AIMC Topic: Retrospective Studies

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A deep learning methodology for the automated detection of end-diastolic frames in intravascular ultrasound images.

The international journal of cardiovascular imaging
Coronary luminal dimensions change during the cardiac cycle. However, contemporary volumetric intravascular ultrasound (IVUS) analysis is performed in non-gated images as existing methods to acquire gated or to retrospectively gate IVUS images have f...

Deep learning-accelerated T2-weighted imaging of the prostate: Reduction of acquisition time and improvement of image quality.

European journal of radiology
PURPOSE: To introduce a novel deep learning (DL) T2-weighted TSE imaging (T2) sequence in prostate MRI and investigate its impact on examination time, image quality, diagnostic confidence, and PI-RADS classification compared to standard T2-weighted T...

A method to screen left ventricular dysfunction through ECG based on convolutional neural network.

Journal of cardiovascular electrophysiology
OBJECTIVE: This study aims to develop an artificial intelligence-based method to screen patients with left ventricular ejection fraction (LVEF) of 50% or lesser using electrocardiogram (ECG) data alone.

Robotic chemotherapy compounding: A multicenter productivity approach.

Journal of oncology pharmacy practice : official publication of the International Society of Oncology Pharmacy Practitioners
INTRODUCTION: The aim of this study is to compare productivity of the KIRO Oncology compounding robot in three hospital pharmacy departments and identify the key factors to predict and optimize automatic compounding time.

Deep Learning Model for Real-Time Prediction of Intradialytic Hypotension.

Clinical journal of the American Society of Nephrology : CJASN
BACKGROUND AND OBJECTIVES: Intradialytic hypotension has high clinical significance. However, predicting it using conventional statistical models may be difficult because several factors have interactive and complex effects on the risk. Herein, we ap...

Deep learning-based metal artefact reduction in PET/CT imaging.

European radiology
OBJECTIVES: The susceptibility of CT imaging to metallic objects gives rise to strong streak artefacts and skewed information about the attenuation medium around the metallic implants. This metal-induced artefact in CT images leads to inaccurate atte...

Radiomic Machine Learning Classifiers in Spine Bone Tumors: A Multi-Software, Multi-Scanner Study.

European journal of radiology
PURPOSE: Spinal lesion differential diagnosis remains challenging even in MRI. Radiomics and machine learning (ML) have proven useful even in absence of a standardized data mining pipeline. We aimed to assess ML diagnostic performance in spinal lesio...

A Machine Learning Prediction Model of Respiratory Failure Within 48 Hours of Patient Admission for COVID-19: Model Development and Validation.

Journal of medical Internet research
BACKGROUND: Predicting early respiratory failure due to COVID-19 can help triage patients to higher levels of care, allocate scarce resources, and reduce morbidity and mortality by appropriately monitoring and treating the patients at greatest risk f...