AIMC Topic: Retrospective Studies

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Characterizing breast masses using an integrative framework of machine learning and CEUS-based radiomics.

Journal of ultrasound
AIMS: We evaluated the performance of contrast-enhanced ultrasound (CEUS) based on radiomics analysis to distinguish benign from malignant breast masses.

Adopt or Abandon? Surgeon-Specific Trends in Robotic Bariatric Surgery Utilization Between 2010 and 2019.

Journal of laparoendoscopic & advanced surgical techniques. Part A
It is unknown if surgeons are more likely to adopt or abandon robotic techniques given that bariatric procedures are already performed by surgeons with advanced laparoscopic skills. We used a statewide bariatric-specific data registry to evaluate s...

A deep learning radiomics model may help to improve the prediction performance of preoperative grading in meningioma.

Neuroradiology
PURPOSE: This study aimed to investigate the clinical usefulness of the enhanced-T1WI-based deep learning radiomics model (DLRM) in differentiating low- and high-grade meningiomas.

Hospital Ownership, Geographic Region, Patient Age, Comorbidities, and Insurance Status Appear to Influence Patient Selection Robot-Assisted Ureteral Reimplantation for Benign Disease: A Population-Based Analysis.

Journal of endourology
Robot-assisted ureteral reimplantation (RAUR) is a relatively new minimally invasive procedure. As such, research is lacking, and the largest adult cohort studies include fewer than 30 patients. Our aim was to be the first population-based study to ...

Automated grading of enlarged perivascular spaces in clinical imaging data of an acute stroke cohort using an interpretable, 3D deep learning framework.

Scientific reports
Enlarged perivascular spaces (EPVS), specifically in stroke patients, has been shown to strongly correlate with other measures of small vessel disease and cognitive impairment at 1 year follow-up. Typical grading of EPVS is often challenging and time...

Prospectively-validated deep learning model for segmenting swallowing and chewing structures in CT.

Physics in medicine and biology
Delineating swallowing and chewing structures aids in radiotherapy (RT) treatment planning to limit dysphagia, trismus, and speech dysfunction. We aim to develop an accurate and efficient method to automate this process.CT scans of 242 head and neck ...

A Knowledge Distillation Ensemble Framework for Predicting Short- and Long-Term Hospitalization Outcomes From Electronic Health Records Data.

IEEE journal of biomedical and health informatics
The ability to perform accurate prognosis is crucial for proactive clinical decision making, informed resource management and personalised care. Existing outcome prediction models suffer from a low recall of infrequent positive outcomes. We present a...

Electroclinical spectrum of generalized paroxysmal fast activity in adults without epileptic encephalopathy.

Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology
INTRODUCTION: Generalized paroxysmal fast activity (GPFA) is a rare and underreported EEG pattern known to be related to epileptic encephalopathy. We aimed to investigate the electroclinical spectrum of GPFA along with other atypical EEG features in ...

Prediction of post-stroke urinary tract infection risk in immobile patients using machine learning: an observational cohort study.

The Journal of hospital infection
BACKGROUND: Urinary tract infection (UTI) is one of major nosocomial infections significantly affecting the outcomes of immobile stroke patients. Previous studies have identified several risk factors, but it is still challenging to accurately estimat...

Comparing two artificial intelligence software packages for normative brain volumetry in memory clinic imaging.

Neuroradiology
PURPOSE: To compare two artificial intelligence software packages performing normative brain volumetry and explore whether they could differently impact dementia diagnostics in a clinical context.