AIMC Topic: Humans

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Simultaneous Isotropic Omnidirectional Hypersensitive Strain Sensing and Deep Learning-Assisted Direction Recognition in a Biomimetic Stretchable Device.

Advanced materials (Deerfield Beach, Fla.)
Omnidirectional strain sensing and direction recognition ability are features of the human tactile sense, essential to address the intricate and dynamic requirements of real-world applications. Most of the current strain sensors work by converting un...

Automated Euler number of the alveolar capillary network based on deep learning segmentation with verification by stereological methods.

Journal of microscopy
Diseases like bronchopulmonary dysplasia (BPD) affect the development of the pulmonary vasculature, including the alveolar capillary network (ACN). Since pulmonary development is highly dependent on angiogenesis and microvascular maturation, ACN inve...

Protocol for functional screening of CFTR-targeted genetic therapies in patient-derived organoids using DETECTOR deep-learning-based analysis.

STAR protocols
Here, we present a protocol for the rapid functional screening of gene editing and addition strategies in patient-derived organoids using the deep-learning-based tool DETECTOR (detection of targeted editing of cystic fibrosis transmembrane conductanc...

Differential diagnosis of multiple system atrophy with predominant parkinsonism and Parkinson's disease using neural networks (part II).

Journal of the neurological sciences
Neural networks (NNs) possess the capability to learn complex data relationships, recognize inherent patterns by emulating human brain functions, and generate predictions based on novel data. We conducted deep learning utilizing an NN to differentiat...

AI-based non-invasive imaging technologies for early autism spectrum disorder diagnosis: A short review and future directions.

Artificial intelligence in medicine
Autism Spectrum Disorder (ASD) is a neurological condition, with recent statistics from the CDC indicating a rising prevalence of ASD diagnoses among infants and children. This trend emphasizes the critical importance of early detection, as timely di...

Developing an interpretable machine learning model for diagnosing gout using clinical and ultrasound features.

European journal of radiology
OBJECTIVE: To develop a machine learning (ML) model using clinical data and ultrasound features for gout prediction, and apply SHapley Additive exPlanations (SHAP) for model interpretation.

The potential use of deep learning in performing autocorrection of setup errors in patients receiving radiotherapy.

Radiography (London, England : 1995)
INTRODUCTION: Modern radiotherapy practice relies on multiple approaches for verification of patient positioning. All of these techniques require experienced radiotherapists who understand the anatomical landmarks and the limitations of the used veri...

A comparative study of statistical, radiomics, and deep learning feature extraction techniques for medical image classification in optical and radiological modalities.

Computers in biology and medicine
Feature extraction in ML plays a crucial role in transforming raw data into a more meaningful and interpretable representation. In this study, we thoroughly examined a range of feature extraction techniques and assessed their impact on the binary cla...

Application of artificial intelligence to ultrasound imaging for benign gynecological disorders: systematic review.

Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology
OBJECTIVE: Although artificial intelligence (AI) is increasingly being applied to ultrasound imaging in gynecology, efforts to synthesize the available evidence have been inadequate. The aim of this systematic review was to summarize and evaluate the...

Unveiling encephalopathy signatures: A deep learning approach with locality-preserving features and hybrid neural network for EEG analysis.

Neuroscience letters
EEG signals exhibit spatio-temporal characteristics due to the neural activity dispersion in space over the brain and the dynamic temporal patterns of electrical activity in neurons. This study tries to effectively utilize the spatio-temporal nature ...