AIMC Topic: Retrospective Studies

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Factors influencing warm ischemia time in robot-assisted partial nephrectomy change depending on the surgeon's experience.

World journal of surgical oncology
INTRODUCTION: Warm ischemia time (WIT) is a primary concern for robot-assisted laparoscopic partial nephrectomy (RALPN) patients because longer WIT is significantly associated with postoperative deteriorating kidney function. Tumor complexity, determ...

Comparison of robot-assisted sleeve gastrectomy outcomes in multiple staple line treatment modalities from 2015 to 2019: a 5-year propensity score-adjusted MBSAQIP® analysis.

Surgical endoscopy
BACKGROUND: Robot-assisted sleeve gastrectomy (RSG) is an increasingly common approach to sleeve gastrectomy (SG). Staple line reinforcement (SLR) is well-discussed in laparoscopic SG literature, but not RSG- likely due to the absence of dedicated ro...

Deep Learning to Optimize Candidate Selection for Lung Cancer CT Screening: Advancing the 2021 USPSTF Recommendations.

Radiology
Background A deep learning (DL) model to identify lung cancer screening candidates based on their chest radiographs requires external validation with a recent real-world non-U.S. sample. Purpose To validate the DL model and identify added benefits to...

Improved Productivity Using Deep Learning-assisted Reporting for Lumbar Spine MRI.

Radiology
Background Lumbar spine MRI studies are widely used for back pain assessment. Interpretation involves grading lumbar spinal stenosis, which is repetitive and time consuming. Deep learning (DL) could provide faster and more consistent interpretation. ...

Robot-Assisted Deep Brain Stimulation: High Accuracy and Streamlined Workflow.

Operative neurosurgery (Hagerstown, Md.)
BACKGROUND: A number of stereotactic platforms are available for performing deep brain stimulation (DBS) lead implantation. Robot-assisted stereotaxy has emerged more recently demonstrating comparable accuracy and shorter operating room times compare...

Radiomics-based machine learning models to distinguish between metastatic and healthy bone using lesion-center-based geometric regions of interest.

Scientific reports
Radiomics-based machine learning classifiers have shown potential for detecting bone metastases (BM) and for evaluating BM response to radiotherapy (RT). However, current radiomics models require large datasets of images with expert-segmented 3D regi...

Fully Automatic Knee Joint Segmentation and Quantitative Analysis for Osteoarthritis from Magnetic Resonance (MR) Images Using a Deep Learning Model.

Medical science monitor : international medical journal of experimental and clinical research
BACKGROUND We aimed to develop and evaluate a deep learning-based method for fully automatic segmentation of knee joint MR imaging and quantitative computation of knee osteoarthritis (OA)-related imaging biomarkers. MATERIAL AND METHODS This retrospe...

Automatic segmentation of multitype retinal fluid from optical coherence tomography images using semisupervised deep learning network.

The British journal of ophthalmology
BACKGROUND/AIMS: To develop and validate a deep learning model for automated segmentation of multitype retinal fluid using optical coherence tomography (OCT) images.

A Primer into the Current State of Artificial Intelligence in Gastroenterology.

Journal of gastrointestinal and liver diseases : JGLD
Artificial intelligence (AI) is not a new idea or field of research. However, recent advancements in computing technology as well as increasing worldwide experience in applying AI to various fields have enabled us to hope that applying it to the medi...

Development of lumbar spine MRI referrals vetting models using machine learning and deep learning algorithms: Comparison models vs healthcare professionals.

Radiography (London, England : 1995)
INTRODUCTION: Referrals vetting is a necessary daily task to ensure the appropriateness of radiology referrals. Vetting requires extensive clinical knowledge and may challenge those responsible. This study aims to develop AI models to automate the ve...