AIMC Topic: Feasibility Studies

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True single-port cholecystectomy with ICG cholangiography through a single 15-mm trocar using the new surgical platform "symphonX": first human case study with a commercially available device.

Surgical endoscopy
BACKGROUND: Minimally invasive single-port surgery is often associated with large incisions up to 2-3 cm, complicated handling due to the lack of triangulation, and instrument crossing. Aim of this prospective study was to perform true single-port su...

Pulse-Wave-Pattern Classification with a Convolutional Neural Network.

Scientific reports
Owing to the diversity of pulse-wave morphology, pulse-based diagnosis is difficult, especially pulse-wave-pattern classification (PWPC). A powerful method for PWPC is a convolutional neural network (CNN). It outperforms conventional methods in patte...

A comparison of machine learning algorithms for the surveillance of autism spectrum disorder.

PloS one
OBJECTIVE: The Centers for Disease Control and Prevention (CDC) coordinates a labor-intensive process to measure the prevalence of autism spectrum disorder (ASD) among children in the United States. Random forests methods have shown promise in speedi...

Detecting substance-related problems in narrative investigation summaries of child abuse and neglect using text mining and machine learning.

Child abuse & neglect
BACKGROUND: State child welfare agencies collect, store, and manage vast amounts of data. However, they often do not have the right data, or the data is problematic or difficult to inform strategies to improve services and system processes. Considera...

Automatic segmentation of the uterus on MRI using a convolutional neural network.

Computers in biology and medicine
BACKGROUND: This study was performed to evaluate the clinical feasibility of a U-net for fully automatic uterine segmentation on MRI by using images of major uterine disorders.

Automated deep learning design for medical image classification by health-care professionals with no coding experience: a feasibility study.

The Lancet. Digital health
BACKGROUND: Deep learning has the potential to transform health care; however, substantial expertise is required to train such models. We sought to evaluate the utility of automated deep learning software to develop medical image diagnostic classifie...

Range and dose verification in proton therapy using proton-induced positron emitters and recurrent neural networks (RNNs).

Physics in medicine and biology
Online proton range/dose verification based on measurements of proton-induced positron emitters is a promising strategy for quality assurance in proton therapy. Because of the nonlinear correlation between the dose distribution and the activity distr...

Predicting real-time 3D deformation field maps (DFM) based on volumetric cine MRI (VC-MRI) and artificial neural networks for on-board 4D target tracking: a feasibility study.

Physics in medicine and biology
To predict real-time 3D deformation field maps (DFMs) using Volumetric Cine MRI (VC-MRI) and adaptive boosting and multi-layer perceptron neural network (ADMLP-NN) for 4D target tracking. One phase of a prior 4D-MRI is set as the prior phase, MRI. Pr...