Purpose To evaluate the accuracy of computed tomography (CT) for diagnosis of internal hernia (IH) in patients who have undergone laparoscopic Roux-en-Y gastric bypass and to develop decision tree models to optimize diagnostic accuracy. Materials and Methods This was a retrospective, ethics-approved study of patients who had undergone laparoscopic Roux-en-Y gastric bypass with surgically confirmed IH (n = 76) and without IH (n = 78). Two radiologists independently reviewed each examination for the following previously established CT signs of IH: mesenteric swirl, small-bowel obstruction (SBO), mushroom sign, clustered loops, hurricane eye, small bowel behind the superior mesenteric artery, and right-sided anastomosis. Radiologists also evaluated images for two new signs, superior mesenteric vein (SMV) "beaking" and "criss-cross" of the mesenteric vessels. Overall impressions for diagnosis of IH were recorded. Diagnostic accuracy and interobserver agreement were calculated, and multivariate recursive partitioning was performed to evaluate various decision tree models by using the CT signs. Results Accuracy and interobserver agreement regarding the nine CT signs of IH showed considerable variation. The best signs were mesenteric swirl (sensitivity and specificity, 86%-89% and 86%-90%, respectively; κ = 0.74) and SMV beaking (sensitivity and specificity, 80%-88% and 94%-95%, respectively; κ = 0.83). Overall reader impression yielded the highest sensitivity and specificity (96%-99% and 90%-99%, respectively; κ = 0.79). The decision tree model with the highest overall accuracy and sensitivity included mesenteric swirl and SBO, with a diagnostic odds ratio of 154 (95% confidence interval [CI]: 146, 161), sensitivity of 96% (95% CI: 87%, 99%), and specificity of 87% (95% CI: 75%, 93%). The decision tree with the highest specificity included SMV beaking and SBO, with a diagnostic odds ratio of 105 (95% CI: 101, 109), sensitivity of 90% (95% CI: 79%, 95%), and specificity of 92% (95% CI: 83%, 97%). Conclusion The decision tree with the highest accuracy and sensitivity for diagnosis of IH included mesenteric swirl and SBO, the model with the highest specificity included SMV beaking and SBO, and the remaining signs showed lower accuracy and/or poor to fair interobserver agreement. Overall reader impression yielded the highest accuracy for diagnosis of IH, likely because alternate diagnoses not incorporated in the models were considered. RSNA, 2016 Online supplemental material is available for this article.
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http://dx.doi.org/10.1148/radiol.2016160956 | DOI Listing |
Vet Med Sci
January 2025
Andırın Vocational School, Department of Computer Technologies, Kahramanmaraş Sütçü İmam University, Kahramanmaraş, Türkiye.
Prediction of body weight (BW) using biometric measurements is an important tool especially for animal welfare and automatic phenotyping tools that needs mathematical models. In this study, it was aimed to predict the BW using body length (BL), chest girth (CG) and width of the waist (WW) for rabbits of the maternal form of Hyla NG. The standard rabbit-raising practices were applied for the animals.
View Article and Find Full Text PDFHealthcare (Basel)
December 2024
Department of Computer Science, School of Arts, Humanities and Social Sciences, University of Roehampton, London SW15 5PH, UK.
: Diabetes is a metabolic disorder characterized by increased blood sugar levels. Early detection of diabetes could help individuals to manage and delay the progression of this disorder effectively. Machine learning (ML) methods are important in forecasting the progression and diagnosis of different medical problems with better accuracy.
View Article and Find Full Text PDFActa Otolaryngol
January 2025
Department of Otorhinolaryngology Head and Neck Surgery, Tianjin First Central Hospital, Tianjin, China.
Background: The early diagnosis of glottic laryngeal cancer is the key to successful treatment, and machine learning (ML) combined with narrow-band imaging (NBI) laryngoscopy provides a new idea for the early diagnosis of glottic laryngeal cancer.
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BMC Med Inform Decis Mak
January 2025
Department of Pediatrics, School of Medicine, Ekbatan Hospital, Hamadan University of Medical Sciences, Hamadan, Iran.
Background: Urinary tract infection (UTI) is a frequent health-threatening condition. Early reliable diagnosis of UTI helps to prevent misuse or overuse of antibiotics and hence prevent antibiotic resistance. The gold standard for UTI diagnosis is urine culture which is a time-consuming and also an error prone method.
View Article and Find Full Text PDFCrit Care
January 2025
Department of Pediatric, West China Second University Hospital, Sichuan University, Chengdu, China.
Background: Patients supported by extracorporeal membrane oxygenation (ECMO) are at a high risk of brain injury, contributing to significant morbidity and mortality. This study aimed to employ machine learning (ML) techniques to predict brain injury in pediatric patients ECMO and identify key variables for future research.
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