Objective: To describe the imaging utilization patterns for the diagnosis of appendicitis among children's hospitals in the United States over the last 10 years (2005-2014).
Methods: All patients with a primary discharge diagnosis of appendicitis included in a large administrative database of 45 pediatric institutions in the United States between 2005 and 2014 were selected. Demographics, imaging utilization, and costs were described.
Results: In all, 96,786 children with appendicitis (59% boys, 41% girls; mean age: 9.9 years) were studied. The average length of stay decreased from 5.0 days in 2005 to 3.4 days in 2014 (P < .01). The percentage of patients undergoing CT increased between 2005 and 2007 from 59.1% to 62.6%, respectively, followed by a decrease from 62.6% to 32.7% in 2014 (r = 0.93). Radiograph utilization decreased from 14.2% in 2005 to 3.6% in 2014 (r = 0.93), and ultrasound and MRI increased from 25% and 0.03% in 2005 to 61% and 1.0% in 2014 (r = 0.97 and 0.64), respectively. The mean total hospital costs increased from $11,700 in 2005 to $16,500 in 2014; imaging costs increased only slightly from $3,205 to $3,259. The imaging fraction of hospital costs decreased from 27.5% to 19.8%.
Conclusion: There has been a significant decrease in utilization of CT and radiographs for the management of appendicitis in children, and ultrasound has continued to increase. Imaging costs have remained stable in comparison to rising hospital costs, generating a drop in the fraction of costs related to imaging.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1016/j.jacr.2016.12.013 | DOI Listing |
Breast Cancer Res
January 2025
Division of Medical Oncology, The Ohio State University Comprehensive Cancer Center, Columbus, OH, USA.
Background: Epidemiological studies associate an increase in breast cancer risk, particularly triple-negative breast cancer (TNBC), with lack of breastfeeding. This is more prevalent in African American women, with significantly lower rate of breastfeeding compared to Caucasian women. Prolonged breastfeeding leads to gradual involution (GI), whereas short-term or lack of breastfeeding leads to abrupt involution (AI) of the breast.
View Article and Find Full Text PDFBMC Cancer
January 2025
Department of Pathology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, Zhejiang, China.
Objective: Rapid on-site evaluation (ROSE) of respiratory cytology specimens is a critical technique for accurate and timely diagnosis of lung cancer. However, in China, limited familiarity with the Diff-Quik staining method and a shortage of trained cytopathologists hamper utilization of ROSE. Therefore, developing an improved deep learning model to assist clinicians in promptly and accurately evaluating Diff-Quik stained cytology samples during ROSE has important clinical value.
View Article and Find Full Text PDFBMC Ophthalmol
January 2025
Department of Retina and Vitreous, Narayana Nethralaya, #121/C, 1st R Block, Chord Road, Rajaji Nagar, Bengaluru, Karnataka, 560010, India.
Background: Accurate localization of premacular hemorrhages (PMHs) is crucial as treatment strategies vary significantly based on whether the hemorrhage resides within the vitreous gel, subhyaloid space, or beneath the internal limiting membrane (ILM). This report outlines the clinical features, diagnostic findings, and treatment outcomes in a patient diagnosed with a PMH secondary to suspected Valsalva retinopathy.
Methods: This is a retrospective interventional case report.
Med Biol Eng Comput
January 2025
Department of Computer Science and Engineering, Shri Shankaracharya Institute of Professional Management and Technology, Raipur, (C.G.), India.
This study presents an advanced methodology for 3D heart reconstruction using a combination of deep learning models and computational techniques, addressing critical challenges in cardiac modeling and segmentation. A multi-dataset approach was employed, including data from the UK Biobank, MICCAI Multi-Modality Whole Heart Segmentation (MM-WHS) challenge, and clinical datasets of congenital heart disease. Preprocessing steps involved segmentation, intensity normalization, and mesh generation, while the reconstruction was performed using a blend of statistical shape modeling (SSM), graph convolutional networks (GCNs), and progressive GANs.
View Article and Find Full Text PDFJ Imaging Inform Med
January 2025
College of Computer, Chongqing University, No. 55 Daxuecheng South Rd, Shapingba, 401331, Chongqing, China.
Convolutional neural networks (CNNs) have become indispensable to medical image diagnosis research, enabling the automated differentiation of diseased images from extensive medical image datasets. Due to their efficacy, these methods raise significant privacy concerns regarding patient images and diagnostic models. To address these issues, some researchers have explored privacy-preserving medical image diagnosis schemes using fully homomorphic encryption (FHE).
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!