Artificial Intelligence-Assisted Chest X-ray for the Diagnosis of COVID-19: A Systematic Review and Meta-Analysis.

Because it is an accessible and routine image test, medical personnel commonly use a chest X-ray for COVID-19 infections. Artificial intelligence (AI) is now widely applied to improve the precision of routine image tests. Hence, we investigated the clinical merit of the chest X-ray to detect COVID-19 when assisted by AI. We used PubMed, Cochrane Library, MedRxiv, ArXiv, and Embase to search for relevant research published between 1 January 2020 and 30 May 2022. We collected essays that dissected AI-based measures used for patients diagnosed with COVID-19 and excluded research lacking measurements using relevant parameters (i.e., sensitivity, specificity, and area under curve). Two independent researchers summarized the information, and discords were eliminated by consensus. A random effects model was used to calculate the pooled sensitivities and specificities. The sensitivity of the included research studies was enhanced by eliminating research with possible heterogeneity. A summary receiver operating characteristic curve (SROC) was generated to investigate the diagnostic value for detecting COVID-19 patients. Nine studies were recruited in this analysis, including 39,603 subjects. The pooled sensitivity and specificity were estimated as 0.9472 (p = 0.0338, 95% CI 0.9009-0.9959) and 0.9610 (p < 0.0001, 95% CI 0.9428-0.9795), respectively. The area under the SROC was 0.98 (95% CI 0.94-1.00). The heterogeneity of diagnostic odds ratio was presented in the recruited studies (I2 = 36.212, p = 0.129). The AI-assisted chest X-ray scan for COVID-19 detection offered excellent diagnostic potential and broader application.

A pseudo-likelihood approach for multivariate meta-analysis of test accuracy studies with multiple thresholds.

Multivariate meta-analysis of test accuracy studies when tests are evaluated in terms of sensitivity and specificity at more than one threshold represents an effective way to synthesize results by fully exploiting the data, if compared to univariate meta-analyses performed at each threshold independently. The approximation of logit transformations of sensitivities and specificities at different thresholds through a normal multivariate random-effects model is a recent proposal that straightforwardly extends the bivariate models well recommended for the one threshold case. However, drawbacks of the approach, such as poor estimation of the within-study correlations between sensitivities and between specificities, and severe computational issues can make it unappealing. We propose an alternative method for inference on common diagnostic measures using a pseudo-likelihood constructed under a working independence assumption between sensitivities and between specificities at different thresholds in the same study. The method does not require within-study correlations, overcomes the convergence issues and can be effortlessly implemented. Simulation studies highlight a satisfactory performance of the method, remarkably improving the results from the multivariate normal counterpart under different scenarios. The pseudo-likelihood approach is illustrated in the evaluation of a test used for diagnosis of preeclampsia as a cause of maternal and perinatal morbidity and mortality.

Influence diagnostics and outlier detection for meta-analysis of diagnostic test accuracy.

Meta-analyses of diagnostic test accuracy (DTA) studies have been gaining prominence in research in clinical epidemiology and health technology development. In these DTA meta-analyses, some studies may have markedly different characteristics from the others and potentially be inappropriate to include. The inclusion of these "outlying" studies might lead to biases, yielding misleading results. In addition, there might be influential studies that have notable impacts on the results. In this article, we propose Bayesian methods for detecting outlying studies and their influence diagnostics in DTA meta-analyses. Synthetic influence measures based on the bivariate hierarchical Bayesian random effects models are developed because the overall influences of individual studies should be simultaneously assessed by the two outcome variables and their correlation information. We propose four synthetic measures for influence analyses: (a) relative distance, (b) standardized residual, (c) Bayesian p-value, and (d) influence statistic on the area under the summary receiver operating characteristic curve. We also show that conventional univariate Bayesian influential measures can be applied to the bivariate random effects models, which can be used as marginal influential measures. Most of these methods can be similarly applied to the frequentist framework. We illustrate the effectiveness of the proposed methods by applying them to a DTA meta-analysis of ultrasound in screening for vesicoureteral reflux among children with urinary tract infections.

Diagnostic Performance of Multiple Sound Touch Elastography for Differentiating Benign and Malignant Thyroid Nodules.

This study evaluated the ability of Sound Touch Elastography (STE) to distinguish malignant from benign thyroid nodules by quantifying tumor stiffness using the elastic ratio (EI) and shear modulus (G). Eighty-six patients with 86 nodules were enrolled in this study. There were 24/86 (27.90%) thyroid papillary carcinomas (TPC) and 62/86 (72.10%) benign nodules. The mean EI was significantly lower in TPCs than in benign nodules. The EI area under the receiver operating characteristic curve (ROC) was 80%. The EI cutoff value for TPCs was 0.215%. The sensitivity (Sen), specificity (Spe), positive likelihood ratio (LR+), and negative likelihood ratio (LR-) were 71%, 73%, 2.58, and 0.40, respectively. G max, G mean, and G sd were significantly higher in TPCs than in benign nodules. There were no significant differences in G min. Compared with other G parameters, G max with an optimal cutoff value of 15.82 kPa had the highest AUROC value (84%). The Sen, Spe, LR+, and LR- were 79.17%, 79.03%, 3.776, and 0.261, respectively. We pooled the EI, G max, G mean, and G sd and the pooled-Sen, Spe, LR+, LR-, diagnostic odds ratio and odds ratio, and area under the summary ROC were 79%, 71%, 2.73, 0.29, 2.23, 9.29, and 82%, respectively. STE could be a new ultrasound diagnostic method for evaluating benign and malignant thyroid nodules.

Meta-analysis of Diagnostic Accuracy and ROC Curves with Covariate Adjusted Semiparametric Mixtures.

Many screening tests dichotomize a measurement to classify subjects. Typically a cut-off value is chosen in a way that allows identification of an acceptable number of cases relative to a reference procedure, but does not produce too many false positives at the same time. Thus for the same sample many pairs of sensitivities and false positive rates result as the cut-off is varied. The curve of these points is called the receiver operating characteristic (ROC) curve. One goal of diagnostic meta-analysis is to integrate ROC curves and arrive at a summary ROC (SROC) curve. Holling, Böhning, and Böhning (Psychometrika 77:106-126, 2012a) demonstrated that finite semiparametric mixtures can describe the heterogeneity in a sample of Lehmann ROC curves well; this approach leads to clusters of SROC curves of a particular shape. We extend this work with the help of the [Formula: see text] transformation, a flexible family of transformations for proportions. A collection of SROC curves is constructed that approximately contains the Lehmann family but in addition allows the modeling of shapes beyond the Lehmann ROC curves. We introduce two rationales for determining the shape from the data. Using the fact that each curve corresponds to a natural univariate measure of diagnostic accuracy, we show how covariate adjusted mixtures lead to a meta-regression on SROC curves. Three worked examples illustrate the method.

CHROMagar Candida medium for identification of Candida species:a systematic review / 国际检验医学杂志

Objective To evaluate the accuracy of CHROMagar Candida medium in identifying common Candida species .Meth‐ods Articles were extensively collected by searching the databases of MEDLINE and EMBase ,the Chinese Biomedical Database (CBM ) ,the Chinese Scientific Journals Database (CSJD) ,the Chinese Journal Full Text Database (CJFD) and through other ways . The qualities of these articles were assessed by using the quality assessment of diagnostic accuracy studies(QUADAS) .At last , summary receiver operating characteristic (SROC) curve was performed by the Meta‐Disc software ,so as to summarize diagnostic accuracy of CHROMagar Candida medium in identifying common Candida species .Results A total of 7 articles meeting all criteria were enrolled in this study .All 7 articles reported the accuracy of CHROMagar Candida medium in identifying the Candida albi‐cans ,the pooled sensitivity and specificity was 98 .3% and 98 .8% respectively ,and area under SROC curve (AUC) was 0 .998 0 .A‐mong them ,6 articles reported the accuracy of CHROMagar Candida medium in identifying Candida tropicalis ,the pooled sensitivity and specificity was 92 .5% and 99 .8% respectively ,and the AUC was 0 .998 3 .Among them ,5 articles reported the accuracy of CHROMagar Candida medium in identifying Candida Glabrata ,the pooled sensitivity and specificity was 98 .3% and 98 .7% respec‐tively ,and the AUC was 0 .996 8 .Conclusion CHROMagar Candida medium could quickly identify clinical common Candida species and results are reliable .

A Meta-Analysis of the Accuracy of Prostate Cancer Studies Which Use Magnetic Resonance Spectroscopy as a Diagnostic Tool

OBJECTIVE: We aimed to do a meta-analysis of the existing literature to assess the accuracy of prostate cancer studies which use magnetic resonance spectroscopy (MRS) as a diagnostic tool. MATERIALS AND METHODS: Prospectively, independent, blind studies were selected from the Cochrane library, Pubmed, and other network databases. The criteria for inclusion and exclusion in this study referenced the criteria of diagnostic research published by the Cochrane center. The statistical analysis was adopted by using Meta-Test version 6.0. Using the homogeneity test, a statistical effect model was chosen to calculate different pooled weighted values of sensitivity, specificity, and the corresponding 95% confidence intervals (95% CI). The summary receiver operating characteristic (SROC) curves method was used to assess the results. RESULTS: We chose two cut-off values (0.75 and 0.86) as the diagnostic criteria for discriminating between benign and malignant. In the first diagnostic criterion, the pooled weighted sensitivity, specificity, and corresponding 95% CI (expressed as area under curve [AUC]) were 0.82 (0.73, 0.89), 0.68 (0.58, 0.76), and 83.4% (74.97, 91.83). In the second criterion, the pooled weighted sensitivity, specificity, and corresponding 95% CI were 0.64 (0.55, 0.72), 0.86 (0.79, 0.91) and 82.7% (68.73, 96.68). CONCLUSION: As a new method in the diagnostic of prostate cancer, MRS has a better applied value compared to other common modalities. Ultimately, large scale RCT (randomized controlled trial) randomized controlled trial studies are necessary to assess its clinical value.