1267 Utility of Artificial Intelligence in the Cystoscopic Detection of Bladder Cancer: A Systematic Review and Meta-Analysis

2021 ◽  
Vol 108 (Supplement_6) ◽  
Author(s):  
S Ganesananthan ◽  
S Ganesananthan ◽  
B S Simpson ◽  
J M Norris
Keyword(s):  
Artificial Intelligence ◽  
Bladder Cancer ◽  
Sensitivity And Specificity ◽  
Meta Analysis ◽  
External Validation ◽  
High Sensitivity ◽  
Diagnostic Odds Ratio ◽  
Pooled Analysis ◽  
Likelihood Ratios ◽  
Novel Technology

Abstract Aim Detection of suspected bladder cancer at diagnostic cystoscopy is challenging and is dependent on clinician skill. Artificial Intelligence (AI) algorithms, specifically, machine learning and deep learning, have shown promise in accurate classification of pathological images in various specialties. However, utility of AI for urothelial cancer diagnosis is unknown. Here, we aimed to systematically review the extant literature in this field and quantitively summarise the role of these algorithms in bladder cancer detection. Method The EMBASE, PubMed and CENTRAL databases were searched up to December 22nd 2020 , in accordance with the PRISMA guidelines, for studies that evaluated AI algorithms for cystoscopic diagnosis of bladder cancer. Random-effects meta-analysis was performed to summarise eligible studies. Risk of Bias was assessed using the QUADAS-2 tool. Results Five from 6715 studies met criteria for inclusion. Pooled sensitivity and specificity values were 0.93 (95% CI 0.89–0.95) and 0.93 (95% CI 0.80–0.89) respectively. Pooled positive likelihood and negative likelihood ratios were 14 (95% CI 4.3–44) and 0.08 (95% CI: 0.05–0.11), respectively. Pooled diagnostic odds ratio was 182 (95% CI 61–546). Summary AUC curve value was 0.95 (95% CI 0.93–0.97). No significant publication bias was noted. Conclusions In summary, AI algorithms performed very well in detection of bladder cancer in this pooled analysis, with high sensitivity and specificity values. However, as with other clinical AI usage, further external validation through deployment in real clinical situations is essential to assess true applicability of this novel technology.

scholarly journals A Systematic Review and Meta-Analysis of the Accuracy of in VivoReflectance Confocal Microscopy for the Diagnosis of Primary Basal Cell Carcinoma

2019 ◽  
Vol 8 (9) ◽  
pp. 1462 ◽  
Author(s):  
Lupu ◽  
Popa ◽  
Voiculescu ◽  
Caruntu ◽  
Caruntu
Keyword(s):  
Systematic Review ◽  
Confocal Microscopy ◽  
Basal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Sensitivity And Specificity ◽  
Basal Cell ◽  
Meta Analysis ◽  
High Sensitivity ◽  
Diagnostic Odds Ratio ◽  
Likelihood Ratios

Basal cell carcinoma (BCC) is the most common cancer worldwide and its incidence is constantly rising. Early diagnosis and treatment can significantly reduce patient morbidity and healthcare costs. The value of reflectance confocal microscopy (RCM) in non-melanoma skin cancer diagnosis is still under debate. This systematic review and meta-analysis were conducted to assess the diagnostic accuracy of RCM in primary BCC. PubMed, Google Scholar, Scopus, and Web of Science databases were searched up to July 05, 2019, to collect articles concerning primary BCC diagnosis through RCM. The studies’ methodological quality was assessed by the QUADAS-2 tool. The meta-analysis was conducted using Stata 13.0, RevMan 5.0, and MetaDisc 1.4 software. We included 15 studies totaling a number of 4163 lesions. The pooled sensitivity and specificity were 0.92 (95% CI, 0.87–0.95; I2= 85.27%) and 0.93 (95% CI, 0.85–0.97; I2= 94.61%), the pooled positive and negative likelihood ratios were 13.51 (95% CI, 5.8–31.37; I2= 91.01%) and 0.08 (95% CI, 0.05–0.14; I2= 84.83%), and the pooled diagnostic odds ratio was 160.31 (95% CI, 64.73–397.02; I2=71%). Despite the heterogeneity and risk of bias, this study demonstrates that RCM, through its high sensitivity and specificity, may have a significant clinical impact on the diagnosis of primary BCC.

scholarly journals Diagnostic Accuracy of Confocal Laser Endomicroscopy for the Diagnosis of Oral Squamous Cell Carcinoma: A Systematic Review and Meta-Analysis

2021 ◽  
Vol 18 (23) ◽  
pp. 12390
Author(s):  
Sneha Sethi ◽  
Xiangqun Ju ◽  
Richard M. Logan ◽  
Paul Sambrook ◽  
Robert A. McLaughlin ◽  
...  
Keyword(s):  
Systematic Review ◽  
Squamous Cell Carcinoma ◽  
Diagnostic Accuracy ◽  
Sensitivity And Specificity ◽  
Meta Analysis ◽  
High Sensitivity ◽  
Risk Of Bias ◽  
Confocal Laser Endomicroscopy ◽  
Confocal Laser ◽  
Likelihood Ratios

Background: Advances in treatment approaches for patients with oral squamous cell carcinoma (OSCC) have been unsuccessful in preventing frequent recurrences and distant metastases, leading to a poor prognosis. Early detection and prevention enable an improved 5-year survival and better prognosis. Confocal Laser Endomicroscopy (CLE) is a non-invasive imaging instrument that could enable an earlier diagnosis and possibly help in reducing unnecessary invasive surgical procedures. Objective: To present an up to date systematic review and meta-analysis assessing the diagnostic accuracy of CLE in diagnosing OSCC. Materials and Methods. PubMed, Scopus, and Web of Science databases were explored up to 30 June 2021, to collect articles concerning the diagnosis of OSCC through CLE. Screening: data extraction and appraisal was done by two reviewers. The quality of the methodology followed by the studies included in this review was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool. A random effects model was used for the meta-analysis. Results: Six studies were included, leading to a total number of 361 lesions in 213 patients. The pooled sensitivity and specificity were 95% (95% CI, 92–97%; I2 = 77.5%) and 93% (95% CI, 90–95%; I2 = 68.6%); the pooled positive likelihood ratios and negative likelihood ratios were 10.85 (95% CI, 5.4–21.7; I2 = 55.9%) and 0.08 (95% CI, 0.03–0.2; I2 = 83.5%); and the pooled diagnostic odds ratio was 174.45 (95% CI, 34.51–881.69; I2 = 73.6%). Although risk of bias and heterogeneity is observed, this study validates that CLE may have a noteworthy clinical influence on the diagnosis of OSCC, through its high sensitivity and specificity. Conclusions: This review indicates an exceptionally high sensitivity and specificity of CLE for diagnosing OSCC. Whilst it is a promising diagnostic instrument, the limited number of existing studies and potential risk of bias of included studies does not allow us to draw firm conclusions. A conclusive inference can be drawn when more studies, possibly with homogeneous methodological approach, are performed.

scholarly journals Diagnostic test accuracy of Xpert MTB/RIF for tuberculous pericarditis: a systematic review and meta-analysis

F1000Research ◽  
2020 ◽  
Vol 9 ◽  
pp. 761
Author(s):  
Andrianto Andrianto ◽  
Ni Made Mertaniasih ◽  
Parama Gandi ◽  
Makhyan Jibril Al-Farabi ◽  
Yusuf Azmi ◽  
...  
Keyword(s):  
Systematic Review ◽  
Sensitivity And Specificity ◽  
Meta Analysis ◽  
Pooled Analysis ◽  
Test Accuracy ◽  
Test Results ◽  
Likelihood Ratios ◽  
Bivariate Model ◽  
Cross Sectional ◽  
Sensitivity Specificity

Introduction: Xpert MTB/RIF is a rapid diagnostic instrument for pulmonary tuberculosis (TB). However, studies reported varied accuracy of Xpert MTB/RIF in detecting Mycobacterium tuberculosis in pericardial effusion. Methods: We performed a systematic review of literature in PubMed, published up to February 1, 2020, according to PRISMA guidelines. We screened cross-sectional studies, observational cohort studies, and randomized control trials that evaluated the accuracy of Xpert MTB/RIF in diagnosing TB pericarditis. Papers with noninterpretable results of sensitivity and specificity, non-English articles, and unpublished studies were excluded. The primary outcomes were the sensitivity and specificity of Xpert MTB/RIF. We conducted a quality assessment using QUADAS-2 to evaluate the quality of the studies. A bivariate model pooled the overall sensitivity, specificity, positive likelihood ratios (PLRs), and negative likelihood ratios (NLRs) of included studies. Results: In total, 581 subjects from nine studies were analyzed in this meta-analysis. Our pooled analysis showed that the overall sensitivity, specificity, PLRs and NLRs of included studies were 0.676 (95% CI: 0.580–0.759), 0.994 (95% CI: 0.919–1.000), 110.11 (95% CI: 7.65–1584.57) and 0.326 (95% CI: 0.246–0.433), respectively. Conclusions: Xpert MTB/RIF had a robust specificity but unsatisfactory sensitivity in diagnosing TB pericarditis. These findings indicated that although positive Xpert MTB/RIF test results might be valuable in swiftly distinguishing the diagnosis of TB pericarditis, negative test results might not be able to rule out TB pericarditis. Registration: PROSPERO CRD42020167480 28/04/2020

scholarly journals The Diagnostic Value of LncRNAs for Bladder Cancer: A Meta-Analysis

2021 ◽  
Author(s):  
Yuan Chen ◽  
Faiza Naz ◽  
Shi Fu ◽  
Mengran Shi ◽  
Haihao Li ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Diagnostic Accuracy ◽  
Likelihood Ratio ◽  
Sensitivity And Specificity ◽  
Odds Ratio ◽  
Meta Analysis ◽  
Positive Likelihood Ratio ◽  
Diagnostic Odds Ratio ◽  
Diagnostic Value ◽  
Cochrane Library

Abstract Background: In recent years, qualitative and quantitative analysis of LncRNA has been reported as a potential method for early diagnosis of bladder cancer, but the results from each research are insufficient and not completely consistent. This meta-analysis aims to evaluate the diagnostic value of LncRNA for BC.Methods: We conducted a diagnostic meta-analysis and the diagnostic significance of LncRNA in blood, urine and tumor tissues was discussed. We searched the PUBMED, EMABASE, and Cochrane Library until June 2020. The current meta-analysis was performed using Review Manager 5.2, Stata 16.0 and Meta-Disc 1.4 software. Results: A total of 18 researches involving early and/or advanced bladder cancer were finally included. The overall diagnostic accuracy was measured as follows: pooled sensitivity and specificity were 0.72 (95%CI:0.70, 0.73) and 0.76 (95%CI: 0.75, 0.78). Pooled positive likelihood ratio and negative likelihood ratio were 3.09 (95%CI: 2.66, 3.58) and 0.37 (95%CI: 0.33, 0.42). Combined diagnostic odds ratio was 9.43 (95%CI: 7.30, 12.20). A high diagnostic accuracy was demonstrated by the summary receiver operating characteristic curve, with area under the curve of 0.82 (95%CI: 0.78, 0.85). UCA1 and H19 had the best diagnostic effect, their diagnostic sensitivity and specificity were 80%, 79% and 79%, 73% respectively, the combined diagnostic odds ratio was 16.85 and 12.67 respectively.Conclusions: This meta-analysis suggests that LncRNA have great potential in the diagnosis of bladder cancer, UCA1 and H19 had the best diagnostic effect. LncRNA panel is the future development direction in the diagnosis of bladder cancer. However, larger sample researches are needed to further confirm our conclusion.

scholarly journals Diagnostic accuracy of synovial fluid D-lactate for periprosthetic joint infection: a systematic review and meta-analysis

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Zhizhuo Li ◽  
Chengxin Li ◽  
Guangxue Wang ◽  
Lijun Shi ◽  
Tengqi Li ◽  
...  
Keyword(s):  
Synovial Fluid ◽  
Diagnostic Accuracy ◽  
Sensitivity And Specificity ◽  
Periprosthetic Joint Infection ◽  
Odds Ratio ◽  
Meta Analysis ◽  
Characteristic Curve ◽  
Joint Infection ◽  
Diagnostic Odds Ratio ◽  
Likelihood Ratios

Abstract Background Periprosthetic joint infection is a grievous complication after arthroplasty that greatly affects the quality of life of patients. Rapid establishment of infection diagnosis is essential, but great challenges still exist. Methods We conducted research in the PubMed, Embase, and Cochrane databases to evaluate the diagnostic accuracy of D-lactate for PJI. Data extraction and quality assessment were completed independently by two reviewers. The pooled sensitivity, specificity, likelihood ratios, diagnostic odds ratio (DOR), summarized receiver operating characteristic curve (sROC), and area under the sROC curve (AUC) were constructed using the bivariate meta-analysis framework. Results Five eligible studies were included in the quantitative analysis. The pooled sensitivity and specificity of D-lactate for the diagnosis of PJI were 0.82 (95% CI 0.70–0.89) and 0.76 (95% CI 0.69–0.82), respectively. The value of the pooled diagnostic odds ratio (DOR) of D-lactate for PJI was 14.18 (95% CI 6.17–32.58), and the area under the curve (AUC) was 0.84 (95% CI 0.80–0.87). Conclusions According to the results of our meta-analysis, D-lactate is a valuable synovial fluid marker for recognizing PJI, with high sensitivity and specificity.

scholarly journals Application of Artificial Intelligence for Screening COVID-19 Patients Using Digital Images: Meta-analysis

10.2196/21394 ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. e21394
Author(s):  
Tahmina Nasrin Poly ◽  
Md Mohaimenul Islam ◽  
Yu-Chuan Jack Li ◽  
Belal Alsinglawi ◽  
Min-Huei Hsu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Sensitivity And Specificity ◽  
Operating Characteristic ◽  
Meta Analysis ◽  
Characteristic Curve ◽  
Diagnostic Odds Ratio ◽  
Study Characteristics ◽  
Key Terms ◽  
Immense Potential

Background The COVID-19 outbreak has spread rapidly and hospitals are overwhelmed with COVID-19 patients. While analysis of nasal and throat swabs from patients is the main way to detect COVID-19, analyzing chest images could offer an alternative method to hospitals, where health care personnel and testing kits are scarce. Deep learning (DL), in particular, has shown impressive levels of performance when analyzing medical images, including those related to COVID-19 pneumonia. Objective The goal of this study was to perform a systematic review with a meta-analysis of relevant studies to quantify the performance of DL algorithms in the automatic stratification of COVID-19 patients using chest images. Methods A search strategy for use in PubMed, Scopus, Google Scholar, and Web of Science was developed, where we searched for articles published between January 1 and April 25, 2020. We used the key terms “COVID-19,” or “coronavirus,” or “SARS-CoV-2,” or “novel corona,” or “2019-ncov,” and “deep learning,” or “artificial intelligence,” or “automatic detection.” Two authors independently extracted data on study characteristics, methods, risk of bias, and outcomes. Any disagreement between them was resolved by consensus. Results A total of 16 studies were included in the meta-analysis, which included 5896 chest images from COVID-19 patients. The pooled sensitivity and specificity of the DL models in detecting COVID-19 were 0.95 (95% CI 0.94-0.95) and 0.96 (95% CI 0.96-0.97), respectively, with an area under the receiver operating characteristic curve of 0.98. The positive likelihood, negative likelihood, and diagnostic odds ratio were 19.02 (95% CI 12.83-28.19), 0.06 (95% CI 0.04-0.10), and 368.07 (95% CI 162.30-834.75), respectively. The pooled sensitivity and specificity for distinguishing other types of pneumonia from COVID-19 were 0.93 (95% CI 0.92-0.94) and 0.95 (95% CI 0.94-0.95), respectively. The performance of radiologists in detecting COVID-19 was lower than that of the DL models; however, the performance of junior radiologists was improved when they used DL-based prediction tools. Conclusions Our study findings show that DL models have immense potential in accurately stratifying COVID-19 patients and in correctly differentiating them from patients with other types of pneumonia and normal patients. Implementation of DL-based tools can assist radiologists in correctly and quickly detecting COVID-19 and, consequently, in combating the COVID-19 pandemic.

scholarly journals Application of Artificial Intelligence for Screening COVID-19 Patients Using Digital Images: Meta-analysis (Preprint)

2020 ◽  
Author(s):  
Tahmina Nasrin Poly ◽  
Md Mohaimenul Islam ◽  
Yu-Chuan Jack Li ◽  
Belal Alsinglawi ◽  
Min-Huei Hsu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Sensitivity And Specificity ◽  
Operating Characteristic ◽  
Meta Analysis ◽  
Characteristic Curve ◽  
Diagnostic Odds Ratio ◽  
Study Characteristics ◽  
Key Terms ◽  
Immense Potential

BACKGROUND The COVID-19 outbreak has spread rapidly and hospitals are overwhelmed with COVID-19 patients. While analysis of nasal and throat swabs from patients is the main way to detect COVID-19, analyzing chest images could offer an alternative method to hospitals, where health care personnel and testing kits are scarce. Deep learning (DL), in particular, has shown impressive levels of performance when analyzing medical images, including those related to COVID-19 pneumonia. OBJECTIVE The goal of this study was to perform a systematic review with a meta-analysis of relevant studies to quantify the performance of DL algorithms in the automatic stratification of COVID-19 patients using chest images. METHODS A search strategy for use in PubMed, Scopus, Google Scholar, and Web of Science was developed, where we searched for articles published between January 1 and April 25, 2020. We used the key terms “COVID-19,” or “coronavirus,” or “SARS-CoV-2,” or “novel corona,” or “2019-ncov,” and “deep learning,” or “artificial intelligence,” or “automatic detection.” Two authors independently extracted data on study characteristics, methods, risk of bias, and outcomes. Any disagreement between them was resolved by consensus. RESULTS A total of 16 studies were included in the meta-analysis, which included 5896 chest images from COVID-19 patients. The pooled sensitivity and specificity of the DL models in detecting COVID-19 were 0.95 (95% CI 0.94-0.95) and 0.96 (95% CI 0.96-0.97), respectively, with an area under the receiver operating characteristic curve of 0.98. The positive likelihood, negative likelihood, and diagnostic odds ratio were 19.02 (95% CI 12.83-28.19), 0.06 (95% CI 0.04-0.10), and 368.07 (95% CI 162.30-834.75), respectively. The pooled sensitivity and specificity for distinguishing other types of pneumonia from COVID-19 were 0.93 (95% CI 0.92-0.94) and 0.95 (95% CI 0.94-0.95), respectively. The performance of radiologists in detecting COVID-19 was lower than that of the DL models; however, the performance of junior radiologists was improved when they used DL-based prediction tools. CONCLUSIONS Our study findings show that DL models have immense potential in accurately stratifying COVID-19 patients and in correctly differentiating them from patients with other types of pneumonia and normal patients. Implementation of DL-based tools can assist radiologists in correctly and quickly detecting COVID-19 and, consequently, in combating the COVID-19 pandemic.

scholarly journals Accuracy of angiopoietin-2 for predicting organ failure in patients with acute pancreatitis: a systematic review and meta-analysis

2021 ◽  
Vol 49 (2) ◽  
pp. 030006052098670
Author(s):  
Yongcai Lv ◽  
Yanhua Yao ◽  
Qi Liu ◽  
Jingjing Lei
Keyword(s):  
Acute Pancreatitis ◽  
Organ Failure ◽  
Meta Analysis ◽  
Characteristic Curve ◽  
Diagnostic Odds Ratio ◽  
Cochrane Library ◽  
Case Control Studies ◽  
Likelihood Ratios ◽  
Summary Receiver Operating Characteristic ◽  
Angiopoietin 2

Objective Our aim was to assess the accuracy of angiopoietin-2 (Ang-2) as a prognostic marker for acute pancreatitis (AP) with organ failure (OF). Methods We undertook a systematic search of the PubMed, Cochrane Library, Embase, Chinese Journals Full-text, Wanfang, China Biology Medicine disc, and Weipu databases to identify eligible cohort studies on the predictive value of Ang-2 for AP with OF. The main outcome measures were sensitivity and specificity. The effects were pooled using a bivariate mixed-effects model. Results Six articles with seven case-control studies (n = 650) were included. Pooled sensitivity, specificity, and positive and negative likelihood ratios with 95% confidence intervals (CI) for AP with OF were 0.93 (95%CI: 0.75–0.99), 0.85 (95%CI: 0.75–0.92), 6.40 (95%CI: 3.36–12.19), and 0.08 (95%CI: 0.02–0.36), respectively. The area under the summary receiver operating characteristic curve was 0.95 (95%CI: 0.92–0.96), and the diagnostic odds ratio was 83.18 (95%CI: 11.50–623.17). Subgroup analysis showed that admission time of AP onset (< or ≥24 hours) was a source of overall heterogeneity. Sensitivity analysis supported this finding. Conclusion Ang-2 had high diagnostic accuracy for AP with OF; the best prediction of Ang-2 may be 24 to 72 hours after onset of AP.

An Artificial Intelligence System for the Detection of Bladder Cancer via Cystoscopy: A Multicenter Diagnostic Study

2021 ◽  
Author(s):  
Shaoxu Wu ◽  
Xiong Chen ◽  
Jiexin Pan ◽  
Wen Dong ◽  
Xiayao Diao ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Bladder Cancer ◽  
Diagnostic Accuracy ◽  
External Validation ◽  
Operation Time ◽  
Short Latency ◽  
Lesion Detection ◽  
Diagnostic Study ◽  
Internal Validation ◽  
Predictive Values

Abstract Background Cystoscopy plays an important role in bladder cancer (BCa) diagnosis and treatment, but its sensitivity needs improvement. Artificial intelligence has shown promise in endoscopy, but few cystoscopic applications have been reported. We report a Cystoscopy Artificial Intelligence Diagnostic System (CAIDS) for BCa diagnosis. Methods In total, 69,204 images from 10,729 consecutive patients from six hospitals were collected and divided into training, internal validation, and external validation sets. The CAIDS was built using a pyramid scene parsing network and transfer learning. A subset (n = 260) of the validation sets was used for a performance comparison between the CAIDS and urologists for complex lesion detection. The diagnostic accuracy, sensitivity, specificity, and positive and negative predictive values and 95% confidence intervals (CIs) were calculated using the Clopper-Pearson method. Results The diagnostic accuracies of the CAIDS were 0.977 (95% CI = 0.974–0.979) in the internal validation set and 0.990 (95% CI = 0.979–0.996), 0.982 (95% CI = 0.974–0.988), 0.978 (95% CI = 0.959–0.989), and 0.991 (95% CI = 0.987–0.994) in different external validation sets. In the CAIDS versus urologists’ comparisons, the CAIDS showed high accuracy and sensitivity (accuracy = 0.939, 95% CI = 0.902–0.964; and sensitivity = 0.954, 95% CI = 0.902–0.983) with a short latency of 12 s, much more accurate and quicker than the expert urologists. Conclusions The CAIDS achieved accurate BCa detection with a short latency. The CAIDS may provide many clinical benefits, from increasing the diagnostic accuracy for BCa, even for commonly misdiagnosed cases such as flat cancerous tissue (carcinoma in situ), to reducing the operation time for cystoscopy.

scholarly journals Pooled analysis of T2 Candida for rapid diagnosis of candidiasis

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Dong-Lan Tang ◽  
Xiao Chen ◽  
Chang-Guo Zhu ◽  
Zhong-wei Li ◽  
Yong Xia ◽  
...  
Keyword(s):  
Likelihood Ratio ◽  
Web Of Science ◽  
Meta Analysis ◽  
Positive Likelihood Ratio ◽  
Negative Likelihood Ratio ◽  
Diagnostic Odds Ratio ◽  
Pooled Analysis ◽  
Current Evidence ◽  
Cochrane Library ◽  
Research Subjects

Abstract Background The present meta-analysis examined the diagnostic accuracy of T2 Candida for candidiasis. Methods The literature databases, such as PubMed, Embase, DVIO, Cochrane library, Web of Science, and CNKI, were searched on T2 Candida detection. Results A total of 8 articles, comprising of 2717 research subjects, were included in the study. The pooled sensitivity and specificity were 0.91 (95% confidence interval (CI): 0.88–0.94) and 0.94 95% CI: 0.93–0.95), respectively. The pooled positive likelihood ratio and negative likelihood ratio was 10.16 (95% CI: 2.75–37.50) and 0.08 (95% CI: 0.02–0.35), respectively. The combined diagnostic odds ratio is 133.65 95% CI: 17.21–1037.73), and the AUC of SROC is 0.9702 [(SE = 0.0235), Q* = 0.9201(SE = 0.0381)]. Conclusions The current evidence supported that T2 Candida has high accuracy and sensitivity and is of major clinical significance in the diagnosis of Candida infection.

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