AIMC Topic: Humans

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Integration of Virtual Reality in the Control System of an Innovative Medical Robot for Single-Incision Laparoscopic Surgery.

Sensors (Basel, Switzerland)
In recent years, there has been an expansion in the development of simulators that use virtual reality (VR) as a learning tool. In surgery where robots are used, VR serves as a revolutionary technology to help medical doctors train in using these rob...

Electrocardiogram-based deep learning algorithm for the screening of obstructive coronary artery disease.

BMC cardiovascular disorders
BACKGROUND: Information on electrocardiogram (ECG) has not been quantified in obstructive coronary artery disease (ObCAD), despite the deep learning (DL) algorithm being proposed as an effective diagnostic tool for acute myocardial infarction (AMI). ...

Preserving privacy in surgical video analysis using a deep learning classifier to identify out-of-body scenes in endoscopic videos.

Scientific reports
Surgical video analysis facilitates education and research. However, video recordings of endoscopic surgeries can contain privacy-sensitive information, especially if the endoscopic camera is moved out of the body of patients and out-of-body scenes a...

Brain-optimized deep neural network models of human visual areas learn non-hierarchical representations.

Nature communications
Deep neural networks (DNNs) optimized for visual tasks learn representations that align layer depth with the hierarchy of visual areas in the primate brain. One interpretation of this finding is that hierarchical representations are necessary to accu...

CONFIDENT-trial protocol: a pragmatic template for clinical implementation of artificial intelligence assistance in pathology.

BMJ open
INTRODUCTION: Artificial intelligence (AI) has been on the rise in the field of pathology. Despite promising results in retrospective studies, and several CE-IVD certified algorithms on the market, prospective clinical implementation studies of AI ha...

Deep learning for predicting future lesion emergence in high-risk breast MRI screening: a feasibility study.

European radiology experimental
BACKGROUND: International societies have issued guidelines for high-risk breast cancer (BC) screening, recommending contrast-enhanced magnetic resonance imaging (CE-MRI) of the breast as a supplemental diagnostic tool. In our study, we tested the app...

Self-helped detection of obstructive sleep apnea based on automated facial recognition and machine learning.

Sleep & breathing = Schlaf & Atmung
PURPOSE: The diagnosis of obstructive sleep apnea (OSA) relies on time-consuming and complicated procedures which are not always readily available and may delay diagnosis. With the widespread use of artificial intelligence, we presumed that the combi...

A deep-learning model using enhanced chest CT images to predict PD-L1 expression in non-small-cell lung cancer patients.

Clinical radiology
AIM: To develop a deep-learning model using contrast-enhanced chest computed tomography (CT) images to predict programmed death-ligand 1 (PD-L1) expression in patients with non-small-cell lung cancer (NSCLC).

Learning Curves in Establishing a New Minimally Invasive Pancreas Program.

The American surgeon
INTRODUCTION: Robotic pancreaticoduodenectomy (rPD) is a complex operation with a reported learning curve of 80 cases. Two recent graduates of a formal robotic complex general surgical oncology training program have been performing rPD at our institu...

Comparison of surgical outcomes between robot-assisted laparoscopic hysterectomy and conventional total laparoscopic hysterectomy in gynecologic benign disease: a single-center cohort study.

Journal of robotic surgery
We compared the surgical outcomes of robot-assisted laparoscopic hysterectomy (RAH) and total laparoscopic hysterectomy (TLH). This single-center cohort study compared 139 RAH cases from January, 2017 to September, 2021 and 291 TLH cases between Janu...