Risk Management

Rapid lumbar MRI protocol using 3D imaging and deep learning reconstruction.

BACKGROUND AND PURPOSE: Three-dimensional (3D) imaging of the spine, augmented with AI-enabled image enhancement and denoising, has the potential to reduce imaging times without compromising image quality or diagnostic performance. This work evaluates the time savings afforded by a novel, rapid lumbar spine MRI protocol as well as image quality and diagnostic differences stemming from the use of a...

Evaluation of deep-learning image reconstruction for chest CT examinations at two different dose levels.

AIMS: The aims of the present study were to, for both a full-dose protocol and an ultra-low dose (ULD) protocol, compare the image quality of chest CT examinations reconstructed using TrueFidelity (Standard kernel) with corresponding examinations reconstructed using ASIR-V (Lung kernel) and to evaluate if post-processing using an edge-enhancement filter affects the noise level, spatial resolution ...

Same-Day Discharge After Robot-Assisted Partial Nephrectomy: Is It Worth It?

Robot-assisted partial nephrectomy (RAPN) has traditionally been performed as an inpatient procedure; however, recent studies have suggested the feas...

Artificial Intelligence in Breast X-Ray Imaging.

This topical review is focused on the clinical breast x-ray imaging applications of the rapidly evolving field of artificial intelligence (AI). The ra...

Deep learning based protocol to construct an immune-related gene network of host-pathogen interactions in plants.

Investigating network behavior from host-pathogen interactions is challenging. Here, we present the deep-learning-based protocol to construct an immun...

Zygomatic implant placement using a robot-assisted flapless protocol: proof of concept.

Robotic assistance can help in physically guiding the drilling trajectory during zygomatic implant positioning. A new robot-assisted strategy for a fl...

Reducing Errors Resulting From Commonly Missed Chest Radiography Findings.

Chest radiography (CXR), the most frequently performed imaging examination, is vulnerable to interpretation errors resulting from commonly missed find...

The usability and feasibility validation of the social robot MINI in people with dementia and mild cognitive impairment; a study protocol.

BACKGROUND: Social robots have demonstrated promising outcomes in terms of increasing the social health and well-being of people with dementia and mil...

Protocol for the operation of a breathing and vaping biomimetic robot to delineate real-time inhaled particle profile of electronic cigarettes.

We recently developed a robotic human vaping mimetic real-time particle analyzer (HUMITIPAA) to evaluate the impact of change in chemical constituents...

A Novel Dynamic Bit Rate Analysis Technique for Adaptive Video Streaming over HTTP Support.

Recently, there has been an increase in research interest in the seamless streaming of video on top of Hypertext Transfer Protocol (HTTP) in cellular ...

Protocol to explain graph neural network predictions using an edge-centric Shapley value-based approach.

Here we present EdgeSHAPer, a workflow for explaining graph neural networks by approximating Shapley values using Monte Carlo sampling. In this protoc...

Using artificial intelligence to optimize delivery of weight loss treatment: Protocol for an efficacy and cost-effectiveness trial.

Gold standard behavioral weight loss (BWL) is limited by the availability of expert clinicians and high cost of delivery. The artificial intelligence ...

Unified machine learning protocol for copolymer structure-property predictions.

Structure-property relationships are extremely valuable when predicting the properties of polymers. This protocol demonstrates a step-by-step approach...

Machine learning and ontology in eCoaching for personalized activity level monitoring and recommendation generation.

Leading a sedentary lifestyle may cause numerous health problems. Therefore, passive lifestyle changes should be given priority to avoid severe long-t...

Deep Learning-based calculation of patient size and attenuation surrogates from localizer Image: Toward personalized chest CT protocol optimization.

PURPOSE: Extracting water equivalent diameter (DW), as a good descriptor of patient size, from the CT localizer before the spiral scan not only minimi...

Nondestructive microbial discrimination using single-cell Raman spectra and random forest machine learning algorithm.

Raman microspectroscopy is a powerful tool for obtaining biomolecular information from single microbial cells in a nondestructive manner. Here, we det...

Overcoming challenges of translating deep-learning models for glioblastoma: the ZGBM consortium.

OBJECTIVE: To report imaging protocol and scheduling variance in routine care of glioblastoma patients in order to demonstrate challenges of integrati...

Protocol to identify functional doppelgängers and verify biomedical gene expression data using doppelgangerIdentifier.

Functional doppelgängers (FDs) are independently derived sample pairs that confound machine learning model (ML) performance when assorted across train...

Deep Learning Segmentation and Reconstruction for CT of Chronic Total Coronary Occlusion.

Background CT imaging of chronic total occlusion (CTO) is useful in guiding revascularization, but manual reconstruction and quantification are time c...

Conformal prediction under feedback covariate shift for biomolecular design.

Many applications of machine-learning methods involve an iterative protocol in which data are collected, a model is trained, and then outputs of that ...