AI Article Synopsis
- The study involved 167 patients who underwent emergency appendectomy to assess three diagnostic scoring systems: Alvarado, Tzanakis, and RIPASA.
- RIPASA demonstrated the highest diagnostic accuracy, especially in centers with less experience in ultrasound diagnostics, outperforming the Tzanakis score.
- While the Alvarado score was easy to use and could accurately confirm appendicitis in some cases, it was not sensitive enough for effective triaging.
Article Abstract
Our prospective observational study, enrolling 167 patients undergoing emergency appendicectomy, served to evaluate the Alvarado, Tzanakis and RIPASA diagnostic scores. The latter was found to be a better scoring system because of its higher diagnostic accuracy. At centres with limited expertise in ultrasonography, RIPASA outperforms the Tzanakis score. Although easy to use, the Alvarado score was useful only to 'rule in' cases with AA with significant accuracy but at the cost of low sensitivity. It cannot be effectively used in triaging.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1177/00494755211030165 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Multiplex Detection and Quantification of Virus Co-Infections Using Label-free Surface-Enhanced Raman Spectroscopy and Deep Learning Algorithms.
ACS Sens
January 2025
Department of Physics and Astronomy, Franklin College of Arts and Sciences, The University of Georgia, Athens, Georgia 30602, United States.
Multiple respiratory viruses can concurrently or sequentially infect the respiratory tract, making their identification crucial for diagnosis, treatment, and disease management. We present a label-free diagnostic platform integrating surface-enhanced Raman scattering (SERS) with deep learning for rapid, quantitative detection of respiratory virus coinfections. Using sensitive silica-coated silver nanorod array substrates, over 1.
View Article and Find Full Text PDFUser and Developer Views on Using AI Technologies to Facilitate the Early Detection of Skin Cancers in Primary Care Settings: Qualitative Semistructured Interview Study.
JMIR Cancer
January 2025
Wolfson Institute of Population Health, Queen Mary University of London, London, United Kingdom.
Background: Skin cancers, including melanoma and keratinocyte cancers, are among the most common cancers worldwide, and their incidence is rising in most populations. Earlier detection of skin cancer leads to better outcomes for patients. Artificial intelligence (AI) technologies have been applied to skin cancer diagnosis, but many technologies lack clinical evidence and/or the appropriate regulatory approvals.
View Article and Find Full Text PDFCould metabolic imaging and artificial intelligence provide a novel path to non-invasive aneuploidy assessments? A certain clinical need.
Reprod Fertil Dev
January 2025
Fertility & Research Centre, Discipline of Women health, School of Clinical Medicine and the Royal Hospital for Women, University of New South Wales, Sydney, NSW, Australia.
Pre-implantation genetic testing for aneuploidy (PGT-A) via embryo biopsy helps in embryo selection by assessing embryo ploidy. However, clinical practice needs to consider the invasive nature of embryo biopsy, potential mosaicism, and inaccurate representation of the entire embryo. This creates a significant clinical need for improved diagnostic practices that do not harm embryos or raise treatment costs.
View Article and Find Full Text PDFThe Role of Serum HMGB-1 Level in Differentiating the Soft-Tissue Infection From Diabetic Foot Osteomyelitis.
J Am Podiatr Med Assoc
January 2025
†Medical Point Gaziantep Hospital, Gaziantep, Turkey.
Background: The incidence of diabetic foot infections is increasing due to the rising number of persons with diabetes and the prolonged life expectancy. It is vital to differentiate soft-tissue infection (STI) from diabetic foot osteomyelitis (DFO), as treatment modalities and durations vary widely, but this can be challenging. We aimed to assess the blood concentration levels of the high mobility group box 1 protein (HMGB-1) in STI and DFO compared to healthy subjects, and to investigate whether this protein could contribute to differentiating STI from DFO.
View Article and Find Full Text PDFThe Added Effect of Artificial Intelligence in CT Assessment of Abdominal Lymphadenopathy.
Lymphology
January 2025
Medical Biophysics Department, Medical Research Institute, Alexandria University, Alexandria, Egypt.
Lymphadenopathy is associated with lymph node abnormal size or consistency due to many causes. We employed the deep convolutional neural network ResNet-34 to detect and classify CT images from patients with abdominal lymphadenopathy and healthy controls. We created a single database containing 1400 source CT images for patients with abdominal lymphadenopathy (n = 700) and healthy controls (n = 700).
View Article and Find Full Text PDFWant 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!