologies-Results of a Retrospective Semi-Automatic Analysis of High-Resolution Ultrasound Images. [PDF]
Erdmann A +6 more
europepmc +1 more source
dynoGP: Deep Gaussian Processes for Dynamic System Identification
This work introduces a novel class of deep models for system identification, dynamical deep Gaussian processes, which combine the strengths of data‐driven methods, such as those based on neural network architectures, with the ability to output a probability distribution for uncertainty representation.
Alessio Benavoli +3 more
wiley +1 more source

