China
Africa
Explore chapters and articles related to this topic
A Review of Automatic Cardiac Segmentation using Deep Learning and Deformable Models
Published in Kayvan Najarian, Delaram Kahrobaei, Enrique Domínguez, Reza Soroushmehr, Artificial Intelligence in Healthcare and Medicine, 2022
Behnam Rahmatikaregar, Shahram Shirani, Zahra Keshavarz-Motamed
Sensitivity or the “true positive rate,” is equal to . Specificity or the “true negative rate,” is calculated as . PPV “positive predictive value” is equal to . NPV “negative predictive value” is calculated as .Where TP (true positive) is the number of pixels that are correctly predicted as belonging to the object (which is the LV here), TN (true negative) is the number of pixels that are correctly predicted as belonging to the background. FP is the number of pixels that belong to background but are misclassified as belonging to the left ventricle. FN is the number of pixels belonging to the LV that are misclassified as belonging to the background.
Alarms and Clinical Surveillance
Published in John R. Zaleski, Clinical Surveillance, 2020
Two other concepts are now introduced: positive predictive value (PPV) and negative predictive value (NPV), given by Equations 3.3 and 3.4, respectively:
The Use of Ovarian Markers
Published in Botros Rizk, Yakoub Khalaf, Controversies in Assisted Reproduction, 2020
Neena Malhotra, Siladitya Bhattacharya
The prevalence of disease in a population affects the performance of screening tests. Even excellent tests have poor predictive value positives in low-prevalence settings. For example, a valid test of ovarian reserve will have a better positive predictive value in women attending a fertility clinic than in a general population of asymptomatic women. Hence, knowledge of the approximate prevalence of disease is a prerequisite to interpreting screening test results. Inappropriate application or interpretation of screening tests can rob people of their perceived health, initiate harmful diagnostic testing, and squander health-care resources. From a clinical perspective, key questions about tests include the following: Is it relevant, i.e., can the test be used in the relevant patient group? Is it affordable, acceptable, and better than the test normally used? And crucially, Will it inform the choice of treatment?
Microscopic hematuria and pelvic ultrasonography could rule out flexible cystoscopy during surveillance for T1-low grade non-muscle invasive bladder cancer
Published in Arab Journal of Urology, 2023
Mohamed Awad, Ahmed M. Harraz, Hashim Farg, Hady S. Gabr, Doaa E. Sharaf, Mohamed Abou-El-Ghar, Ahmed S. El-Hefnawy, Yasser Osman
All data were prospectively collected and maintained in an electronic database. The sensitivity of a test is defined as the percent of patients with the disease for whom the test is positive (true positive/total number of patients with the disease × 100). A comparison of sensitivity between the two measures was performed using the McNemar test. The negative predictive value (NPV) is defined as the percent of individuals in whom the test is negative, and the disease is not present [true negative/test negative (true negative + false negative) × 100]. Statistical differences of NPV were calculated with the generalized score statistic test. Specificity and PPV were not the primary targets of the study. Wilcoxon signed-rank test was used for paired comparison of non-parametric variables. Statistical analysis was performed using IBM v.25 statistical software and R programming language v. 3.6.3 (http://www.r-project.org) with the package DTComPair.
The impact of the HLA DQB1 gene and amino acids on the development of narcolepsy
Published in International Journal of Neuroscience, 2022
Leila Kachooei-Mohaghegh-yaghoobi, Fatemeh Rezaei-Rad, Khosro Sadeghniiat-Haghighi, Mahdi Zamani
All data, including demographic, clinical, DQB1 genotypes and alleles have been entered into a database and analyzed with SPSS, version 25 for WINDOWS (Chicago, IL, USA). Genotype and allele frequencies of the HLA-DQB1 have been compared between the patients and the controls, and Fisher’s exact test has been used to detect the significances. After using Bonferroni correction for multiple comparisons, corrected values (Pc) < 0.05 have been considered statistically significant. Moreover, positive predictive value (PPV) calculates the effectiveness of a diagnostic test and denotes the likelihood for a person with a positive test of developing or having the disease. In this case-control study, the following formula has been used to characterize the Prevalence corrected Positive Predictive Value (PcPPV):
Evaluation of the GeneXpert MTB/RIF assay performance in sputum samples with various characteristics from presumed pulmonary tuberculosis patients in Shiselweni region, Eswatini
Published in Infectious Diseases, 2022
Durbbin Lupiya Mulengwa, Maropeng Charles Monyama, Sogolo Lucky Lebelo
All samples tested on GeneXpert MTB/RIF assay were also processed on MGIT culture. Statistical analysis was performed to determine sensitivity and specificity as well as positive and negative predictive values on both GeneXpert MTB/RIF and MGIT culture. The sensitivity was defined as the ability of the test to correctly identify those patients (or samples) with the disease. Specificity was defined as the test’s ability to correctly identify those patients (or sputum samples without the disease. The positive predictive value was described as the probability that subjects with a positive screening test truly have the disease while the negative predictive value is the probability that subjects with a negative screening test truly don't have the disease. The likelihood ratio was defined as how much more likely was it that a patient (or sample obtained which tests positive has the disease compared with one that tests negative. To measure the effects of each characteristic on the GeneXpert MTB/RIF positive results, a univariate and multivariate analysis was performed using simple logistic regression and multiple linear regression respectively. The difference was declared as statistically significant if P-value was less than .05. P-value is the probability of obtaining results as extreme as the observed results of a statistical hypothesis assuming that the null hypothesis is correct.