AI Medical Compendium Topic:
Infant

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Deep Learning Assisted Neonatal Cry Classification Support Vector Machine Models.

Frontiers in public health
Neonatal infants communicate with us through cries. The infant cry signals have distinct patterns depending on the purpose of the cries. Preprocessing, feature extraction, and feature selection need expert attention and take much effort in audio sign...

Machine learning accurately classifies neural responses to rhythmic speech vs. non-speech from 8-week-old infant EEG.

Brain and language
Currently there are no reliable means of identifying infants at-risk for later language disorders. Infant neural responses to rhythmic stimuli may offer a solution, as neural tracking of rhythm is atypical in children with developmental language diso...

Radiology "forensics": determination of age and sex from chest radiographs using deep learning.

Emergency radiology
PURPOSE: To develop and test the performance of deep convolutional neural networks (DCNNs) for automated classification of age and sex on chest radiographs (CXR).

Hybridized neural networks for non-invasive and continuous mortality risk assessment in neonates.

Computers in biology and medicine
Premature birth is the primary risk factor in neonatal deaths, with the majority of extremely premature babies cared for in neonatal intensive care units (NICUs). Mortality risk prediction in this setting can greatly improve patient outcomes and reso...

Pedestrian attribute recognition using two-branch trainable Gabor wavelets network.

PloS one
Keeping an eye on pedestrians as they navigate through a scene, surveillance cameras are everywhere. With this context, our paper addresses the problem of pedestrian attribute recognition (PAR). This problem entails recognizing attributes such as age...

Using deep learning convolutional neural networks to automatically perform cerebral aqueduct CSF flow analysis.

Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia
Since the development of phase-contrast magnetic resonance imaging (PC-MRI), quantification of cerebrospinal fluid (CSF) flow across the cerebral aqueduct has been utilized for diagnosis of conditions such as normal pressure hydrocephalus (NPH). This...

Deep-Learning-Driven Quantification of Interstitial Fibrosis in Digitized Kidney Biopsies.

The American journal of pathology
Interstitial fibrosis and tubular atrophy (IFTA) on a renal biopsy are strong indicators of disease chronicity and prognosis. Techniques that are typically used for IFTA grading remain manual, leading to variability among pathologists. Accurate IFTA ...

Analysing wideband absorbance immittance in normal and ears with otitis media with effusion using machine learning.

Scientific reports
Wideband Absorbance Immittance (WAI) has been available for more than a decade, however its clinical use still faces the challenges of limited understanding and poor interpretation of WAI results. This study aimed to develop Machine Learning (ML) too...

Novel AI driven approach to classify infant motor functions.

Scientific reports
The past decade has evinced a boom of computer-based approaches to aid movement assessment in early infancy. Increasing interests have been dedicated to develop AI driven approaches to complement the classic Prechtl general movements assessment (GMA)...