Browsing by Subject "Sensitivity and Specificity"

Now showing 1 - 4 of 4
  • Thumbnail Image
    Item
    Clinical Ultrasound Is Safe and Highly Specific for Acute Appendicitis in Moderate to High Pre-test Probability Patients
    (eScholarship, 2018-05) Corson-Knowles, Daniel; Russell, Frances M.; Emergency Medicine, School of Medicine
    Introduction: Clinical ultrasound (CUS) is highly specific for the diagnosis of acute appendicitis but is operator-dependent. The goal of this study was to determine if a heterogeneous group of emergency physicians (EP) could diagnose acute appendicitis on CUS in patients with a moderate to high pre-test probability. Methods: This was a prospective, observational study of a convenience sample of adult and pediatric patients with suspected appendicitis. Sonographers received a structured, 20-minute CUS training on appendicitis prior to patient enrollment. The presence of a dilated (>6 mm diameter), non-compressible, blind-ending tubular structure was considered a positive study. Non-visualization or indeterminate studies were considered negative. We collected pre-test probability of acute appendicitis based on a 10-point visual analog scale (moderate to high was defined as >3), and confidence in CUS interpretation. The primary objective was measured by comparing CUS findings to surgical pathology and one week follow-up. Results: We enrolled 105 patients; 76 had moderate to high pre-test probability. Of these, 24 were children. The rate of appendicitis was 36.8% in those with moderate to high pre-test probability. CUS were recorded by 33 different EPs. The sensitivity, specificity, and positive and negative likelihood ratios of EP-performed CUS in patients with moderate to high pre-test probability were 42.8% (95% confidence interval [CI] [25-62.5%]), 97.9% (95% CI [87.5-99.8%]), 20.7 (95% CI [2.8-149.9]) and 0.58 (95% CI [0.42-0.8]), respectively. The 16 false negative scans were all interpreted as indeterminate. There was one false positive CUS diagnosis; however, the sonographer reported low confidence of 2/10. Conclusion: A heterogeneous group of EP sonographers can safely identify acute appendicitis with high specificity in patients with moderate to high pre-test probability. This data adds support for surgical consultation without further imaging beyond CUS in the appropriate clinical setting.
  • Thumbnail Image
    Item
    Human connectome module pattern detection using a new multi-graph MinMax cut model
    (Springer, 2014) Wang, De; Wang, Yang; Nie, Feiping; Cai, Weidong; Saykin, Andrew J.; Shen, Li; Huang, Heng; Department of Radiology and Imaging Sciences, IU School of Medicine
    Many recent scientific efforts have been devoted to constructing the human connectome using Diffusion Tensor Imaging (DTI) data for understanding the large-scale brain networks that underlie higher-level cognition in human. However, suitable computational network analysis tools are still lacking in human connectome research. To address this problem, we propose a novel multi-graph min-max cut model to detect the consistent network modules from the brain connectivity networks of all studied subjects. A new multi-graph MinMax cut model is introduced to solve this challenging computational neuroscience problem and the efficient optimization algorithm is derived. In the identified connectome module patterns, each network module shows similar connectivity patterns in all subjects, which potentially associate to specific brain functions shared by all subjects. We validate our method by analyzing the weighted fiber connectivity networks. The promising empirical results demonstrate the effectiveness of our method.
  • Thumbnail Image
    Item
    Identifying Associations Between Brain Imaging Phenotypes and Genetic Factors via A Novel Structured SCCA Approach
    (Springer, 2017-06) Du, Lei; Zhang, Tuo; Liu, Kefei; Yan, Jingwen; Yao, Xiaohui; Risacher, Shannon L.; Saykin, Andrew J.; Han, Junwei; Guo, Lei; Shen, Li; Alzheimer's Disease Neuroimaging Initiative; Radiology and Imaging Sciences, School of Medicine
    Brain imaging genetics attracts more and more attention since it can reveal associations between genetic factors and the structures or functions of human brain. Sparse canonical correlation analysis (SCCA) is a powerful bi-multivariate association identification technique in imaging genetics. There have been many SCCA methods which could capture different types of structured imaging genetic relationships. These methods either use the group lasso to recover the group structure, or employ the graph/network guided fused lasso to find out the network structure. However, the group lasso methods have limitation in generalization because of the incomplete or unavailable prior knowledge in real world. The graph/network guided methods are sensitive to the sign of the sample correlation which may be incorrectly estimated. We introduce a new SCCA model using a novel graph guided pairwise group lasso penalty, and propose an efficient optimization algorithm. The proposed method has a strong upper bound for the grouping effect for both positively and negatively correlated variables. We show that our method performs better than or equally to two state-of-the-art SCCA methods on both synthetic and real neuroimaging genetics data. In particular, our method identifies stronger canonical correlations and captures better canonical loading profiles, showing its promise for revealing biologically meaningful imaging genetic associations.
  • Thumbnail Image
    Item
    A novel structure-aware sparse learning algorithm for brain imaging genetics
    (Springer, 2014) Du, Lei; Yan, Jingwen; Kim, Sungeun; Risacher, Shannon L.; Huang, Heng; Inlow, Mark; Moore, Jason H.; Saykin, Andrew J.; Shen, Li; Department of Radiology and Imaging Sciences, IU School of Medicine
    Brain imaging genetics is an emergent research field where the association between genetic variations such as single nucleotide polymorphisms (SNPs) and neuroimaging quantitative traits (QTs) is evaluated. Sparse canonical correlation analysis (SCCA) is a bi-multivariate analysis method that has the potential to reveal complex multi-SNP-multi-QT associations. Most existing SCCA algorithms are designed using the soft threshold strategy, which assumes that the features in the data are independent from each other. This independence assumption usually does not hold in imaging genetic data, and thus inevitably limits the capability of yielding optimal solutions. We propose a novel structure-aware SCCA (denoted as S2CCA) algorithm to not only eliminate the independence assumption for the input data, but also incorporate group-like structure in the model. Empirical comparison with a widely used SCCA implementation, on both simulated and real imaging genetic data, demonstrated that S2CCA could yield improved prediction performance and biologically meaningful findings.