Summary Receiver Operating Characteristic Research Articles

Predicting adverse drug event using machine learning based on electronic health records: a systematic review and meta-analysis

IntroductionAdverse drug events (ADEs) pose a significant challenge in current clinical practice. Machine learning (ML) has been increasingly used to predict specific ADEs using electronic health record (EHR) data. This systematic review provides a comprehensive overview of the application of ML in predicting specific ADEs based on EHR data.MethodsA systematic search of PubMed, Web of Science, Embase, and IEEE Xplore was conducted to identify relevant articles published from the inception to 20 May 2024. Studies that developed ML models for predicting specific ADEs or ADEs associated with particular drugs were included using EHR data.ResultsA total of 59 studies met the inclusion criteria, covering 15 drugs and 15 ADEs. In total, 38 machine learning algorithms were reported, with random forest (RF) being the most frequently used, followed by support vector machine (SVM), eXtreme gradient boosting (XGBoost), decision tree (DT), and light gradient boosting machine (LightGBM). The performance of the ML models was generally strong, with an average area under the curve (AUC) of 76.68% ± 10.73, accuracy of 76.00% ± 11.26, precision of 60.13% ± 24.81, sensitivity of 62.35% ± 20.19, specificity of 75.13% ± 16.60, and an F1 score of 52.60% ± 21.10. The combined sensitivity, specificity, diagnostic odds ratio (DOR), and AUC from the summary receiver operating characteristic (SROC) curve using a random effects model were 0.65 (95% CI: 0.65–0.66), 0.89 (95% CI: 0.89–0.90), 12.11 (95% CI: 8.17–17.95), and 0.8069, respectively. The risk factors associated with different drugs and ADEs varied.DiscussionFuture research should focus on improving standardization, conducting multicenter studies that incorporate diverse data types, and evaluating the impact of artificial intelligence predictive models in real-world clinical settings.Systematic Review Registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42024565842, identifier CRD42024565842.

Diagnostic Accuracy of Optic Nerve Sheath Diameter Measurement by Ultrasonography for Noninvasive Estimation of Intracranial Hypertension in Traumatic Brain Injury: A Systematic Review and Meta-Analysis.

Intracranial hypertension (IH) is associated with an unfavorable outcome in traumatic brain injury (TBI), and management strategies guided by intracranial pressure monitoring improve prognosis. Owing to the limitations of using invasive devices, measurement of optic nerve sheath diameter (ONSD) by ultrasonography is an alternative noninvasive method. However, its accuracy has not been validated in patients with TBI, so we aim to determine the diagnostic accuracy of measuring ONSD by ultrasonography in patients with TBI to estimate IH, compared with invasive monitoring. Systematic review of electronic databases and manual literature review from inception to June 2023. The analysis included diagnostic accuracy studies of ultrasonographic measurement of ONSD compared with invasive monitoring published in any language and with patients of any age. A qualitative synthesis was performed describing the clinical and methodological characteristics, strengths, limitations, and quality of evidence. In addition, a bivariate random effects model meta-analysis and a hierarchical summary receiver operating characteristics model were performed for the pediatric and adult population separately. Five hundred and forty eight patients of 688 in 16 eligible studies were adults and 120 were children. Pooled sensitivity and specificity of ONSD measurement by ultrasonography were 84% (95% CI, 76%-89%) and 83% (95% CI, 73%-90%), respectively. During the sensitivity analysis, these parameters exhibited consistent values. Pooled area under the curve was 0.91 for adults and 0.76 for children. Optimal threshold for estimating IH was 5.76 mm for adults and 5.78 mm for children. Measurement of ONSD by ultrasonography is a reliable, low-cost, and safe alternative for the estimation of IH with TBI in adults. More robust studies are needed to overcome the high risk of bias and heterogeneity for this analysis.

The performance of machine learning for predicting the recurrent stroke: a systematic review and meta-analysis on 24,350 patients.

Stroke is a leading cause of death and disability worldwide. Approximately one-third of patients with stroke experienced a second stroke. This study investigates the predictive value of machine learning (ML) algorithms for recurrent stroke. This study was prepared according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline. PubMed, Scopus, Embase, and Web of Science (WOS) were searched until January 1, 2024. The quality assessment of studies was conducted using the QUADAS-2 tool. The diagnostic meta-analysis was conducted to calculate the pooled sensitivity, specificity, diagnostic accuracy, positive and negative diagnostic likelihood ratio (DLR), diagnostic accuracy, diagnostic odds ratio (DOR), and area under of the curve (AUC) by the MIDAS package in STATA V.17. Twelve studies, comprising 24,350 individuals, were included. The meta-analysis revealed a sensitivity of 71% (95% CI 0.64-0.78) and a specificity of 88% (95% confidence interval (CI) 0.76-0.95). Positive and negative DLR were 5.93 (95% CI 3.05-11.55) and 0.33 (95% CI 0.28-0.39), respectively. The diagnostic accuracy and DOR was 2.89 (95% CI 2.32-3.46) and 18.04 (95% CI 10.21-31.87), respectively. The summary ROC curve indicated an AUC of 0.82 (95% CI 0.78-0.85). ML demonstrates promise in predicting recurrent strokes, with moderate to high sensitivity and specificity. However, the high heterogeneity observed underscores the need for standardized approaches and further research to enhance the reliability and generalizability of these models. ML-based recurrent stroke prediction can potentially augment clinical decision-making and improve patient outcomes by identifying high-risk patients.

Diagnostic performance of artificial intelligence in detecting oral potentially malignant disorders and oral cancer using medical diagnostic imaging: a systematic review and meta-analysis

ObjectiveOral cancer is a widespread global health problem characterised by high mortality rates, wherein early detection is critical for better survival outcomes and quality of life. While visual examination is the primary method for detecting oral cancer, it may not be practical in remote areas. AI algorithms have shown some promise in detecting cancer from medical images, but their effectiveness in oral cancer detection remains Naïve. This systematic review aims to provide an extensive assessment of the existing evidence about the diagnostic accuracy of AI-driven approaches for detecting oral potentially malignant disorders (OPMDs) and oral cancer using medical diagnostic imaging.MethodsAdhering to PRISMA guidelines, the review scrutinised literature from PubMed, Scopus, and IEEE databases, with a specific focus on evaluating the performance of AI architectures across diverse imaging modalities for the detection of these conditions.ResultsThe performance of AI models, measured by sensitivity and specificity, was assessed using a hierarchical summary receiver operating characteristic (SROC) curve, with heterogeneity quantified through I2 statistic. To account for inter-study variability, a random effects model was utilized. We screened 296 articles, included 55 studies for qualitative synthesis, and selected 18 studies for meta-analysis. Studies evaluating the diagnostic efficacy of AI-based methods reveal a high sensitivity of 0.87 and specificity of 0.81. The diagnostic odds ratio (DOR) of 131.63 indicates a high likelihood of accurate diagnosis of oral cancer and OPMDs. The SROC curve (AUC) of 0.9758 indicates the exceptional diagnostic performance of such models. The research showed that deep learning (DL) architectures, especially CNNs (convolutional neural networks), were the best at finding OPMDs and oral cancer. Histopathological images exhibited the greatest sensitivity and specificity in these detections.ConclusionThese findings suggest that AI algorithms have the potential to function as reliable tools for the early diagnosis of OPMDs and oral cancer, offering significant advantages, particularly in resource-constrained settings.Systematic Review Registrationhttps://www.crd.york.ac.uk/, PROSPERO (CRD42023476706).

Diagnostic value of circulating IL-6 in adult sepsis: a meta-analysis.

In recent years, numerous studies have been published on the diagnostic performance of circulating IL-6 levels in adult sepsis, but the conclusions are not consistent. This meta-analysis aims to systematically evaluate the accuracy of circulating IL-6 in the diagnosis of adult sepsis. Literature on the diagnostic value of circulating IL-6 in adult sepsis published on CNKI, Wanfang Data, PubMed, Embase, Cochrane Library, and Web of Science were searched up to December 2023. QUADAS-2 scale was used to evaluate the quality of the included literature. Stata 15.0 statistical software was used for data processing. The pooled sensitivity, specificity, diagnostic odds ratio (DOR), positive likelihood ratio (PLR), and negative likelihood ratio (NLR) were calculated. The summary receiver operating characteristic curve (SROC) was drawn, with the area under the curve (AUC) calculated. A total of 31 articles were included in this meta-analysis. The results of the meta-analysis showed that the combined sensitivity, specificity, DOR, PLR, and NLR of circulating IL-6 for the diagnosis of adult sepsis were 0.74 (95%CI: 0.69-0.78), 0.73 (95%CI: 0.68-0.78), 7.71 (95%CI: 5.66-10.52), 2.77 (95%CI: 2.29-3.35), 0.36 (95%CI: 0.30-0.43), respectively. The AUC was 0.80 (95%CI: 0.76-0.83). Circulating IL-6 has a good diagnostic value in adult sepsis, with moderate sensitivity and specificity. Therefore, IL-6 can provide a basis for the diagnosis of adult sepsis.

The Evidence Base for Circulating Tumor DNA-Methylation in Non-Small Cell Lung Cancer: A Systematic Review and Meta-Analysis.

Background: Non-Small Cell Lung Cancer (NSCLC) remains a challenging disease to manage with effectiveness. Early detection and precise monitoring are crucial for improving patient outcomes. Circulating tumor DNA (ctDNA) offers a non-invasive cancer detection and monitoring method. Emerging biomarkers, such as ctDNA methylation, have shown promise in enhancing diagnostic accuracy and prognostic assessment in NSCLC. In this review, we examined the current evidence regarding ctDNA methylation's role in NSCLC detection through a systematic review of the existing literature and meta-analysis. Methods: We systematically searched PubMed, Medline, Embase, and Web of Science databases up to 26 June 2024 for studies on the role of ctDNA methylation analysis in NSCLC patients. We included studies from 2010 to 2024 on NSCLC patients. We excluded case reports, non-English articles, studies on cell lines or artificial samples, those without cfDNA detection, prognostic studies, and studies with non-extractable data or mixed cancer types. Funnel plots were visually examined for potential publication bias, with a p value < 0.05 indicating bias. Meta-analysis was conducted using R packages (meta, forestplot, and mada). Combined sensitivity, specificity, positive likelihood ratio (LR+), negative likelihood ratio (LR-), positive and negative predictive values, diagnostic odds ratio (DOR), and 95% confidence intervals (95% CI) were calculated. A summary receiver operating characteristic curve (SROC) and area under the curve (AUC) with related Standard Error (SE) were used to evaluate the overall diagnostic performance. Additionally, RASSF1A, APC, SOX17, SEPT9, and RARβ2 were analyzed, since their methylation was assessed in two or more studies. Results: From 38 candidate papers, we finally identified 12 studies, including 472 NSCLC patients. The pooled sensitivity was 0.62 (0.47-0.77) and the specificity was 0.90 (0.85-0.94). The diagnostic odds ratio was 15.6 (95% CI 9.36-26.09) and the area under the curve was 0.249 (SE = 0.138). The positive and negative predictive values were 5.38 (95% CI 3.89-7.44) and 0.34 (95% CI 0.22-0.54), respectively. For single genes, the specificity reached 0.83~0.96, except for RARβ2, but the sensitivity was relatively low for each gene. Significant heterogeneity across the included studies, the potential publication bias for specificity (p = 0.0231), and the need to validate the clinical utility of ctDNA methylation for monitoring treatment response and predicting outcomes in NSCLC patients represent the main limitations of this study. Conclusions: These results provide evidence of the significant potential of ctDNA methylation as a valuable biomarker for improving the diagnosis of NSCLC, advocating for its integration into clinical practice to enhance patient management.

Deep learning approach to predict autism spectrum disorder: a systematic review and meta-analysis

BackgroundThe use of the deep learning (DL) approach has been suggested or applied to identify childhood autism spectrum disorder (ASD). The capacity to predict ASD, however, differs across investigations. Our study’s objective was to conduct a meta-analysis to determine the DL for ASD in children’s classification accuracy.MethodsEligibility criteria were designed according to the purpose of the meta-analysis; PubMed, EMBASE, Cochrane Library, and Web of Science Database were searched for articles published up to April 16, 2023, on the accuracy of DL methods for ASD classification. Using the Revised Tool for the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) to assess the quality of the included studies. Sensitivity, specificity, areas under the curve (AUC), summary receiver operating characteristic (SROC), and corresponding 95% confidence intervals (CIs) were compiled by using the bivariate random-effects models.ResultsA total of 11 predictive trials based on DL models were included, involving 9495 ASD patients from 6 different databases. According to bivariate random-effects models’ results, the overall sensitivity, specificity, and AUC of the DL technique for ASD were, 0.95 (95% CI = 0.88–0.98), 0.93 (95% CI = 0.85–0.97), and 0.98 (95%CI: 0.97–0.99), respectively. Subgroup analysis results found that different datasets did not cause heterogeneity (meta-regression P = 0.55). The Kaggle dataset’s sensitivity and specificity were 0.94 (95%CI: 0.82-1.00) and 0.91 (95%CI: 0.76-1.00), and with 0.97 (95%CI: 0.92-1.00) and 0.97 (95%CI: 0.92-1.00) for ABIDE dataset.ConclusionsDL techniques has satisfactory sensitivity, specificity, and AUC in ASD classification. However, the major heterogeneity of the included studies limited the effectiveness of this meta-analysis. Further trials need to be performed to demonstrate the clinical practicability of DL diagnosis.

Accuracy of MRI in detecting seminal vesicle invasion in prostate cancer: a systematic review and meta-analysis.

To determine the diagnostic test accuracy of multiparametric magnetic resonance imaging (mpMRI) in detecting seminal vesicle invasion (SVI). The Medical Literature Analysis and Retrieval System Online (MEDLINE), PubMed, the Excerpta Medica dataBASE (EMBASE) and Cochrane databases were search up to May 2023. We included studies that investigated the accuracy of mpMRI in detecting SVI when compared to radical prostatectomy specimens as the reference standard. Data extraction was performed by two independent reviewers to construct 2 × 2 tables, as well as patient and study characteristics. The methodological quality of the included studies was assessed with the Quality of Assessment of Diagnostic Accuracy Studies-2 tool. Sensitivity and specificity were pooled and presented graphically with summary receiver operator characteristic (SROC) plots. A total of 27 articles with 4862 patients were included for analysis. The summary sensitivity and specificity were 0.57 (95% confidence interval [CI] 0.45-0.68) and 0.95 (95% CI 0.92-0.99), respectively. Meta-regression indicated that there was no evidence that coil strength (P = 0.079), coil type (P = 0.589), year of publication (P = 0.503) or use of the Prostate Imaging-Reporting and Data System (P = 0.873) significantly influenced these results. The summary diagnostic odds ratio was 28.3 (95% CI 15.0-48.8) and the area under the curve for the SROC curve was 0.87. The I2 statistic was a modest 11.9%. In general, methodological quality was good. The use of mpMRI in detecting SVI has excellent specificity but poor sensitivity. Both endorectal coils and magnetic field strength do not significantly impact the accuracy of MRI. These findings suggest that mpMRI cannot reliably rule out SVI in patients with prostate cancer.

Radiomics based on MRI in predicting lymphovascular space invasion of cervical cancer: a meta-analysis.

The objective of this meta-analysis is to assess the efficacy of radiomics techniques utilizing magnetic resonance imaging (MRI) for predicting lymphovascular space invasion (LVSI) in patients with cervical cancer (CC). A comprehensive literature search was conducted in databases including PubMed, Embase, Cochrane Library, Medline, Scopus, CNKI, and Wanfang, with studies published up to 08/04/2024, being considered for inclusion. The meta-analysis was performed using Stata 15 and Review Manager 5.4. The quality of the included studies was evaluated using the Quality Assessment of Diagnostic Accuracy Studies 2 and Radiomics Quality Score tools. The analysis encompassed the pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio (DOR). Summary ROC curves were constructed, and the AUC was calculated. Heterogeneity was investigated using meta-regression. Statistical significance was set at p ≤ 0.05. There were 13 studies involving a total of 2,245 patients that were included in the meta-analysis. The overall sensitivity and specificity of the MRI-based model in the Training set were 83% (95% CI: 77%-87%) and 72% (95% CI: 74%-88%), respectively. The AUC, DOR, PLR, and NLR of the MRI-based model in the Training set were 0.89 (95% CI: 0.86-0.91), 22 (95% CI: 12-40), 4.6 (95% CI: 3.1-7.0), and 0.21 (95% CI: 0.16-0.29), respectively. Subgroup analysis revealed that the AUC of the model combining radiomics with clinical factors [0.90 (95% CI: 0.87-0.93)] was superior to models based on T2-weighted imaging (T2WI) sequence [0.78 (95% CI: 0.74-0.81)], contrast-enhanced T1-weighted imaging (T1WI-CE) sequence [0.85 (95% CI: 0.82-0.88)], and multiple sequences [0.86 (95% CI: 0.82-0.89)] in the Training set. The pooled sensitivity and specificity of the model integrating radiomics with clinical factors [83% (95% CI: 73%-89%) and 86% (95% CI: 73%-93%)] surpassed those of models based on the T2WI sequence [79% (95% CI: 71%-85%) and 72% (95% CI: 67%-76%)], T1WI-CE sequence [78% (95% CI: 67%-86%) and 78% (95% CI: 68%-86%)], and multiple sequences [78% (95% CI: 67%-87%) and 79% (95% CI: 70%-87%)], respectively. Funnel plot analysis indicated an absence of publication bias (p > 0.05). MRI-based radiomics demonstrates excellent diagnostic performance in predicting LVSI in CC patients. The diagnostic performance of models combing radiomics and clinical factors is superior to that of models utilizing radiomics alone. https://www.crd.york.ac.uk/PROSPERO/#myprospero, identifier CRD42024538007.

Diagnostic Accuracy of PSMA PET-Guided Prostate Biopsy in Prostate Cancer—A Systematic Review and Meta-analysis

Purpose Early diagnosis and treatment of prostate cancer (PC) are crucial for effective management and improved patient outcomes. Newer imaging modalities like prostate-specific membrane antigen PET have shown superior diagnostic performance in detecting PC and clinically significant PC (csPC). This systematic review and meta-analysis aims to synthesize evidence on the diagnostic performance of PSMA PET-guided prostate biopsy in detecting PC and csPC. Patients and Methods The study followed the PRISMA-DTA guidelines. Using a predefined search strategy, 3 databases (PubMed, Embase, and Web of Science) were systematically searched using appropriate keywords. A meta-analysis was conducted using diagnostic accuracy parameters of the included studies. Risk of bias assessment was done using the QUADAS-2 tool. Results Out of 378 articles, 20 were assessed for full-text screening and 10 articles with 874 patients were finally included. Eight studies reported a pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio of 0.90 (95%confidence interval [CI], 0.82–0.95), 0.93 (95% CI, 0.57–0.99), 12.3 (95% CI, 1.5–98.9), 0.10 (95% CI, 0.05–0.20), and 117 (95% CI, 12–1178), respectively, for detecting PC using PSMA PET-guided prostate biopsy with an area under the summary receiver operating characteristics curve of 0.94 (95% CI, 0.92–0.96). Similarly, 6 studies reported a pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio of 0.89 (95% CI, 0.82–0.94), 0.65 (95% CI, 0.49–0.79), 2.6 (95% CI, 1.6–4.1), 0.17 (95% CI, 0.09–0.31), and 15 (95% CI, 6–41), respectively, for detecting csPC using PSMA PET-guided prostate biopsy with area under summary receiver operating characteristics curve of 0.86 (95% CI, 0.82–0.88). Conclusions PSMA PET-guided prostate biopsy has a high diagnostic accuracy in detecting PC and csPC in patients with clinical suspicion of PC, and provides a 1-stop solution for early diagnosis and staging of PC.

The Value of lncRNAs as a Biomarker for the Diagnosis of Gestational Diabetes: A Meta-Analysis.

Gestational diabetes mellitus (GDM) is a common metabolic disorder in pregnancy and leads to serious harm to the mother and the fetus. A variety of lncRNAs play a key role in GDM. This meta-analysis was performed to explore the potential value of lncRNAs in GDM diagnosis. Articles correlated with lncRNA and GDM were screened from Embase, Medline, EBSCO, PubMed, Chinese National Knowledge Infrastructure, and WanFang databases. Summary receiver operator characteristic (SROC) was performed to evaluate the pooled area under curve (AUC). Forest plot was conducted to calculate the sensitivity, specificity, diagnostic likelihood ratio (LR), diagnostic score, and diagnostic odds ratio (DOR). Deeks' funnel plot was utilized to evaluate the publication bias. Eleven articles containing 12 tests (1060 GDM patients and 1066 controls) were included in this meta-analysis. AUC (0.89, 95%CI=0.86-0.92), sensitivity (0.84, 95%CI=0.80-0.87), and specificity (0.81, 95%CI=0.77-0.85)of the SROC curve showed a high diagnostic value of lncRNA for GDM. Positive LR (PLR 4.40, 95%CI=3.45-5.60) and negative LR (NLR 0.20, 95%CI=0.15-0.26) results indicated that the diagnosis of lncRNA for GDM had low clinical utility. Diagnostic score (3.09, 95%CI=2.62-3.57) and DOR (22.04, 95%CI=13.68-35.51) results suggested lncRNAs have good discriminative effect on GDM. Heterogeneity was significantly higher, but not induced by the subgroups. LncRNAs had high diagnostic value and good discriminative effect for GDM, but the clinical utility was not high. This meta-analysis study offers a potential target for GDM diagnosis.

Meta-analysis on the Diagnostic Value of SDC2 Methylation in Colorectal Cancer Screening Among Chinese

To systematically evaluate the diagnostic value of syndecan-2 (SDC2) gene methylation status in the detection of Colorectal cancer (CRC) and advanced adenomas (AA) through meta-analysis. Databases including PubMed, Web of Science were searched from 2020 to June 14, 2024. Relevant studies were selected based on predefined inclusion and exclusion criteria. The true positive (TP), false positive (FP), false negative (FN), and true negative (TN) results were calculated for each study. The quality of the included studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool. A bivariate meta-analysis model was used to pool the sensitivity, specificity, diagnostic odds ratio (DOR), positive likelihood ratio (PLR), and negative likelihood ratio (NLR), and a summary receiver operating characteristic (SROC) curve was constructed. Spearman’s correlation coefficient, Cochran-Q test, and I² test were used to evaluate heterogeneity. Sensitivity analysis was performed to explore the potential sources of heterogeneity. 24 literatures were included for meta-analysis. The pooled sensitivity was 0.77 for CRC and 0.52 for AA, and the pooled specificity was 0.93 for CRC and 0.91 for AA. The pooled PLR was 10.63 for CRC and 5.57 for AA, and the pooled NLR was 0.25 for CRC and 0.53 for AA. The DOR was 43.21 for CRC and 10.47 for AA. The SROC AUC was 0.91 for CRC and 0.83 for AA. The methylation level of the SDC2 gene in stool samples has significant diagnostic value for colorectal cancer and is considered an ideal biomarker for the early detection of this disease.

Can artificial intelligence aid the urologists in detecting bladder cancer?

ABSTRACT Introduction: The emergence of artificial intelligence (AI)-based support system endoscopy, including cystoscopy, has shown promising results by training deep learning algorithms with large datasets of images and videos. This AI-aided cystoscopy has the potential to significantly transform the urological practice by assisting the urologists in identifying malignant areas, especially considering the diverse appearance of these lesions. Methods: Four databases, the PubMed, ProQuest, EBSCOHost, and ScienceDirect were searched, along with a manual hand search. Prospective and retrospective studies, experimental studies, cross-sectional studies, and case–control studies assessing the utilization of AI for the detection of bladder cancer through cystoscopy and comparing with the histopathology results as the reference standard were included. The following terms and their variants were used: “artificial intelligence,” “cystoscopy,” and “bladder cancer.” The risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. A random effects model was used to calculate the pooled sensitivity and specificity. The Moses–Littenberg model was used to derive the Summary Receiver Operating Characteristics (SROC) curve. Results: Five studies were selected for the analysis. Pooled sensitivity and specificity were 0.953 (95% confidence interval [CI]: 0.908–0.976) and 0.957 (95% CI: 0.923–0.977), respectively. Pooled diagnostic odd ratio was 449.79 (95% CI: 12.42–887.17). SROC curve (area under the curve: 0.988, 95% CI: 0.982–0.994) indicated a strong discriminating power of AI-aided cystoscopy in differentiation normal or benign bladder lesions from the malignant ones. Conclusions: Although the utilization of AI for aiding in the detection of bladder cancer through cystoscopy remains questionable, it has shown encouraging potential for enhancing the detection rates. Future studies should concentrate on identification of the patients groups which could derive maximum benefit from accurate identification of the bladder cancer, such as those with intermediate or high-risk invasive tumors.

Contrast-enhanced endoscopic ultrasound for differential diagnosis of autoimmune pancreatitis: a meta-analysis.

Background and study aims To assess the diagnostic value of contrast-enhanced endoscopic ultrasound (CE-EUS) for autoimmune pancreatitis and other solid pancreatic masses. Patients and methods A systematic search of PubMed, Embase, and Web of Science was performed from inception to October 2022. We calculated individual and pooled sensitivities and specificities to determine the diagnostic ability of CE-EUS. In addition, we calculated I 2 to test for heterogeneity and explored the source of heterogeneity by meta-regression analysis. Results A total of 472 patients from seven eligible studies were included. The mean sensitivity and specificity of the Bivariate analysis were 0.84 (95% CI 0.71-0.92) and 0.95 (95% CI 0.84-0.99), respectively. The diagnostic advantage ratio was 107.91 (95% confidence interval [CI] 22.22-524.13), and the area under the summary receiver operating characteristics curve was 0.91 (95% CI 0.88-0.93). The overall heterogeneity of the studies is negligible (I 2 =0, 95% CI 0-100). However, notable heterogeneity was observed in the combined specificity ( P <0.01, I 2 =74.82) and diagnostic odds ratio ( P =0.05, I 2 =51.54). The heterogeneity in these aspects could be elucidated through sensitivity analysis. Conclusions Our analysis showed that CE-EUS is useful in identifying autoimmune pancreatitis. However, further large sample size, multicenter, prospective studies are needed to demonstrate its utility.

Diagnostic value of a magnetic resonance imaging (MRI)-based vertebral bone quality score for bone mineral density assessment: an updated systematic review and meta-analysis.

Many studies have explored the application of the magnetic resonance imaging (MRI)-based vertebral bone quality (VBQ) score as a tool for opportunistic osteoporosis screening before spine surgery. We aimed to conduct a systematic review and meta-analysis of studies to determine the diagnostic value of the VBQ score for bone mineral density (BMD) assessment. The PubMed, EBSCO, Ovid, Web of Science, Cochrane Library, Wanfang, China National Knowledge Infrastructure, Chinese Science and Technology Periodical databases were searched to retrieve original studies on VBQ and BMD published from inception to 31 December 2023 with no restrictions on language and the reference standard of BMD. The quality of the included articles was assessed using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2). Summary accuracy metrics were obtained by bivariate random-effects meta-analysis. The area under the curve (AUC) of the summary receiver operating characteristic (SROC) analysis was used to evaluate the performance of the VBQ score. Meta-regression and subgroup analyses were performed to examine the source of heterogeneity. In total, 23 studies, comprising 2981 patients, from 2022 to 2023, of high- or medium-to-high scientific quality were included in the meta-analysis. The pooled sensitivity, specificity, and diagnostic odds ratio (DOR) of the VBQ score in the assessment of BMD were 0.77 [95% confidence interval (CI), 0.73-0.81; P<0.001], 0.65 (95% CI, 0.59-0.71; P<0.001), and 6.49 (95% CI, 4.82-8.73; P<0.001), respectively, with an area under the SROC curve of 0.78 (95% CI, 0.74-0.82; P<0.001). The presence of heterogeneity was found to have a significant association with factors such as the reference standard, sex, mean age, region of publication, VBQ method, and magnet field strength. The MRI-based VBQ score has some diagnostic value in detecting osteoporosis. The VBQ score could be used as a tool for opportunistic osteoporosis screening before spine surgery.

Diagnostic Accuracy of Neuromuscular Ultrasound vs. Electrodiagnostic Studies for Carpal Tunnel Syndrome: Systematic Review and Meta-analysis of Paired Accuracy Studies.

This systematic review with meta-analysis compared the diagnostic accuracy of neuromuscular ultrasound (NMUS) and electrodiagnostic studies (EDX) as confirmatory tests for carpal tunnel syndrome (CTS) diagnosis. We used bivariate random-effects models to estimate pooled sensitivity and specificity and generate hierarchical summary receiver-operating characteristic curves to assess diagnostic test accuracy. Nine paired accuracy studies were included, representing 1751 hands (743 clinically diagnosed CTS; 1008 without CTS) that underwent NMUS and EDX. Compared to the clinical diagnosis reference standard, the pooled sensitivity was 86.4% for NMUS and 91.6% for EDX. Pooled specificity was 79.3% for NMUS and 81.9% for EDX. The positive likelihood ratios were 4.2 and 5.1 for NMUS and EDX, respectively, and the negative likelihood ratios were 0.17 and 0.10, respectively. The diagnostic odds ratio was 24 for NMUS and 49 for EDX. No statistically significant differences were identified between NMUS and EDX for sensitivity, specificity, or overall diagnostic accuracy. Overall, the diagnostic accuracy of NMUS is similar to that of EDX for CTS diagnosis, with high sensitivity and moderate specificity for each. The choice between these confirmatory diagnostic tests should incorporate shared decision-making between patients and providers that weighs diagnostic accuracy as well as factors such as patient preferences, test availability, cost, and tolerability.

Role of Lung Ultrasound in the Detection of Lung Sequelae in Post-COVID-19 Patients: A Systematic Review and Meta-Analysis.

Background: During the COVID-19 pandemic, several studies demonstrated the effectiveness of lung ultrasound (LUS) as a frontline tool in diagnosing and managing acute SARS-CoV-2 pneumonia. However, its role in detecting post-COVID-19 lung sequelae remains to be fully determined. This study aims to evaluate the diagnostic accuracy of LUS in identifying lung parenchymal damage, particularly fibrotic-like changes, following COVID-19 pneumonia, comparing its performance to that of CT. Methods: Relevant studies published before July 2024 were identified through a comprehensive search of PubMed, Embase, and Cochrane library. The search terms were combinations of the relevant medical subject heading (MeSH) terms, key words and word variants for "lung", "post-COVID", "long-COVID", and "ultrasound". The pooled sensitivity, specificity, diagnostic odds ratio (DOR), and summary receiver-operating characteristic (SROC) curve were used to examine the accuracy of CEUS. The selected works used different thresholds for the detection and counting of B-lines by ultrasound. This led to dividing our analysis into two models, the first based on the lower thresholds for detection of B-lines found in the works, and the second on data obtained using a higher detection threshold. Results: In terms of the diagnostic accuracy of LUS in detecting residual fibrotic-like changes in patients post-COVID-19 infection, a low-threshold model displayed a pooled sensitivity of 0.98 [95% confidence interval (CI): 0.95-0.99] and a pooled specificity of 0.54 (95% CI: 0.49-0.59). The DOR was 44.9 (95% CI: 10.8-187.1). The area under the curve (AUC) of SROC was 0.90. In the second analysis, the model with the higher threshold to detect B-lines showed a pooled sensitivity of 0.90 (95% CI: 0.85-0.94) and a pooled specificity of 0.88 (95% CI: 0.84-0.91). The DOR was 50.4 (95% CI: 15.9-159.3). The AUC of SROC was 0.93. Conclusions: In both analyses (even using the high threshold for the detection of B-lines), excellent sensitivity (98% in model 1 and 90% in model 2) is maintained. The specificity has a significant variation between the two models from 54 (model 1) to 87% (model 2). The model with the highest threshold for the detection of B-lines displayed the best diagnostic accuracy, as confirmed by the AUC values of the SROC (0.93).

Secretory Phospholipase A2 as a Promising Biomarker for Predicting Acute Chest Syndrome in Children With Sickle Cell Disease: A Systematic Review and Meta-Analysis.

Acute chest syndrome (ACS) is a severe and potentially life-threatening complication of sickle cell disease (SCD). Early identification of patients at risk for ACS is crucial for timely intervention. There is a potential association between ACS and elevated levels of secretory phospholipase A2 (sPLA2), an enzyme involved in the breakdown of phospholipids. sPLA2 has emerged as a promising biomarker for predicting ACS. This systematic review and meta-analysis aimed to assess the diagnostic value of PLA2 in predicting ACS among children with SCD. A comprehensive search was conducted across multiple databases, including MEDLINE, Embase, Cochrane Library, PubMed, and Web of Science. Studies assessing the relationship between sPLA2 levels and ACS in SCD patients were included. Pooled sensitivity, specificity, likelihood ratios, and the area under the receiver operating characteristic curve (AUC) were calculated to assess sPLA2's diagnostic accuracy. There is a potential association between significant association between elevated sPLA2 levels and increased ACS risk in SCD patients. The pooled sensitivity of sPLA2 for predicting ACS was 0.766 (95% CI: 0.620-0.877), with a pooled specificity of 0.736 (95% CI: 0.680-0.787). The AUC of the summary receiver operating characteristic (SROC) curve was 0.84, indicating good discriminatory ability. sPLA2 emerges as a promising biomarker for predicting ACS in SCD patients, potentially guiding risk stratification and early intervention strategies to enhance patient outcomes. Nonetheless, further prospective studies are warranted to validate its clinical utility and standardize sPLA2 assay protocols.

Meta-analysis of the diagnostic value of functional magnetic resonance imaging for distinguishing unresponsive wakefulness syndrome/vegetative state and minimally conscious state.

Unresponsive wakefulness syndrome/vegetative state (UWS/VS) and minimally conscious state (MCS) are considered different clinical entities, but their differential diagnosis remains challenging. As a potential clinical tool, functional magnetic resonance imaging (fMRI) could detect residual awareness without the need for the patients' actual motor responses. This study aimed to investigate the diagnostic value of fMRI for distinguishing between UWS/VS and MCS through a meta-analysis of the existing studies. We conducted a comprehensive search (from the database creation date to November. 2023) for relevant English articles on fMRI for the differential diagnosis of UWS/VS and MCS. The pooled sensitivity, specificity, positive likelihood ratio (LR+), negative likelihood ratio (LR-), summary receiver operating characteristic (SROC) curve, and area under the curve (AUC) were calculated to assess the diagnostic value of fMRI in distinguishing between UWS/VS and MCS. The statistical I 2 test was used to assess heterogeneity, and the source of heterogeneity was investigated by performing a meta-regression analysis. Publication bias was assessed using the Deeks funnel plot asymmetry test. Ten studies were included in the meta-analysis. The pooled sensitivity and specificity were 0.71 (95% CI 0.62-0.79) and 0.71 (95% CI 0.54-0.84), respectively. The fMRI for the differential diagnosis of UWS/VS and MCS has a moderate positive likelihood ratio (2.5) and a relatively low negative likelihood ratio (0.40). Additionally, SROC curves showed that the AUC was 0.76 (95% CI 0.72-0.80). Functional magnetic resonance imaging has a good performance in the differential diagnosis of UWS/VS and MCS, and may provide a potential tool for evaluating the prognosis and guiding the rehabilitation therapy in patients with disorders of consciousness.

Systematic review and meta-analysis of diagnostic test accuracy in chronic wound's microbiology.

This study aims to assess the diagnostic accuracy of non-culture-based methodologies for detecting microorganisms in chronic wounds. We systematically reviewed studies that evaluated the diagnostic accuracy of alternative tests in chronic wound samples, excluding studies focused on animal samples or unrelated conditions. The search encompassed PubMed, CINAHL, Scopus and Web of Science databases, employing the QUADAS-2 tool for risk of bias assessment. Our search included the PubMed, CINAHL, Scopus and Web of Science databases, and we assessed the risk of bias using the QUADAS-2 tool. A meta-analysis was conducted on polymerase chain reaction (PCR) and colorimetric methods to determine sensitivity, specificity, diagnostic odds ratio, and summary receiver-operating characteristic (sROC) curves using a random-effects model. For methods not suitable for quantitative synthesis, a narrative synthesis was performed. Nineteen studies involving various types of chronic wounds were analysed, revealing diverse diagnostic methods including fluorescence, PCR, colorimetry, voltammetry, electronic nose, biosensors, enzymatic methods, staining and microscopy. Combining fluorescence with clinical signs and symptoms (CSS) versus culture showed significant accuracy. Colorimetry demonstrated low sensitivity but high specificity, with a diagnostic odds ratio of 6.3. PCR generally exhibited good accuracy, although significant heterogeneity was noted, even in subgroup analyses. This study identified a broad spectrum of diagnostic approaches, highlighting the superior diagnostic accuracy achieved when microbiological analysis is combined with clinical assessments. However, the heterogeneity and methodological variations across studies present challenges in meta-analysis. Future research should aim for standardized and homogeneous study designs to enhance the assessment of diagnostic accuracy for alternative methods.