Purpose: Most studies of colonic polyps rely on visual estimation when regarding polyp size; however, the reliability of a visual estimate is questionable. Our study aims to develop a training model to improve the accuracy of size estimation of colonic polyps in vivo.
Methods: Colon polyps were recorded on 160 video clips during colonoscopy. The size of each polyp was estimated by visual inspection and subsequently measured with a flexible linear measuring probe. The study included a pretest, an intervention, and a posttest. The pretest included 160 video clips, which comprised the visual-estimation portion of the study. The intervention was an educational model consisting of 30 video clips which included a visual-estimation section and a linear-measuring-probe section, designed to help the endoscopists to compare their visual estimate of size with the measured size of the polyps. The posttest included the 160 video clips used in the pretest, presented in random order. Intraobserver agreement and diagnostic accuracy were compared before and after the training session.
Results: Eight beginners and four experienced colonoscopists were enrolled. The overall kappa (kappa) values of intraobserver agreement for pretest and posttest were 0.74 and 0.85 for beginner group as well as 0.83 and 0.88 for experienced group, respectively. The overall diagnostic accuracy improved from 0.52 to 0.78 for beginner group and 0.71 to 0.87 for experienced group (P < 0.05) after education with the training model.
Conclusions: This training model could help endoscopists improve the accuracy of measurement of polyps on colonoscopy in a short period. The durability of learning effect needs further investigation.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1007/s00384-010-0878-9 | DOI Listing |
World Neurosurg
January 2025
Department of Neurosurgery, Namazi Hospital, Shiraz University of Medical Sciences, Shiraz, Iran.
Contact Dermatitis
January 2025
Department of Dermatology, Bispebjerg Hospital, Copenhagen, Denmark.
Background: Hand eczema (HE) is common among hospital cleaners, yet no specific prevention programme exists for this group in Denmark.
Objectives: To evaluate the effectiveness of visual aids (pictograms and video scenes) based on evidence-based skin care and protection recommendations on HE outcomes, focusing on disease severity, point prevalence and skin care and protection knowledge.
Methods: A cluster-randomised controlled trial was conducted in professional cleaners from three Danish hospitals.
JBJS Essent Surg Tech
January 2025
Department of Orthopaedic Surgery, Montefiore Medical Center, Bronx, New York.
Background: The pelvis is one of the most common areas for metastatic bone disease. We recently described the use of a minimally invasive percutaneous screw fixation of metastatic non-periacetabular pelvic lesions, with excellent results.
Description: The procedure can be completed in a standard operating theater without the need for special instruments.
Bioengineering (Basel)
November 2024
School of Mechanical and Electrical Engineering, Sanming University, Sanming 365004, China.
In experimental pain studies involving animals, subjective pain reports are not feasible. Current methods for detecting pain-related behaviors rely on human observation, which is time-consuming and labor-intensive, particularly for lengthy video recordings. Automating the quantification of these behaviors poses substantial challenges.
View Article and Find Full Text PDFBrain Sci
November 2024
Facultad de Ciencias Químicas e Ingeniería, Universidad Autónoma de Baja California, Calzada Universidad 14418, Parque Industrial Internacional, Tijuana 22390, Mexico.
Background: Emotions significantly influence decision-making, social interactions, and medical outcomes. Leveraging emotion recognition through Electroencephalography (EEG) signals offers potential advancements in personalized medicine, adaptive technologies, and mental health diagnostics. This study aimed to evaluate the performance of three neural network architectures-ShallowFBCSPNet, Deep4Net, and EEGNetv4-for emotion classification using the SEED-V dataset.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!