To evaluate the diagnostic accuracy of reduced montage electroencephalography (EEG) for seizure detection and provide evidence-based recommendations.

Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we conducted a diagnostic meta-analysis to assess the sensitivity and specificity of reduced EEG montages in detecting seizure activity. A hierarchical summary receiver operating characteristic curve (HSROC) model was used to estimate the area under the curve (AUC). Subgroup analyses were conducted to identify sources of heterogeneity. Bayesian estimates were used for validation.

Across 8 studies encompassing 3,458 reduced EEG montage samples, all studies used a reduced EEG montage with 7 to 10 electrodes. The pooled sensitivity was 0.75 (95 % CI: 0.68-0.80), and the pooled specificity was 0.97 (95 % CI: 0.95-0.98). The HSROC model had an AUC of 0.96 (95 % CI: 0.93-0.97). Variations in study results were attributed to factors such as the number of electrodes (pooled sensitivity of 0.66 for studies employing <8 leads and 0.77 for studies employing ≥8 leads) and montage design coverage (pooled sensitivity of 0.64 for studies employing subhairline montage and 0.77 for studies employing above-hairline montage). The Bayesian and frequentist findings agreed with each other and had a pooled sensitivity of 0.74 (95 % HPD: 0.65-0.83) and pooled specificity of 0.97 (95 % highest posterior density 0.95-0.98).

Reduced EEG montages with 8 or more electrodes are feasible for seizure detection, especially in emergency settings where rapid detection is crucial.

This study aimed to compare various frailty screening and assessment tools with the Frailty Phenotype (FP), Frailty Index (FI), and Comprehensive Geriatric Assessment (CGA), which are considered the current gold standards, among the Asia-Pacific population in community settings.

Systematic review and meta-analysis.

This review included studies evaluating frailty identification tools based on the criteria of population, index and reference tests, and diagnosis of interest.

A diagnostic test accuracy review was conducted to assess frailty instruments recommended by the Asia-Pacific Clinical Practice Guidelines. Comprehensive electronic database searches and manual searches were conducted up to August 20, 2024. Study quality, including risks of bias and applicability, was assessed using the QUADAS-2 tool. Hierarchical analysis and Youden's index were employed to identify the optimal tool and cutoff points, and pooled frailty prevalence was calculated.

Fourteen studies were included: 10 for the FRAIL scale, 3 for TUG, and 2 for the SOF index (screening tools), and 2 each for the CFS and KCL, and 1 for the REFS (assessment tools). All studies demonstrated a high risk of bias. The pooled sensitivity and specificity for screening tools were 0.63 and 0.89, respectively, whereas for assessment tools, they were 0.79 and 0.85. The pooled prevalence of frailty and pre-frailty was 19.7% and 31.7%, respectively. The pooled diagnostic odds ratios were highest for the FRAIL scale (15.72) and CFS (35.03) among the screening and assessment tools. The subgroup analysis revealed that the setting had no significant impact on screening tool performance (p = 0.58), but a borderline significant effect was observed for assessment tools (p = 0.06), although this result is limited by the small number of studies, with only one conducted in a community setting. The FRAIL scale, with a cutoff of 2, had a Youden's index of 0.60, signifying optimal screening performance.

Among the frailty instruments recommended by the Asia-Pacific Clinical Practice Guidelines, this meta-analysis identifies the FRAIL scale as the most robust tool for distinguishing frailty, with a cutoff of 2 significantly enhancing diagnostic accuracy. Furthermore, the estimated prevalence of frailty in the Asia-Pacific region is 19.7% across various community settings, underscoring the need for further research and the development of validated assessment tools tailored to this population.

Emerging evidence underscores the potential application of artificial intelligence (AI) in discovering noninvasive blood biomarkers. However, the diagnostic value of AI-derived blood biomarkers for ovarian cancer (OC) remains inconsistent.

We aimed to evaluate the research quality and the validity of AI-based blood biomarkers in OC diagnosis.

A systematic search was performed in the MEDLINE, Embase, IEEE Xplore, PubMed, Web of Science, and the Cochrane Library databases. Studies examining the diagnostic accuracy of AI in discovering OC blood biomarkers were identified. The risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies-AI tool. Pooled sensitivity, specificity, and area under the curve (AUC) were estimated using a bivariate model for the diagnostic meta-analysis.

A total of 40 studies were ultimately included. Most (n=31, 78%) included studies were evaluated as low risk of bias. Overall, the pooled sensitivity, specificity, and AUC were 85% (95% CI 83%-87%), 91% (95% CI 90%-92%), and 0.95 (95% CI 0.92-0.96), respectively. For contingency tables with the highest accuracy, the pooled sensitivity, specificity, and AUC were 95% (95% CI 90%-97%), 97% (95% CI 95%-98%), and 0.99 (95% CI 0.98-1.00), respectively. Stratification by AI algorithms revealed higher sensitivity and specificity in studies using machine learning (sensitivity=85% and specificity=92%) compared to those using deep learning (sensitivity=77% and specificity=85%). In addition, studies using serum reported substantially higher sensitivity (94%) and specificity (96%) than those using plasma (sensitivity=83% and specificity=91%). Stratification by external validation demonstrated significantly higher specificity in studies with external validation (specificity=94%) compared to those without external validation (specificity=89%), while the reverse was observed for sensitivity (74% vs 90%). No publication bias was detected in this meta-analysis.

AI algorithms demonstrate satisfactory performance in the diagnosis of OC using blood biomarkers and are anticipated to become an effective diagnostic modality in the future, potentially avoiding unnecessary surgeries. Future research is warranted to incorporate external validation into AI diagnostic models, as well as to prioritize the adoption of deep learning methodologies.

PROSPERO CRD42023481232; https://www.crd.york.ac.uk/PROSPERO/view/CRD42023481232.

Evaluating diagnostic test accuracy during epidemics is difficult due to an urgent need for test availability, changing disease prevalence and pathogen characteristics, and constantly evolving testing aims and applications. Based on lessons learned during the SARS-CoV-2 pandemic, we introduce a framework for rapid diagnostic test development, evaluation, and validation during outbreaks of emerging infections. The framework is based on the feedback loop between test accuracy evaluation, modelling studies for public health decision-making, and impact of public health interventions. We suggest that building on this feedback loop can help future diagnostic test evaluation platforms better address the requirements of both patient care and public health.

Melioidosis is a life-threatening infectious disease caused by the bacterium Burkholderia pseudomallei. Although culture is the gold standard for diagnosing melioidosis, it is time-consuming and delays timely treatment. Non-culture-based diagnostic techniques are interesting alternatives for the rapid detection of melioidosis. This systematic review provides an overview of the performance of antibody-detection tests for melioidosis. A thorough literature search was conducted in two databases to identify relevant studies published until 31 December 2023. Among the 453 studies identified, 29 were included for further analysis. Various antibody-detection methods have been developed, primarily enzyme-linked immunosorbent assays (ELISAs). Recombinant outer membrane protein A-(OmpA)-specific immunoglobulin G (IgG), immunoglobulin A (IgA), immunoglobulin M (IgM), and immunoglobulin D (IgD) exhibited the highest accuracy, with a sensitivity of 95.0% and a specificity of 98.0% in ELISA. Furthermore, immunochromatographic testing has emerged as a promising rapid diagnostic test (RDT), with haemolysin co-regulated protein 1 (Hcp1) demonstrating significant accuracy, a sensitivity of 88.3%, and a specificity of 91.6%. Additionally, IgG against Burkholderia invasion protein D (BipD) showed excellent accuracy, with a sensitivity of 100.0% and a specificity of 100.0% in surface plasmon resonance assay. Combining multiple antigens or employing different detection techniques can enhance the accuracy of melioidosis diagnosis.

D-dimer is the only biomarker currently recommended in guidelines for the diagnosis of acute aortic syndrome (AAS). We undertook a systematic review to determine whether any alternative biomarkers could be useful in AAS diagnosis.

We searched electronic databases (including MEDLINE, EMBASE and the Cochrane Library) from inception to February 2024. Diagnostic studies were eligible if they examined biomarkers other than D-dimer for diagnosing AAS compared with a reference standard test in people presenting to the ED with symptoms of AAS. Case-control studies were identified but excluded due to high risk of bias. Selection of studies, data extraction and risk of bias assessments using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool were undertaken independently by at least two reviewers. We used narrative synthesis to summarise the findings.

We identified 2017 citations, included 13 cohort studies (n=76-999), and excluded 38 case-control studies. Methodological quality was variable, with most included studies having unclear or high risk of bias and applicability concerns in at least one item of the QUADAS-2 tool. Only two studies reported biomarkers with sensitivity and specificity comparable to D-dimer (ie, >90% and >50%, respectively). Wang et al reported 99.1% sensitivity and 84.9% specificity for soluble ST2; however, these findings conflicted with estimates of 58% sensitivity and 70.8% specificity reported in another study. Chun and Siu reported 95.6% sensitivity and 56.1% specificity for neutrophil count, but this has not been confirmed elsewhere.

There are many potential alternative biomarkers for AAS but few have been evaluated in more than one study, study designs are often weak and reported biomarker accuracy is modest or inconsistent between studies. Alternative biomarkers to D-dimer are not ready for routine clinical use.

CRD42022252121.

Diagnosing high-risk varices (HRV) is crucial for determining the prognosis and treatment strategy in patients with hepatocellular carcinoma (HCC). Although the Baveno VI consensus guidelines have been validated for assessing HRV in patients with liver cirrhosis, their applicability to those with HCC remains uncertain. This study aims to evaluate the effectiveness of the Baveno VI criteria in screening for HRV in patients with HCC.

We searched for English-language articles related to Baveno criteria and HCC across PubMed, Embase, Web of Science, and Cochrane databases, covering publications from their inception until April 19, 2024. Our meta-analysis was conducted using STATA 14.0 and Meta-Disc 1.4 software. We assessed the quality of the included studies using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool. We analyzed pooled sensitivity (SEN), specificity (SPE), diagnostic odds ratio (DOR), positive likelihood ratio (LR+), and negative likelihood ratio (LR-) using a random-effects model and constructed a summary receiver operating characteristic (SROC) curve. Based on established consensus, the favorable Baveno VI criteria were defined as a liver stiffness measurement (LSM) < 20 kPa and a platelet count (PLT) > 150×109/L to exclude HRV. This study is registered with PROSPERO under the registration number CRD42024533946.

We finally brought four studies, including 1277 patients with HCC, into this meta-analysis. The SEN, SPE, DOR, and AUC of favorable Baveno VI criteria in screening HRV in patients with HCC were 0.90 (95% CI: 0.81-0.95), 0.33 (95% CI: 0.25-0.41), 4.44 (95% CI: 2.14-9.22), and 0.59 (95% CI: 0.55-0.64), respectively. The LR+ and LR- of the favorable Baveno VI criteria were 1.34 (95% CI: 1.19-1.50) and 0.30 (95% CI: 0.16-0.58), respectively. Subgroup and meta-regression analyses indicated that BCLC and Child-Pugh stages likely contribute to the heterogeneity in the SPE.

The favorable Baveno VI criteria may not effectively screen HRV in patients with HCC. However, the current evidence is insufficient, and further studies with larger sample sizes and detailed patient subgroups are needed.

https://www.crd.york.ac.uk/prospero/, identifier CRD42024533946.

Existing challenges with the early diagnosis of rheumatoid arthritis (RA) and active disease, mainly by non-rheumatologists, have prompted the search for novel biomarkers. Elevations in indices derived from blood cell counts, e.g., the neutrophil-to-lymphocyte ratio (NLR) and the platelet-to-lymphocyte ratio (PLR), have been reported in RA patients. However, their diagnostic accuracy has not been comprehensively assessed. Therefore, we conducted a systematic review and meta-analysis of studies reporting the sensitivity and specificity of the NLR and PLR, obtained by receiver operating characteristic (ROC) curve analysis, for the presence of RA and active disease. We searched electronic databases from inception to 15 March 2024 and assessed the risk of bias using the JBI Critical Appraisal Checklist (PROSPERO registration number: CRD42024533546). In 15 studies, the NLR exhibited acceptable accuracy for the presence of RA (area under the curve, AUC = 0.76, 95% CI 0.72 to 0.80) and active disease (AUC = 0.70, 95% CI 0.66 to 0.74). The PLR exhibited good accuracy for the presence of RA (AUC = 0.80, 95% CI 0.76 to 0.83). There were insufficient studies to assess the accuracy of the PLR for the presence of active disease. Our systematic review and meta-analysis suggests that the NLR and the PLR are promising biomarkers of RA (NLR and PLR) and active disease (NLR). Further research is required to investigate whether the NLR and PLR can significantly enhance the capacity to diagnose RA and active disease in clinical practice.

There is inconsistent evidence regarding the accuracy of GNAS mutations identification for the diagnosis of FD/MAS. This study was performed to estimate the prevalence and diagnostic accuracy of GNAS mutations detection and to preliminarily investigate the genotype-phenotype correlation in FD patients.

Five electronic databases were searched from 1995 to 2024 using search terms related to GNAS and fibrous dysplasia. Observational studies of FD patients undergoing GNAS mutation detection in FD were included.

A total of 878 FD patients were included. The pooled prevalence of GNAS mutations in FD based on the random effects model was 74% (95% CI = 64%-83%). Regarding diagnostic accuracy, a sensitivity of 0.83 (95% CI, 0.65-0.96), specificity of 0.99 (95% CI, 0.98-1.00) and the area under the receiver operating characteristic curve of 98.38% were found. Additionally, meta-analysis and Fisher's test showed the GNAS mutation types were significantly associated with FD types (OR = 3.51, 95% CI = 1.05 to 11.72; p < 0.05).

A high detection rate of GNAS mutations occurred in FD, and its detection is reliable for diagnosing FD. Additionally, GNAS mutation type was types were significantly associated with FD type.

Identifier CRD42024553469.

Natural language processing (NLP) represents one of the adjunct technologies within artificial intelligence and machine learning, creating structure out of unstructured data. This study aims to assess the performance of employing NLP to identify and categorize unstructured data within the emergency medicine (EM) setting.

We systematically searched publications related to EM research and NLP across databases including MEDLINE, Embase, Scopus, CENTRAL, and ProQuest Dissertations & Theses Global. Independent reviewers screened, reviewed, and evaluated article quality and bias. NLP usage was categorized into syndromic surveillance, radiologic interpretation, and identification of specific diseases/events/syndromes, with respective sensitivity analysis reported. Performance metrics for NLP usage were calculated and the overall area under the summary of receiver operating characteristic curve (SROC) was determined.

A total of 27 studies underwent meta-analysis. Findings indicated an overall mean sensitivity (recall) of 82%-87%, specificity of 95%, with the area under the SROC at 0.96 (95% CI 0.94-0.98). Optimal performance using NLP was observed in radiologic interpretation, demonstrating an overall mean sensitivity of 93% and specificity of 96%.

Our analysis revealed a generally favorable performance accuracy in using NLP within EM research, particularly in the realm of radiologic interpretation. Consequently, we advocate for the adoption of NLP-based research to augment EM health care management.

This Bayesian network meta-analysis was performed to analyze the associations between clinicopathological characteristics and BRAF mutations in ameloblastoma (AM) patients and to evaluate the diagnostic accuracy.

Four electronic databases were searched from 2010 to 2024. The search terms used were specific to BRAF and AM. Observational studies or randomized controlled trials were considered eligible. The incidence of BRAF mutation and corresponding clinicopathological features in AM patients were subjected to Bayesian network analyses and diagnostic accuracy evaluation.

A total of 937 AM patients from 20 studies were included. The pooled prevalence of BRAF mutations in AM patients was 72%. According to the Bayesian network analysis, BRAF mutations are more likely to occur in younger (odds ratio [OR], 2.3; credible interval [CrI]: 1.2-4.5), mandible site (OR, 3.6; 95% CrI: 2.7-5.2), and unicystic (OR, 1.6; 95% CrI: 1.1-2.4) AM patients. Similarly, higher diagnostic accuracy was found in the younger, mandible, and unicystic AM groups.

The incidence, risk, and diagnostic accuracy of BRAF mutation in AM were greater in younger patients, those with mandible involvement, and those with unicystic AM than in patients with other clinicopathological features. In addition, there was a strong concordance in the diagnostic accuracy between molecular tests and immunohistochemical analysis.

This study aims to establish a scientific foundation for early detection and diagnosis of silicosis by conducting meta-analysis on the role of single biomarkers in independent diagnosis. The combined sensitivity (Sen), specificity (Spe), positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic score, and diagnostic odds ratio (DOR) were 0.84 (95% confidence interval (CI): 0.77-0.90), 0.83 (95% CI: 0.78-0.88), 5.08 (95% CI: 3.92-6.59), 0.19 (95% CI: 0.13-0.27), 3.31 (95% CI: 2.88-3.74) and 27.29 (95% CI: 17.77-41.91), respectively. The area under the curve (AUC) was 0.90 (95% CI: 0.88-0.93). The Fagan plot shows a positive posterior probability of 82% and a negative posterior probability of 15%. This study establishes an academic basis for the swift identification, mitigation, and control of silicosis through scientific approaches. The assessed biomarkers offer precision and dependability in silicosis diagnosis, opening novel paths for early detection and intervention, thereby mitigating the disease burden associated with silicosis.

To evaluate the diagnostic accuracy of the aortic dissection detection risk score (ADD-RS) used alone or in combination with D-dimer for detecting acute aortic syndrome (AAS) in patients presenting with symptoms suggestive of AAS.

We searched MEDLINE, EMBASE, and the Cochrane Library from inception to February 2024. Additionally, the reference lists of included studies and other systematic reviews were thoroughly searched. All diagnostic accuracy studies that assessed the use of ADD-RS alone or with D-Dimer for diagnosing AAS compared with a reference standard test (e.g. computer tomographic angiography (CTA), ECG-gated CTA, echocardiography, magnetic resonance angiography, operation, or autopsy) were included. Two reviewers independently selected and extracted data. Risk of bias was appraised using QUADAS-2 tool. Data were synthesised using hierarchical meta-analysis models.

We selected 13 studies from the 2017 citations identified, including six studies evaluating combinations of ADD-RS alongside D-dimer>500ng/L. Summary sensitivities and specificities (95% credible interval) were: ADD-RS>0 94.6% (90%, 97.5%) and 34.7% (20.7%, 51.2%), ADD-RS>1 43.4% (31.2%, 57.1%) and 89.3% (80.4%, 94.8%); ADD RS>0 or D-Dimer>500ng/L 99.8% (98.7%, 100%) and 21.8% (12.1%, 32.6%); ADD RS>1 or D-Dimer>500ng/L 98.3% (94.9%, 99.5%) and 51.4% (38.7%, 64.1%); ADD RS>1 or ADD RS = 1 with D-dimer>500ng/L 93.1% (87.1%, 96.3%) and 67.1% (54.4%, 77.7%).

Combinations of ADD-RS and D-dimer can be used to select patients with suspected AAS for imaging with a range of trade-offs between sensitivity (93.1% to 99.8%) and specificity (21.8% to 67.1%).

Acute aortic syndrome is a life-threatening emergency condition. Previous systematic reviews of D-dimer diagnostic accuracy for acute aortic syndrome have been contradictory and based on limited data, but recently published studies offer potential for a more definitive overview. We aimed to perform a systematic review and meta-analysis to determine the diagnostic accuracy of D-dimer for diagnosing acute aortic syndrome.

We searched MEDLINE, EMBASE, and the Cochrane Library from inception to February 2024. Additionally, the reference lists of included studies and other systematic reviews were thoroughly searched. All diagnostic cohort studies (prospective or retrospective) that assessed the use of D-dimer for diagnosing acute aortic syndrome compared with a reference standard test (eg, computed tomographic angiography (CTA), ECG-gated CTA, echocardiography, magnetic resonance angiography, operation, or autopsy) were included. Two independent reviewers completed study selection, data extractions and quality assessment using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool. Data were synthesized using a bivariate meta-analysis model.

Of 2017 potentially relevant citations, 25 cohort studies met the inclusion criteria, and 18 reporting the 500 ng/mL threshold were included in the primary meta-analysis. Risk of bias domains were mostly unclear due to limited study reporting. The summary sensitivity was 96.5% (95% credible interval [CrI] 94.8% to 98%) and summary specificity was 56.2% (95% CrI, 48.3% to 63.9%). Study specificity varied markedly from 33% to 86%, indicating substantial heterogeneity. Sensitivity analysis including the 7 studies reporting other thresholds showed summary sensitivity of 95.7% (95% CrI, 93.2% to 97.5%) and summary specificity of 57.5% (95% CrI, 50.1% to 64.6%).

D-dimer concentration has high sensitivity (96.5%) and moderate specificity (56.2%) for acute aortic syndrome, with some uncertainty around estimates due to risk of bias and heterogeneity. Previous meta-analysis reporting higher specificity may be explained by inclusion of case-control studies that may overestimate accuracy.

Recognizing Helicobacter pylori infection during endoscopy is important because it can lead to the performance of confirmatory testing. Linked color imaging (LCI) is an image enhancement technique that can improve the detection of gastrointestinal lesions. The purpose of this study was to compare LCI to conventional white light imaging (WLI) in the endoscopic diagnosis of H. pylori infection.

We conducted a comprehensive literature search using PubMed, Embase, and the Cochrane Library. All studies evaluating the diagnostic performance of LCI or WLI in the endoscopic diagnosis of H. pylori were eligible. Studies on magnifying endoscopy, chromoendoscopy, and artificial intelligence were excluded.

Thirty-four studies were included in this meta-analysis, of which 32 reported the performance of WLI and eight reported the performance of LCI in diagnosing H. pylori infection. The pooled sensitivity and specificity of WLI in the diagnosis of H. pylori infection were 0.528 (95% confidence interval [CI], 0.517 to 0.540) and 0.821 (95% CI, 0.811 to 0.830), respectively. The pooled sensitivity and specificity of LCI in the diagnosis of H. pylori were 0.816 (95% CI, 0.790 to 0.841) and 0.868 (95% CI, 0.850 to 0.884), respectively. The pooled diagnostic odds ratios of WLI and LCI were 15.447 (95% CI, 8.225 to 29.013) and 31.838 (95% CI, 15.576 to 65.078), respectively. The areas under the summary receiver operating characteristic curves of WLI and LCI were 0.870 and 0.911, respectively.

LCI showed higher sensitivity in the endoscopic diagnosis of H. pylori infection than standard WLI.

Physical therapists use diagnostic tests in a variety of settings. Choosing the best diagnostic test to apply in a particular situation can be difficult. The choice of diagnostic test should be informed, at least in part, by evidence of test accuracy. Finding evidence of diagnostic test accuracy has, until recently, been challenging. Ideally, there would exist a database that comprehensively indexes evidence on diagnostic tests relevant to physical therapy practice, is free to access, and is easy to use.

This Masterclass will describe the DiTA (Diagnostic Test Accuracy) database (dita.org.au) including its development and search interface, and provide advice on how to search and retrieve records.

DiTA indexes more than 2400 primary studies and systematic reviews of diagnostic test accuracy relevant to physical therapy practice. Users can search DiTA using text fields and dropdown lists to find evidence of diagnostic test accuracy. The database is freely accessible on the internet. Since its launch, DiTA has been accessed from almost every country in the world, the largest number of searches having been conducted from Brazil.

Mandibular third molar is prone to impaction, resulting in its inability to erupt into the oral cavity. The radiographic examination is required to support the odontectomy of impacted teeth. The use of computer-aided diagnosis based on deep learning is emerging in the field of medical and dentistry with the advancement of artificial intelligence (AI) technology. This review describes the performance and prospects of deep learning for the detection, classification, and evaluation of third molar-mandibular canal relationships on panoramic radiographs.

This work was conducted using three databases: PubMed, Google Scholar, and Science Direct. Following the literature selection, 49 articles were reviewed, with the 12 main articles discussed in this review.

Several models of deep learning are currently used for segmentation and classification of third molar impaction with or without the combination of other techniques. Deep learning has demonstrated significant diagnostic performance in identifying mandibular impacted third molars (ITM) on panoramic radiographs, with an accuracy range of 78.91% to 90.23%. Meanwhile, the accuracy of deep learning in determining the relationship between ITM and the mandibular canal (MC) ranges from 72.32% to 99%.

Deep learning-based AI with high performance for the detection, classification, and evaluation of the relationship of ITM to the MC using panoramic radiographs has been developed over the past decade. However, deep learning must be improved using large datasets, and the evaluation of diagnostic performance for deep learning models should be aligned with medical diagnostic test protocols. Future studies involving collaboration among oral radiologists, clinicians, and computer scientists are required to identify appropriate AI development models that are accurate, efficient, and applicable to clinical services.

In a recent systematic review, Bastos et al. (Ann Intern Med. 2021;174(4):501-510) compared the sensitivities of saliva sampling and nasopharyngeal swabs in the detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection by assuming a composite reference standard defined as positive if either test is positive and negative if both tests are negative (double negative). Even under a perfect specificity assumption, this approach ignores the double-negative results and risks overestimating the sensitivities due to residual misclassification. In this article, we first illustrate the impact of double-negative results in the estimation of the sensitivities in a single study, and then propose a 2-step latent class meta-analysis method for reevaluating both sensitivities using the same published data set as that used in Bastos et al. by properly including the observed double-negative results. We also conduct extensive simulation studies to compare the performance of the proposed method with Bastos et al.'s method for varied levels of prevalence and between-study heterogeneity. The results demonstrate that the sensitivities are overestimated noticeably using Bastos et al.'s method, and the proposed method provides a more accurate evaluation with nearly no bias and close-to-nominal coverage probability. In conclusion, double-negative results can significantly impact the estimated sensitivities when a gold standard is absent, and thus they should be properly incorporated.

Malnutrition is prevalent among gastric cancer (GC) patients, necessitating early assessment of nutritional status to guide monitoring and interventions for improved outcomes. We aim to evaluate the accuracy and prognostic capability of three nutritional tools in GC patients, providing insights for clinical implementation.

The present study is an analysis of data from 1308 adult GC patients recruited in a multicenter from July 2013 to July 2018. Nutritional status was assessed using Nutritional Risk Screening 2002 (NRS-2002), Patient-Generated Subjective Global Assessment (PG-SGA) and Global Leadership Initiative on Malnutrition (GLIM) criteria. Bayesian latent class model (LCM) estimated the malnutrition prevalence of GC patients, sensitivity and specificity of nutritional tools. Cox regression model analyzed the relationship between nutritional status and overall survival (OS) in GC patients.

Among 1308 GC patients, NRS-2002, PG-SGA, and GLIM identified 50.46%, 76.76%, and 68.81% as positive, respectively. Bayesian LCM analysis revealed that PG-SGA had the highest sensitivity (0.96) for malnutrition assessment, followed by GLIM criteria (0.78) and NRS-2002 (0.65). Malnutrition or being at risk of malnutrition were identified as independent prognostic factors for OS. Use any of these tools improved survival prediction in TNM staging system.

PG-SGA is the most reliable tool for diagnosing malnutrition in GC patients, whereas NRS-2002 is suitable for nutritional screening in busy clinical practice. Given the lower sensitivity of NRS-2002, direct utilization of GLIM for nutritional assessment may be necessary. Each nutritional tool should be associated with a specific course of action, although further research is needed.

The development of deep learning (DL) algorithms for use in dentistry is an emerging trend. Periodontitis is one of the most prevalent oral diseases, which has a notable impact on the life quality of patients. Therefore, it is crucial to classify periodontitis accurately and efficiently. This systematic review aimed to identify the application of DL for the classification of periodontitis and assess the accuracy of this approach.

A literature search up to November 2023 was implemented through EMBASE, PubMed, Web of Science, Scopus, and Google Scholar databases. Inclusion and exclusion criteria were used to screen eligible studies, and the quality of the studies was evaluated by the Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology with the QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies) tool. Random-effects inverse-variance model was used to perform the meta-analysis of a diagnostic test, with which pooled sensitivity, specificity, positive likelihood ratio (LR), negative LR, and diagnostic odds ratio (DOR) were calculated, and a summary receiver operating characteristic (SROC) plot was constructed.

Thirteen studies were included in the meta-analysis. After excluding an outlier, the pooled sensitivity, specificity, positive LR, negative LR and DOR were 0.88 (95%CI 0.82-0.92), 0.82 (95%CI 0.72-0.89), 4.9 (95%CI 3.2-7.5), 0.15 (95%CI 0.10-0.22) and 33 (95%CI 19-59), respectively. The area under the SROC was 0.92 (95%CI 0.89-0.94).

The accuracy of DL-based classification of periodontitis is high, and this approach could be employed in the future to reduce the workload of dental professionals and enhance the consistency of classification.

Adding additional specimen types (eg, serology or sputum) to nasopharyngeal swab (NPS) reverse transcription polymerase chain reaction (RT-PCR) increases respiratory syncytial virus (RSV) detection among adults. We assessed if a similar increase occurs in children and quantified underascertainment associated with diagnostic testing.

We searched databases for studies involving RSV detection in persons <18 years using ≥2 specimen types or tests. We assessed study quality using a validated checklist. We pooled detection rates by specimen and diagnostic tests and quantified performance.

We included 157 studies. Added testing of additional specimens to NP aspirate (NPA), NPS, and/or nasal swab (NS) RT-PCR resulted in statistically nonsignificant increases in RSV detection. Adding paired serology testing increased RSV detection by 10%, NS by 8%, oropharyngeal swabs by 5%, and NPS by 1%. Compared to RT-PCR, direct fluorescence antibody tests, viral culture, and rapid antigen tests were 87%, 76%, and 74% sensitive, respectively (pooled specificities all ≥98%). Pooled sensitivity of multiplex versus singleplex RT-PCR was 96%.

RT-PCR was the most sensitive pediatric RSV diagnostic test. Adding multiple specimens did not substantially increase RSV detection, but even small proportional increases could result in meaningful changes in burden estimates. The synergistic effect of adding multiple specimens should be evaluated.

What is the diagnostic accuracy of the International Federation of Orthopaedic Manipulative Physical Therapists (IFOMPT) framework to assess the risk of vascular complications in patients seeking physiotherapy care for neck pain and/or headache?

Cross-sectional diagnostic accuracy study.

One hundred and fifty patients seeking physiotherapy for neck pain and/or headache in primary care.

Nineteen physiotherapists performed the index test according to the IFOMPT framework. Patients were classified as having a high, intermediate or low risk of vascular complications, following manual therapy and/or exercise, derived from the estimated risk of the presence of vascular pathology. The reference test was a consensus medical decision reached by a vascular neurologist and an interventional neurologist, with input from a neuroradiologist. The neurologists had access to clinical data and magnetic resonance imaging of the cervical spine, including an angiogram of the cervical arteries.

Diagnostic accuracy measures were calculated for 'no contraindication' (ie, the low-risk category) and 'contraindication' (ie, the high-risk and intermediate-risk categories) for manual therapy and/or exercise. Sensitivity, specificity, predictive values, likelihood ratios and the area under the curve were calculated.

Manual therapy and/or exercise were contraindicated in 54.7% of the patients. The sensitivity of the IFOMPT framework was low (0.50, 95% CI 0.39 to 0.61) and its specificity was moderate (0.63, 95% CI 0.51 to 0.75). The positive and negative likelihood ratios were weak at 1.36 (95% CI 0.93 to 1.99) and 0.79 (95% CI 0.60 to 1.05), respectively. The area under the curve was poor (0.57, 95% CI 0.49 to 0.65).

The IFOMPT framework has poor diagnostic accuracy when compared with a reference standard consisting of a consensus medical decision.

Magnetic resonance imaging (MRI) can reduce the need for unnecessary invasive diagnostic tests by nearly half. In this meta-analysis, we investigated the diagnostic accuracy of intravoxel incoherent motion modeling (IVIM) and dynamic contrast-enhanced (DCE) MRI in differentiating benign from malignant breast lesions.

We systematically searched PubMed, EMBASE, and Scopus. We included English articles reporting diagnostic accuracy for both sequences in differentiating benign from malignant breast lesions. Articles were assessed by quality assessment of diagnostic accuracy studies-2 (QUADAS-2) questionnaire. We used a bivariate effects model for standardized mean difference (SMD) analysis and diagnostic test accuracy analysis.

Ten studies with 537 patients and 707 (435 malignant and 272 benign) lesions were included. The D, f, Ktrans, and Kep mean values significantly differ between benign and malignant lesions. The pooled sensitivity (95 % confidence interval) and specificity were 86.2 % (77.9 %-91.7 %) and 70.3 % (56.5 %-81.1 %) for IVIM, and 93.8 % (85.3 %-97.5 %) and 68.1 % (52.7 %-80.4 %) for DCE, respectively. Combined IVIM and DCE depicted the highest area under the curve of 0.94, with a sensitivity and specificity of 91.8 % (82.8 %-96.3 %) and 87.6 % (73.8 %-94.7 %), respectively.

Combined IVIM and DCE had the highest diagnostic accuracy, and multiparametric MRI may help reduce unnecessary benign breast biopsy.

Sarcopenia is defined as a decline in muscle function as well as muscle mass. Sarcopenia itself and sarcopenic obesity, defined as sarcopenia in obese patients, have been used as surrogates for a worse prognosis in colorectal cancer. This review aims to determine if there is evidence for sarcopenia as a prognostic parameter in colorectal liver metastases (CRLM).

PubMed, Embase, Cochrane Central, Web of Science, SCOPUS, and CINAHL databases were searched for articles that were selected in accordance with the PRISMA guidelines. The primary outcomes were overall survival (OS) and disease-free survival (DFS). A random effects meta-analysis was conducted.

After eliminating duplicates and screening abstracts (n = 111), 949 studies were screened, and 33 publications met the inclusion criteria. Of them, 15 were selected after close paper review, and 10 were incorporated into the meta-analysis, which comprised 825 patients. No significant influence of sarcopenia for OS (odds ratio (OR), 2.802 (95% confidence interval (CI), 1.094-1.11); p = 0.4) or DFS (OR, 1.203 (95% CI, 1.162-1.208); p = 0.5) was found, although a trend was defined toward sarcopenia. Sarcopenia significantly influenced postoperative complication rates (OR, 7.905 (95% CI, 1.876-3.32); p = 0.001) in two studies where data were available.

Existing evidence on the influence of sarcopenia on postoperative OS as well as DFS in patients undergoing resection for CRLM exists. We were not able to confirm that sarcopenic patients have a significantly worse OS and DFS in our analysis, although a trend toward this hypothesis was visible. Sarcopenia seems to influence complication rates but prospective studies are needed.

Introduction. The FilmArray blood culture identification panel (BCID) panel is a multiplex PCR assay with high sensitivity and specificity to identify the most common pathogens in bloodstream infections (BSIs).Hypothesis. We hypothesize that the BCID panel has good diagnostic performance for BSIs and can be popularized in clinical application.Aim: To provide summarized evidence for the diagnostic accuracy of the BCID panel for the identification of positive blood cultures.Methodology. We searched the MEDLINE, EMBASE and Cochrane databases through March 2021 and assessed the efficacy of the diagnostic test of the BCID panel. We performed a meta-analysis and calculated the summary sensitivity and specificity of the BCID panel. Systematic review protocols were registered in the International Prospective Register of Systematic Reviews (PROSPERO) (registration number CRD42021239176).Results. A total of 16 full-text articles were eligible for analysis. The overall sensitivities of the BCID panel on Gram-positive bacteria, Gram-negative bacteria and fungi were 97 % (95 % CI, 0.96-0.98), 100 % (95 % CI, 0.98-01.00) and 99 % (95 % CI, 0.87-1.00), respectively. The pooled diagnostic specificities were 99 % (95 % CI, 0.97-1.00), 100 % (95 % CI, 1.00-1.00) and 100 % (95 % CI, 1.00-1.00) for Gram-positive bacteria, Gram-negative bacteria and fungi, respectively.Conclusions. The BCID panel has high rule-in value for the early detection of BSI patients. The BCID panel can still provide valuable information for ruling out bacteremia or fungemia in populations with low pretest probability.

The application of fecal calprotectin in the relapse of inflammatory bowel disease remains uncertain in children and adolescents. We systematically searched the common databases for eligible studies. Judgment of diagnostic accuracy included pooled sensitivity and specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and pooled area under the receiver operating characteristic curve. Ultimately, a total of 414 participants from six studies were included. The combined sensitivity, specificity, PLR, and NLR with 95% confidence intervals (CIs) were 0.85 (95% CI = 0.65 to 0.79), 0.71 (95% CI = 0.52 to 0.85), 2.95 (95% CI = 1.71 to 5.09), and 0.21 (95% CI = 0.08 to 0.51), respectively. The area under the summary receiver operating characteristic curve was 0.85, and the DOR was 14.14 (95% CI = 4.46 to 44.80). Our study showed that fecal calprotectin as a biomarker to predict the clinical relapse of inflammatory bowel disease during remission in children and adolescents is effective and worth applying. [Pediatr Ann. 2023;52(9):e357-e362.].

Pneumocystis jirovecii pneumonia (PJP) can be a life-threatening opportunistic infection. We aimed to evaluate the diagnostic accuracy of metagenomic next-generation sequencing (mNGS) for PJP.

A comprehensive electronic literature search of Web of Knowledge, PubMed, Cochrane Library, CNKI and Wanfang data was performed. Bivariate analysis was conducted to calculate the pooled sensitivity, specificity, diagnostic odds ratio (DOR), the area under the summary receiver operator characteristic (SROC) curve and the Q-point value (Q*).

The literature search resulted in 9 studies with a total of 1343 patients, including 418 cases diagnosed with PJP and 925 controls. The pooled sensitivity of mNGS for diagnosis of PJP was 0.974 [95% confidence interval (CI), 0.953-0.987]. The pooled specificity was 0.943 (95% CI, 0.926-0.957), the DOR was 431.58 (95% CI, 186.77-997.27), the area under the SROC curve was 0.987, and the Q* was 0.951. The I2 test indicated no heterogeneity between studies. The Deek funnel test suggested no potential publication bias. Subgroup analyses showed that the area under the SROC curve of mNGS for diagnosis of PJP in immunocompromised and non-HIV patients was 0.9852 and 0.979, respectively.

Current evidence indicates that mNGS exhibits excellent accuracy for the diagnosis of PJP. The mNGS is a promising tool for assessment of PJP in both immunocompromised and non-HIV patients.

(1) Background: The current diagnostic algorithm for acute pulmonary embolism (PE) is associated with the overuse of CT pulmonary angiography (CTPA). An additional highly specific blood test could potentially lower the proportion of patients with suspected PE that require CTPA. The aim was to summarize the literature on the diagnostic performance of biomarkers of patients admitted to an emergency department with suspected acute PE. (2) Methods: Medline and Embase databases were searched from 1995 to the present. The study selection process, data extraction, and risk of bias assessment were conducted by two reviewers. Eligibility criteria accepted all blood biomarkers except D-dimer, and CTPA was used as the reference standard. Qualitative data synthesis was performed. (3) Results: Of the 8448 identified records, only 6 were included. Eight blood biomarkers were identified, of which, three were investigated in two separate studies. Red distribution width and mean platelet volume were reported to have a specificity of ≥ 90% in one study, although these findings were not confirmed by other studies. The majority of the studies contained a high risk of selection bias. (4) Conclusions: The modest findings and the uncertain validity of the included studies suggest that none of the biomarkers identified in this systematic review have the potential to improve the current diagnostic algorithm for acute PE by reducing the overuse of CTPA.

The presence of resistant ESKAPE pathogens to antimicrobials including chemical disinfectants (ChDs) is a serious threat to public health worldwide. In the present study, we systematically reviewed published reports on mechanisms beyond ChD resistance of ESKAPE bacteria.

Several databases without date limitations were searched. Studies focused on the ChD resistance/tolerance mechanisms of ESKAPE bacteria were included. Meta-analysis was done to assess the frequency of tolerance and genes in ESKAPE clinical isolates. By screening of initial 6733 records, finally, 41 studies were included.

The overall tolerance to at least one ChD was 48.6%. Pseudomonas aeruginosa and Acinetobacter baumannii were highly ChD-resistant. In several studies, phenotypic changes including changes in general morphology, pump function, cell surface, and membrane, as well as metabolic changes were observed after ChD addition. The resistance gene frequency was 70.2% for norfloxacin efflux pump genes, 40.6% for qac major facilitator superfamily genes, and 22.2% for qac small multidrug resistance genes.

We systematically reviewed the effect of various mechanisms in the resistance process of ESKAPE bacteria to ChDs. However, except for the impact of genes, the numbers of studies investigating other mechanisms were very limited, demanding carrying out more studies in this field.

To determine the diagnostic accuracy of different endometrial sampling tests for detecting endometrial carcinoma.

Systematic review and meta-analysis of studies of diagnostic accuracy.

Cochrane Library, MEDLINE/PubMed, CINAHL, Web of Science and Scopus, from the date of inception of the databases to 18 January 2023. Additionally, the reference lists of included studies and other systematic reviews were thoroughly searched.

We included published cross-sectional studies that evaluated any endometrial sampling test (index tests) in women (participants) with clinical suspicion of endometrial carcinoma (target condition) in comparison with histopathology of hysterectomy specimens (reference standard). We excluded case-control and case series studies. No restrictions on language or date of publication were applied.

Two independent reviewers extracted study data and assessed study quality using the revised quality assessment tool for diagnostic accuracy studies (QUADAS-2). We used bivariate diagnostic random-effects meta-analysis and presented the results in a summary receiver operating characteristic curve. We assessed the certainty of evidence as recommended by the GRADE (Grading of Recommendations, Assessment, Development, and Evaluations) approach.

Twelve studies (1607 participants), published between 1986 and 2022, contributed data to the meta-analysis results. Seven studies were judged to be at a low risk of bias in all domains and all studies had low applicability concerns. The most studied index tests were Pipelle and conventional dilation and curettage (D&C). The sensitivity, specificity, positive likelihood ratio and negative likelihood ratio (95% CIs) for Pipelle were 0.774 (0.565 to 0.900), 0.985 (0.927 to 0.997), 97.000 (14.000 to 349.000) and 0.241 (0.101 to 0.442) and for conventional D&C were 0.880 (0.281 to 0.993), 0.984 (0.956 to 0.995), 59.300 (14.200 to 153.000) and 0.194 (0.007 to 0.732), respectively.

High certainty evidence indicates that endometrial sampling using Pipelle or conventional D&C is accurate in diagnosing endometrial cancer. Studies assessing other endometrial sampling tests were sparse.

https://osf.io/h8e9z.

COVID-19 infections have become a global public health emergency. Although COVID-19 is primarily a respiratory disease, some of hospitalised patients exhibit cognitive impairment-related neurological damage. Using a systematic review and meta-analysis, we aim to investigate the risk factors for cognitive impairment in patients with COVID-19.

This meta-analysis has been registered with the International Prospective Register of Systematic Reviews. From inception to 5 August 2022, we will search PubMed, Web of Science, Embase via Ovid, the Chinese Biological Medical Database and the Cochrane Central Register of Controlled Trials (CENTRAL) for relevant studies . We will also look for additional studies in the reference lists of selected articles. To ensure data quality and accuracy, only researches published in English and Chinese will be included. Fixed or random-effects model will be used to calculate the relative risk (RR) or odds ratio (OR) and 95% CIs for pooled data about dichotomous outcomes. We will also assess heterogeneity using Cochrane's Q and I² tests. Cognitive impairment RR or OR is the primary outcome.

Data will be extracted from published studies, so ethical approval is not required. The outcomes of this meta-analysis will be published in a journal with peer review.

CRD42022351011.

Anxiety is common in long-term care (LTC), but it is unclear which anxiety detection tools are accurate when compared to a reference standard for residents of LTC. Four databases and grey literature sources were searched using the search concepts "anxiety" and "LTC". Included studies evaluated the diagnostic accuracy of an anxiety detection tool compared to a reference standard in LTC residents. Diagnostic accuracy measures were extracted. Four articles out of 4,620 met the inclusion criteria. Despite limited evidence and poorly reported study procedures and characteristics, the Geriatric Anxiety Inventory (sensitivity: 90.0%, specificity: 86.2%) and the Hospital Anxiety and Depression Scale-Anxiety (sensitivity: 90.0%, specificity: 80.6%) had the best performance when detecting generalized anxiety disorder. We identified four anxiety detection tools appropriate for use in LTC; a critical first step to diagnosing and managing anxiety in residents of LTC. Non-generalized anxiety disorders and tool feasibility must be further evaluated.

There are concerns about the validation and accuracy of currently available consumer sleep technology for sleep-disordered breathing. The present report provides a background review of existing consumer sleep technologies and discloses the methods and procedures for a systematic review and meta-analysis of diagnostic test accuracy of these devices and apps for the detection of obstructive sleep apnea and snoring in comparison with polysomnography. The search will be performed in four databases (PubMed, Scopus, Web of Science, and the Cochrane Library). Studies will be selected in two steps, first by an analysis of abstracts followed by full-text analysis, and two independent reviewers will perform both phases. Primary outcomes include apnea-hypopnea index, respiratory disturbance index, respiratory event index, oxygen desaturation index, and snoring duration for both index and reference tests, as well as the number of true positives, false positives, true negatives, and false negatives for each threshold, as well as for epoch-by-epoch and event-by-event results, which will be considered for the calculation of surrogate measures (including sensitivity, specificity, and accuracy). Diagnostic test accuracy meta-analyses will be performed using the Chu and Cole bivariate binomial model. Mean difference meta-analysis will be performed for continuous outcomes using the DerSimonian and Laird random-effects model. Analyses will be performed independently for each outcome. Subgroup and sensitivity analyses will evaluate the effects of the types (wearables, nearables, bed sensors, smartphone applications), technologies (e.g., oximeter, microphone, arterial tonometry, accelerometer), the role of manufacturers, and the representativeness of the samples.

In light of the pandemic of spurious penicillin allergy, correct diagnosis of amoxicillin (AX) allergy is of great importance. The diagnosis of immediate hypersensitivity reactions relies on skin tests and specific IgE, and although reliable, these are not absolutely predictive. Therefore, drug challenges are needed in some cases, which contain the risk of severe reactions. Safe in vitro diagnostics as an alternative for the drug challenge in the diagnostic workup of AX allergy would be more than welcome to fill this gap. In this respect, the basophil activation test (BAT) has shown potential, but its clinical reliability is doubtful.

To investigate the reliability of the BAT to AX and determining its exact place in the diagnostic algorithm of AX allergy.

BAT for AX was performed in 70 exposed control individuals and 66 patients diagnosed according to the European Academy of Allergy and Clinical Immunology guidelines for AX allergy. Upregulation of both CD63 and CD203c was flow-cytometrically assessed.

Analyses revealed that 1370 μmol/L and 685 μmol/L were the most discriminative stimulation concentrations for CD63 and CD203c upregulation, respectively, and a diagnostic threshold of 9% for positivity for both markers was identified. At these concentrations, sensitivity and specificity for CD63 upregulation were 13% and 100%, respectively, and for CD203c upregulation, 23% and 98%.

BAT with dual analysis of CD63 and CD203c is of poor performance to document AX allergy. The sensitivity is too low to let it occupy a prominent role in the diagnostic algorithm.

Lung cancer is the principal cause of cancer-related deaths worldwide. Early detection of lung cancer with screening is indispensable to reduce the high morbidity and mortality rates. Artificial intelligence (AI) is widely utilised in healthcare, including in the assessment of medical images. A growing number of reviews studied the application of AI in lung cancer screening, but no overarching meta-analysis has examined the diagnostic test accuracy (DTA) of AI-based imaging for lung cancer screening.

To systematically review the DTA of AI-based imaging for lung cancer screening.

PubMed, EMBASE, Cochrane Library, CINAHL, IEEE Xplore, Web of Science, ACM Digital Library, Scopus, PsycINFO, and ProQuest Dissertations and Theses were searched from inception to date. Studies that were published in English and that evaluated the performance of AI-based imaging for lung cancer screening were included. Two independent reviewers screened titles and abstracts and used the Quality Assessment of Diagnostic Accuracy Studies-2 tool to appraise the quality of selected studies. Grading of Recommendations Assessment, Development, and Evaluation to diagnostic tests was used to assess the certainty of evidence.

Twenty-six studies with 150,721 imaging data were included. Hierarchical summary receiver-operating characteristic model used for meta-analysis demonstrated that the pooled sensitivity for AI-based imaging for lung cancer screening was 94.6 % (95 % CI: 91.4 % to 96.7 %) and specificity was 93.6 % (95 % CI: 88.5 % to 96.6 %). Subgroup analyses revealed that similar results were found among different types of AI, region, data source, and year of publication, but the overall quality of evidence was very low.

AI-based imaging could effectively detect lung cancer and be incorporated into lung cancer screening programs. Further high-quality DTA studies on large lung cancer screening populations are required to validate AI's role in early lung cancer detection.

Our meta-analysis aimed to evaluate the diagnostic value of ¹⁸F-DCFPyL prostate-specific membrane antigen (PSMA) PET in patients with suspected prostate cancer.

We searched for articles that evaluate the diagnostic value of ¹⁸F-DCFPyL PSMA PET in patients with suspected prostate cancer in PubMed, Embase, Cochrane Library, and Web of Science until 1 August 2022. Using the QUADAS-2 instrument, two researchers independently assessed the effectiveness of the studies that were included. The four-grid table data were analyzed by Meta-disc1.4 and Stata 16.0 software. The heterogeneity of each study was tested.

A total of five studies with 258 patients were included, and the pooled sensitivity and specificity of ¹⁸F-DCFPyL PSMA PET for primary prostate cancer were 0.92 (95% confidence interval (CI): 0.85-0.96) and 0.59 (95% CI: 0.08-0.96), respectively. ¹⁸F-DCFPyL PSMA PET was successful in detecting primary prostate cancer, with an area under the curve (AUC) of 0.92 (95% CI: 0.89-0.94).

¹⁸F-DCFPyL PSMA PET has a strong predictive value for primary prostate cancer and is an effective method for the non-invasive diagnosis of prostate cancer. More prospective articles were needed.

Due to relatively low malaria parasitaemia in pregnancy, an appropriate field test that can adequately detect infections in pregnant women presenting with illness or for malaria screening during antenatal care is crucially important. The objective was to evaluate the diagnostic accuracy of loop-mediated isothermal amplification (LAMP) for the detection of uncomplicated malaria in pregnancy.

This was a meta-analysis of diagnostic accuracy. Relevant studies that assessed the diagnostic performance of LAMP for the detection of malaria in pregnancy were searched in health-related electronic databases including PubMed, Ovid, and Google Scholar. The methodological quality of the studies included was evaluated using the QUADAS-2 tool.

Of the 372 studies identified, eight studies involving 2999 pregnant women in five endemic countries that assessed the accuracy of LAMP were identified. With three types of PCR as reference tests, the pooled sensitivity of LAMP was 91% (95%CI 67-98%) and pooled specificity was 99% (95%CI 83-100%, 4 studies), and the negative likelihood ratio was 9% (2-40%). Caution is needed in the interpretation as there was substantial between-study heterogeneity (I²: 80%), and a low probability that a person without infection is tested negative. With microscopy as a reference, the pooled sensitivity of LAMP was 95% (95%CI 26-100%) and pooled specificity was 100% (95%CI 94-100%, 4 studies). There was a wide range of sensitivity and substantial between-study heterogeneity (I²: 83.5-98.4%). To investigate the source of heterogeneity, a meta-regression analysis was performed with covariates. Of these potential confounding factors, reference test (p: 0.03) and study design (p:0.03) had affected the diagnostic accuracy of LAMP in malaria in pregnancy. Overall, there was a low certainty of the evidence in accuracy estimates.

The findings suggest that LAMP is more sensitive than traditional tests used at facilities, but the utility of detecting and treating these low-density infections is not well understood. Due to the limited number of studies with bias in their methodological quality, variation in the study design, and different types of reference tests further research is likely to change the estimate. Well-conceived large prospective studies with blinding of the index test results are recommenced.

The increasing incidence of breast cancer necessitates the need to explore alternate screening strategies that circumvent the setbacks of conventional techniques especially among population that report earlier age at diagnosis. Serum autoantibodies is one such potential area of interest. However, their ubiquitous presence across cancer types limits its applicability to any one specific type of cancer. This review was therefore carried out to explore and consolidate available evidence on autoantibodies for early detection of breast cancer and to identify those that demonstrated a higher sensitivity.

A diagnostic test accuracy (DTA) review was carried out to ascertain serum autoantibodies that could be used for early detection of breast cancer among women. All relevant articles that investigated the role of autoantibodies in early detection of breast cancer were included for the review. MEDLINE, Scopus, ProQuest, Ovid SP, and Cochrane Library were searched extensively for eligible studies. Quality of the included studies was assessed using Quality Assessment of Diagnostic Accuracy Studies (QUADAS)-2 tool. RevMan 5.3 was used for exploratory and MetaDTA 2019 for hierarchical analyses. The review helped identify the most frequently investigated autoantibodies and a meta-analysis further consolidated the findings.

A total of 53 articles were included for the final analysis that reported over a 100 autoantibodies that were studied for early detection of breast cancer in women. P53, MUC1, HER2, HSP60, P16, Cyclin B1, and c-Myc were the most frequently investigated autoantibodies. Of these P53, MUC1, HER2, and HSP60 exhibited higher summary sensitivity measures. While the individual pooled sensitivity estimates ranged between 10 and 56%, the panel sensitivity values reported across studies were higher with an estimated range of 60-87%.

Findings from the review indicate a higher sensitivity for an autoantibody panel in comparison to individual assays. A panel comprising of P53, MUC1, HER2, and HSP60 autoantibodies has the potential to be investigated as an early detection biomarker for breast cancer.

Background and aims  Artificial intelligence (AI) technology is being evaluated for its potential to improve colonoscopic assessment of inflammatory bowel disease (IBD), particularly with computer-aided image classifiers. This review evaluates the clinical application and diagnostic test accuracy (DTA) of AI algorithms in colonoscopy for IBD. Methods  A systematic review was performed on studies evaluating AI in colonoscopy of adult patients with IBD. MEDLINE, Embase, Emcare, PsycINFO, CINAHL, Cochrane Library and Clinicaltrials.gov databases were searched on 28 ^th April 2021 for English language articles published between January 1, 2000 and April 28, 2021. Risk of bias and applicability were assessed with the Quality Assessment of Diagnostic Accuracy Studies-2 tool. Diagnostic accuracy was presented as median (interquartile range). Results  Of 1029 records screened, nine studies with 7813 patients were included for review. AI was used to predict endoscopic and histologic disease activity in ulcerative colitis, and differentiation of Crohn's disease from Behcet's disease and intestinal tuberculosis. DTA of AI algorithms ranged between 52-91 %. The sensitivity and specificity for AI algorithms predicting endoscopic severity of disease were 78 % (range 72-83, interquartile range 5.5) and 91 % (range 86-96, interquartile range 5), respectively. Conclusions  AI has been primarily used to assess disease activity in ulcerative colitis. The diagnostic performance is promising and suggests potential for other clinical application of AI in IBD colonoscopy such as dysplasia detection. However, current evidence is limited by retrospective data and models trained on still images only. Future prospective multicenter studies with full-motion videos are needed to replicate the real-world clinical setting.