AIMC Topic: Retrospective Studies

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Deep Learning to Predict Neonatal and Infant Brain Age from Myelination on Brain MRI Scans.

Radiology
Background Assessment of appropriate brain myelination on T1- and T2-weighted MRI scans is based on gestationally corrected age (GCA) and requires subjective visual inspection of the brain with knowledge of normal myelination milestones. Purpose To d...

Simplified Transfer Learning for Chest Radiography Models Using Less Data.

Radiology
Background Developing deep learning models for radiology requires large data sets and substantial computational resources. Data set size limitations can be further exacerbated by distribution shifts, such as rapid changes in patient populations and s...

Convolutional Neural Networks in Spinal Magnetic Resonance Imaging: A Systematic Review.

World neurosurgery
OBJECTIVE: Convolutional neural networks (CNNs) are being increasingly used in the medical field, especially for image recognition in high-resolution, large-volume data sets. The study represents the current state of research on the application of CN...

A deep learning model for discriminating true progression from pseudoprogression in glioblastoma patients.

Journal of neuro-oncology
INTRODUCTION: Glioblastomas (GBMs) are highly aggressive tumors. A common clinical challenge after standard of care treatment is differentiating tumor progression from treatment-related changes, also known as pseudoprogression (PsP). Usually, PsP res...

An externally validated deep learning model for the accurate segmentation of the lumbar paravertebral muscles.

European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society
PURPOSE: Imaging studies about the relevance of muscles in spinal disorders, and sarcopenia in general, require the segmentation of the muscles in the images which is very labour-intensive if performed manually and poses a practical limit to the numb...

Clinical and oncological outcomes of open partial nephrectomy versus robot assisted partial nephrectomy over 15 years.

Journal of robotic surgery
Partial nephrectomy (PN) is the gold standard surgical treatment for localized kidney cancer. The objective of our study was to compare clinical and perioperative outcomes of open partial nephrectomy (OPN) and robotic-assisted partial nephrectomy (RA...

The impact of disclosure of conflicts of interest in studies comparing robot-assisted and laparoscopic cholecystectomies-a persistent problem.

Surgical endoscopy
INTRODUCTION: Accurate disclosure of conflicts of interest (COI) is critical to interpretation of study results, especially when industry interests are involved. We reviewed published manuscripts comparing robot-assisted cholecystectomy (RAC) and lap...

Identification of early invisible acute ischemic stroke in non-contrast computed tomography using two-stage deep-learning model.

Theranostics
Although non-contrast computed tomography (NCCT) is the recommended examination for the suspected acute ischemic stroke (AIS), it cannot detect significant changes in the early infarction. We aimed to develop a deep-learning model to identify early ...

The Surgical Learning Curve for Biochemical Recurrence After Robot-assisted Radical Prostatectomy.

European urology oncology
BACKGROUND: Improved cancer control with increasing surgical experience-the learning curve-was demonstrated for open and laparoscopic prostatectomy. In a prior single-center study, we found that this might not be the case for robot-assisted radical p...