AIMC Topic: Retrospective Studies

Clear Filters Showing 3661 to 3670 of 9989 articles

Evaluation of a Deep Learning Reconstruction for High-Quality T2-Weighted Breast Magnetic Resonance Imaging.

Tomography (Ann Arbor, Mich.)
Deep learning (DL) reconstruction techniques to improve MR image quality are becoming commercially available with the hope that they will be applicable to multiple imaging application sites and acquisition protocols. However, before clinical implemen...

Deep Learning-Based Interpretable AI for Prostate T2W MRI Quality Evaluation.

Academic radiology
RATIONALE AND OBJECTIVES: Prostate MRI quality is essential in guiding prostate biopsies. However, assessment of MRI quality is subjective with variation. Quality degradation sources exert varying impacts based on the sequence under consideration, su...

A Real-Time Automated Machine Learning Algorithm for Predicting Mortality in Trauma Patients: Survey Says it's Ready for Prime-Time.

The American surgeon
BACKGROUND: Though artificial intelligence ("AI") has been increasingly applied to patient care, many of these predictive models are retrospective and not readily available for real-time decision-making. This survey-based study aims to evaluate imple...

Development and internal validation of a nomogram predicting 3-year chronic kidney disease upstaging following robot-assisted partial nephrectomy.

International urology and nephrology
PURPOSE: Aim of the present study was to develop and validate a nomogram to accurately predict the risk of chronic kidney disease (CKD) upstaging at 3 years in patients undergoing robot-assisted partial nephrectomy (RAPN).

Deep learning model shows promise for detecting and grading sesamoiditis in horse radiographs.

American journal of veterinary research
OBJECTIVE: The objective of this study was to develop a robust machine-learning approach for efficient detection and grading of sesamoiditis in horses using radiographs, specifically in data-limited conditions.

Development of a novel artificial intelligence algorithm to detect pulmonary nodules on chest radiography.

Fukushima journal of medical science
BACKGROUND: In this study, we aimed to develop a novel artificial intelligence (AI) algorithm to support pulmonary nodule detection, which will enable physicians to efficiently interpret chest radiographs for lung cancer diagnosis.

Deep learning based on susceptibility-weighted MR sequence for detecting cerebral microbleeds and classifying cerebral small vessel disease.

Biomedical engineering online
BACKGROUND: Cerebral microbleeds (CMBs) serve as neuroimaging biomarkers to assess risk of intracerebral hemorrhage and diagnose cerebral small vessel disease (CSVD). Therefore, detecting CMBs can evaluate the risk of intracerebral hemorrhage and use...

A Comparison of Endoscope-Assisted and Open Frontoorbital Distraction for the Treatment of Unicoronal Craniosynostosis.

Plastic and reconstructive surgery
BACKGROUND: Frontoorbital distraction osteogenesis (FODO) is an established surgical technique for patients with unicoronal craniosynostosis. The authors' institution has used an endoscope-assisted technique (endo-FODO) in recent years to decrease cu...

Using Machine Learning to Select Breast Implant Volume.

Plastic and reconstructive surgery
BACKGROUND: In breast augmentation surgery, selection of the appropriate breast implant size is a crucial step that can greatly affect patient satisfaction and the outcome of the procedure. However, this decision is often based on the subjective judg...