AIMC Topic: Humans

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Improving accuracy of early dental carious lesions detection using deep learning-based automated method.

Clinical oral investigations
OBJECTIVE: To investigate the effectiveness of a convolutional neural network (CNN) in detecting healthy teeth and early carious lesions on occlusal surfaces and to assess the applicability of this deep learning algorithm as an auxiliary aid.

Multifunctional, Self-Adhesive MXene-Based Hydrogel Flexible Strain Sensors for Hand-Written Digit Recognition with Assistance of Deep Learning.

Langmuir : the ACS journal of surfaces and colloids
The conductive hydrogel as a flexible sensor not only has certain mechanical flexibility but also can be used in the field of human health detection and human-computer interaction. Herein, by introduction of tannic acid (TA) with MXene into the polya...

The Association of Ischemia Type and Duration with Acute Kidney Injury after Robot-Assisted Partial Nephrectomy.

Current oncology (Toronto, Ont.)
BACKGROUND: Acute kidney injury (AKI) after robot-assisted partial nephrectomy (RAPN) is a robust surrogate for chronic kidney disease. The objective of this study was to evaluate the association of ischemia type and duration during RAPN with postope...

Employing Deep-Learning Approach for the Early Detection of Mild Cognitive Impairment Transitions through the Analysis of Digital Biomarkers.

Sensors (Basel, Switzerland)
Mild cognitive impairment (MCI) is the precursor to the advanced stage of Alzheimer's disease (AD), and it is important to detect the transition to the MCI condition as early as possible. Trends in daily routines/activities provide a measurement of c...

"A net for everyone": fully personalized and unsupervised neural networks trained with longitudinal data from a single patient.

BMC medical imaging
BACKGROUND: With the rise in importance of personalized medicine and deep learning, we combine the two to create personalized neural networks. The aim of the study is to show a proof of concept that data from just one patient can be used to train dee...

MultiCOVID: a multi modal deep learning approach for COVID-19 diagnosis.

Scientific reports
The rapid spread of the severe acute respiratory syndrome coronavirus 2 led to a global overextension of healthcare. Both Chest X-rays (CXR) and blood test have been demonstrated to have predictive value on Coronavirus Disease 2019 (COVID-19) diagnos...

Principles of Photocatalysts and Their Different Applications: A Review.

Topics in current chemistry (Cham)
Human existence and societal growth are both dependent on the availability of clean and fresh water. Photocatalysis is a type of artificial photosynthesis that uses environmentally friendly, long-lasting materials to address energy and environmental ...

Applying machine-learning to rapidly analyze large qualitative text datasets to inform the COVID-19 pandemic response: comparing human and machine-assisted topic analysis techniques.

Frontiers in public health
INTRODUCTION: Machine-assisted topic analysis (MATA) uses artificial intelligence methods to help qualitative researchers analyze large datasets. This is useful for researchers to rapidly update healthcare interventions during changing healthcare con...

A machine learning approach to identify important variables for distinguishing between fallers and non-fallers in older women.

PloS one
Falls are a significant ongoing public health concern for older adults. At present, few studies have concurrently explored the influence of multiple measures when seeking to determine which variables are most predictive of fall risks. As such, this c...

Learning Skill Training Schedules From Domain Experts for a Multi-Patient Multi-Robot Rehabilitation Gym.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
A robotic gym with multiple rehabilitation robots allows multiple patients to exercise simultaneously under the supervision of a single therapist. The multi-patient training outcome can potentially be improved by dynamically assigning patients to rob...