98%
921
2 minutes
20
Background: Barrett's oesophagus is an established risk factor for developing oesophageal adenocarcinoma. However, Barrett's neoplasia can be subtle and difficult to identify. Acetic acid chromoendoscopy (AAC) is a simple technique that has been demonstrated to highlight neoplastic areas but lesion recognition with AAC remains a challenge, thereby hampering its widespread use.
Objective: To develop and validate a simple classification system to identify Barrett's neoplasia using AAC.
Design: The study was conducted in four phases: phase 1-development of component descriptive criteria; phase 2-development of a classification system; phase 3-validation of the classification system by endoscopists; and phase 4-validation of the classification system by non-endoscopists.
Results: Phases 1 and 2 led to the development of a simplified AAC classification system based on two criteria: focal loss of acetowhitening and surface patterns of Barrett's mucosa. In phase 3, the application of PREDICT (Portsmouth acetic acid classification) by endoscopists improved the sensitivity and negative predictive value (NPV) from 79.3% and 80.2% to 98.1% and 97.4%, respectively (p<0.001). In phase 4, the application of PREDICT by non-endoscopists improved the sensitivity and NPV from 69.6% and 75.5% to 95.9% and 96.0%, respectively (p<0.001).
Conclusion: We developed and validated a classification system known as PREDICT for the diagnosis of Barrett's neoplasia using AAC. The improvement seen in the sensitivity and NPV for detection of Barrett's neoplasia in phase 3 demonstrates the clinical value of PREDICT and the similar improvement seen among non-endoscopists demonstrates the potential for generalisation of PREDICT once proven in real time.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1136/gutjnl-2017-314512 | DOI Listing |
Plast Reconstr Surg
September 2025
Department of Surgery, Federal University of Santa Catarina, Florianópolis, SC, Brazil.
Background: Poor recovery of active glenohumeral external rotation (aGHER) after brachial plexus birth injury (BPBI) is common. Late spinal accessory nerve to infraspinatus motor branch (SAN-IS) transfer has been reported as effective. We investigated its efficacy in children over 4 years with BPBI.
View Article and Find Full Text PDFBull Entomol Res
September 2025
Instituto de Biotecnología y Ecología Aplicada, Universidad Veracruzana, Xalapa, Veracruz, México.
Insect pupae change morphologically (e.g., pigmentation of eyes, wings, setae and legs) during the intrapuparial period.
View Article and Find Full Text PDFJ Ultrasound Med
September 2025
Department of Clinical Analysis, Federal University of Santa Catarina (UFSC), Florianópolis, Brazil.
Objectives: To evaluate the performance of artificial intelligence (AI)-based models in predicting elevated neonatal insulin levels through fetal hepatic echotexture analysis.
Methods: This diagnostic accuracy study analyzed ultrasound images of fetal livers from pregnancies between 37 and 42 weeks, including cases with and without gestational diabetes mellitus (GDM). Images were stored in Digital Imaging and Communications in Medicine (DICOM) format, annotated by experts, and converted to segmented masks after quality checks.
Cell Mol Biol (Noisy-le-grand)
September 2025
Medical Microbiology Department, College of Medicine, Ibn Sina University of Medical and Pharmaceutical Sciences, Baghdad, Iraq.
Pseudomonas aeruginosa is a prominent opportunistic pathogen, especially in burn wound infections, and is often associated with high morbidity and mortality due to its multidrug resistance (MDR) characteristics.This study aimed to evaluate the multidrug resistance profile and perform a molecular phylogenetic analysis of P. aeruginosa isolates recovered from human burn infection sample .
View Article and Find Full Text PDFMed Biol Eng Comput
September 2025
Department of Computer Science, Università degli Studi di Bari Aldo Moro, Bari, Italy.
Fetal standard plane detection is essential in prenatal care, enabling accurate assessment of fetal development and early identification of potential anomalies. Despite significant advancements in machine learning (ML) in this domain, its integration into clinical workflows remains limited-primarily due to the lack of standardized, end-to-end operational frameworks. To address this gap, we introduce FetalMLOps, the first comprehensive MLOps framework specifically designed for fetal ultrasound imaging.
View Article and Find Full Text PDF