Epidemiologic Trends and Diagnostic Evaluation of Fecal Incontinence.

Gastroenterol Hepatol (N Y)

Dr Sharma is an associate professor of medicine and Dr Rao is a professor of medicine and the J. Harold Harrison, MD, Distinguished University Chair in Gastroenterology in the Division of Gastroenterology and Hepatology in the Medical College of Georgia at Augusta University in Augusta, Georgia.

Published: June 2020

Fecal incontinence (FI) is a prevalent condition that occurs in up to 15% of the Western population and significantly impairs quality of life. The current understanding of the epidemiology of FI is shifting because of an increasing recognition of FI in men, better appreciation for the impact of changing obstetric practices on FI in women, and comprehension of the effect of modifiable risk factors on the development of FI over time. The pathophysiology of FI is complex and multifactorial, which necessitates the use of multiple diagnostic tests, including tests of anorectal sensorimotor function, peripheral nerve function, and anatomic structure. Translumbosacral anorectal magnetic stimulation is an emerging noninvasive diagnostic test for assessing lumbosacral neuropathy. This article is not intended as a comprehensive recitation of the literature, but rather focuses on recent developments in the understanding of the epidemiology of FI, as well as on the diagnostic evaluation of this condition. This article aims to increase awareness of FI and to outline an initial diagnostic approach to affected patients.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8132710PMC

Publication Analysis

Top Keywords

diagnostic evaluation
8
fecal incontinence
8
understanding epidemiology
8
diagnostic
5
epidemiologic trends
4
trends diagnostic
4
evaluation fecal
4
incontinence fecal
4
incontinence prevalent
4
prevalent condition
4

Similar Publications

Validating the Accuracy of Parkinson's Disease Clinical Diagnosis: A UK Brain Bank Case-Control Study.

Ann Neurol

January 2025

Research Unit of Neurology, Neurophysiology and Neurobiology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Rome, Italy.

Objective: Despite diagnostic criteria refinements, Parkinson's disease (PD) clinical diagnosis still suffers from a not satisfying accuracy, with the post-mortem examination as the gold standard for diagnosis. Seminal clinicopathological series highlighted that a relevant number of patients alive-diagnosed with idiopathic PD have an alternative post-mortem diagnosis. We evaluated the diagnostic accuracy of PD comparing the in-vivo clinical diagnosis with the post-mortem diagnosis performed through the pathological examination in 2 groups.

View Article and Find Full Text PDF

Background: Urine neutrophil gelatinase-associated lipocalin (uNGAL) is a biomarker for the early diagnosis of AKI.

Objectives: To evaluate uNGAL in dogs with non-associative immune mediated hemolytic anemia (IMHA) and to evaluate whether uNGAL correlates with disease severity markers, negative prognostic indicators and outcome.

Animals: Twenty-two dogs with non-associative IMHA and 14 healthy dogs.

View Article and Find Full Text PDF

Magnetic resonance imaging (MRI) is frequently used to monitor disease progression in multiple sclerosis (MS). This study aims to systematically evaluate the correlation between MRI measures and histopathological changes, including demyelination, axonal loss, and gliosis, in the central nervous system of MS patients. We systematically reviewed post-mortem histological studies evaluating myelin density, axonal loss, and gliosis using quantitative imaging in MS.

View Article and Find Full Text PDF

In Vivo Confocal Microscopy for Automated Detection of Meibomian Gland Dysfunction: A Study Based on Deep Convolutional Neural Networks.

J Imaging Inform Med

January 2025

Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, National Clinical Research Center for Eye Disease, Shanghai, 200080, China.

The objectives of this study are to construct a deep convolutional neural network (DCNN) model to diagnose and classify meibomian gland dysfunction (MGD) based on the in vivo confocal microscope (IVCM) images and to evaluate the performance of the DCNN model and its auxiliary significance for clinical diagnosis and treatment. We extracted 6643 IVCM images from the three hospitals' IVCM database as the training set for the DCNN model and 1661 IVCM images from the other two hospitals' IVCM database as the test set to examine the performance of the model. Construction of the DCNN model was performed using DenseNet-169.

View Article and Find Full Text PDF

Systematic Review of Hybrid Vision Transformer Architectures for Radiological Image Analysis.

J Imaging Inform Med

January 2025

School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA.

Vision transformer (ViT)and convolutional neural networks (CNNs) each possess distinct strengths in medical imaging: ViT excels in capturing long-range dependencies through self-attention, while CNNs are adept at extracting local features via spatial convolution filters. While ViT may struggle with capturing detailed local spatial information, critical for tasks like anomaly detection in medical imaging, shallow CNNs often fail to effectively abstract global context. This study aims to explore and evaluate hybrid architectures that integrate ViT and CNN to leverage their complementary strengths for enhanced performance in medical vision tasks, such as segmentation, classification, reconstruction, and prediction.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!