AIMC Topic: Humans

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Multiparametric mapping in the brain from conventional contrast-weighted images using deep learning.

Magnetic resonance in medicine
PURPOSE: To develop a deep-learning-based method to quantify multiple parameters in the brain from conventional contrast-weighted images.

Robotic hernia repair II. English version : Robotic primary ventral and incisional hernia repair (rv‑TAPP and r‑Rives or r‑TARUP). Video report and results of a series of 118 patients.

Der Chirurg; Zeitschrift fur alle Gebiete der operativen Medizen
Endoscopic management of umbilical and incisional hernias has adapted to the limitations of conventional laparoscopic instruments over the past 30 years. This includes the development of meshes for intraperitoneal placement (intraperitoneal onlay mes...

Scalable quorum-based deep neural networks with adversarial learning for automated lung lobe segmentation in fast helical free-breathing CTs.

International journal of computer assisted radiology and surgery
PURPOSE: Fast helical free-breathing CT (FHFBCT) scans are widely used for 5DCT and 5D Cone Beam imaging protocols. For quantitative analysis of lung physiology and function, it is important to segment the lung lobes in these scans. Since the 5DCT pr...

Application of Artificial Intelligence for Diagnosis and Risk Stratification in NAFLD and NASH: The State of the Art.

Hepatology (Baltimore, Md.)
The diagnosis of nonalcoholic fatty liver disease and associated fibrosis is challenging given the lack of signs, symptoms and nonexistent diagnostic test. Furthermore, follow up and treatment decisions become complicated with a lack of a simple repr...

EEG Channel Correlation Based Model for Emotion Recognition.

Computers in biology and medicine
Emotion recognition using Artificial Intelligence (AI) is a fundamental prerequisite to improve Human-Computer Interaction (HCI). Recognizing emotion from Electroencephalogram (EEG) has been globally accepted in many applications such as intelligent ...

Single cortical neurons as deep artificial neural networks.

Neuron
Utilizing recent advances in machine learning, we introduce a systematic approach to characterize neurons' input/output (I/O) mapping complexity. Deep neural networks (DNNs) were trained to faithfully replicate the I/O function of various biophysical...

A New Deep Learning-Based Methodology for Video Deepfake Detection Using XGBoost.

Sensors (Basel, Switzerland)
Currently, face-swapping deepfake techniques are widely spread, generating a significant number of highly realistic fake videos that threaten the privacy of people and countries. Due to their devastating impacts on the world, distinguishing between r...

Endoscopic Image-Based Skill Assessment in Robot-Assisted Minimally Invasive Surgery.

Sensors (Basel, Switzerland)
Objective skill assessment-based personal performance feedback is a vital part of surgical training. Either kinematic-acquired through surgical robotic systems, mounted sensors on tooltips or wearable sensors-or visual input data can be employed to p...

Recent Advances in Flexible Tactile Sensors for Intelligent Systems.

Sensors (Basel, Switzerland)
Tactile sensors are an important medium for artificial intelligence systems to perceive their external environment. With the rapid development of smart robots, wearable devices, and human-computer interaction interfaces, flexible tactile sensing has ...