AIMC Topic: Constriction, Pathologic

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Classification and regression of stenosis using an in-vitro pulse wave data set: Dependence on heart rate, waveform and location.

Computers in biology and medicine
BACKGROUND: Data-based approaches promise to use the information in cardiovascular signals to diagnose cardiovascular diseases. Considerable effort has been undertaken in the field of pulse-wave analysis to harness this information. However, the inve...

Automated detection of intracranial artery stenosis and occlusion in magnetic resonance angiography: A preliminary study based on deep learning.

Magnetic resonance imaging
BACKGROUND AND OBJECTIVES: Intracranial atherosclerotic stenosis of a major intracranial artery is the common cause of ischemic stroke. We evaluate the feasibility of using deep learning to automatically detect intracranial arterial steno-occlusive l...

Identification of patients with malignant biliary strictures using a cholangioscopy-based deep learning artificial intelligence (with video).

Gastrointestinal endoscopy
BACKGROUND AND AIMS: Accurately diagnosing malignant biliary strictures (MBSs) as benign or malignant remains challenging. It has been suggested that direct visualization and interpretation of cholangioscopy images provide greater accuracy for strict...

Robot-assisted Laparoscopic Bilateral Ileal Ureter in Duplex Ureter With Strictures After Treatment Failure of Allium Stents.

Urology
BACKGROUND: Ureteral injury and vaginal fistula are common complications after surgical treatment and radiotherapy of gynecological tumor. Ureteral injury in duplex system is more challenging and rarely reported. OBJECTIVE: We report our surgical tec...

Deep learning reconstruction for the evaluation of neuroforaminal stenosis using 1.5T cervical spine MRI: comparison with 3T MRI without deep learning reconstruction.

Neuroradiology
PURPOSE: To compare image quality and interobserver agreement in evaluations of neuroforaminal stenosis between 1.5T cervical spine magnetic resonance imaging (MRI) with deep learning reconstruction (DLR) and 3T MRI without DLR.

External validation of the deep learning system "SpineNet" for grading radiological features of degeneration on MRIs of the lumbar spine.

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
BACKGROUND: Magnetic resonance imaging (MRI) is used to detect degenerative changes of the lumbar spine. SpineNet (SN), a computer vision-based system, performs an automated analysis of degenerative features in MRI scans aiming to provide high accura...

Robotic Ureteral Reconstruction.

The Urologic clinics of North America
It is generally accepted that robotic ureteral reconstruction provides equivalent results to open and laparoscopic approaches while decreasing pain and length of stay. There is a rapid expansion of robotic ureteral reconstructive techniques, platform...

Uretero-enteric stricture outcomes: secondary analysis of a randomised controlled trial comparing open versus robot-assisted radical cystectomy.

BJU international
OBJECTIVES: To analyse the risk of uretero-enteric anastomotic stricture in patients randomised to open (ORC) or robot-assisted radical cystectomy (RARC) with extracorporeal urinary diversion.

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. ...