Diagnostic Accuracy Data Research Articles

Diagnostic Accuracy of Rapid Antigen Tests for Trichomoniasis: A Meta-analysis.

Trichomoniasis is a globally prevalent sexually transmitted infection caused by the protozoan Trichomonas vaginalis. Polymerase chain reaction (PCR) is the gold standard for diagnosing trichomoniasis, but it is expensive. Antigen tests are immunochromatographic immunoassays that detect T. vaginalis membrane proteins. Despite being user-friendly and rapid, the diagnostic accuracy of antigen tests remains uncertain. Therefore, this meta-analysis aimed to evaluate the diagnostic accuracy of antigen tests for T. vaginalis infections. We mined the PubMed, Embase, and Cochrane Library databases for studies evaluating the diagnostic accuracy of antigen tests for T. vaginalis. We included studies that provided diagnostic test accuracy data in order to conduct a meta-analysis. We evaluated antigen tests based on immunochromatography and lateral flow devices. The meta-analysis was conducted by using the hierarchical summary receiver operating characteristic model. Eleven studies with 5,884 samples were included. The meta-analysis yielded a pooled sensitivity of 87.0% and a pooled specificity of 98.3%. A subgroup analysis employing PCR as the reference standard yielded a sensitivity of 58.5%, whereas another subgroup analysis using culture returned a sensitivity of 95.9%. The subgroup analysis of 6 studies comprising 2,328 specimens from symptomatic individuals yielded a pooled sensitivity of 85% and a specificity of 99.9%. The antigen tests exhibited high sensitivity and specificity. Additionally, subgroup analyses revealed that antigen tests demonstrated greater sensitivity in diagnosing symptomatic patients compared to asymptomatic individuals. While less sensitive than PCR, antigen testing remains a promising avenue for detecting T. vaginalis infections.

Managing Pregnancies of Unknown Location With the M4 Prediction Model or the NICE Algorithm: A Randomised Controlled Trial With Cross-Sectional Diagnostic Accuracy Data.

To determine the diagnostic performance and clinical utility of the M4 prediction model and the NICE algorithm managing women with pregnancy of unknown location (PUL). The study has a superiority design regarding specificity for non-ectopic pregnancy for M4, given that the primary outcome of sensitivity for ectopic pregnancy (EP) is non-inferior in comparison with the NICE algorithm. Emergency gynaecology units in Sweden. 595 women with PUL. Participants were randomised (1:1) to M4 or the NICE algorithm after two serum human chorionic (hCG) levels and were categorised as high or low risk of having an EP. The diagnostic performance was evaluated on cross-sectional data and utility by parallel groups. The proportion of EP categorised as high risk (sensitivity) and non-ectopic pregnancies categorised as low risk (specificity). Clinical outcomes were assessed. The sensitivity for EP was 79% (115 of 146) for M4 versus 85% (124 of 146) for the NICE algorithm, p = 0.1496 and the specificity for non-ectopic pregnancies was 67% (300 of 449) for M4 and 74% (334 of 449) for the NICE algorithm, p = 0.0003. Clinical outcomes were similar between groups. The sensitivity for EP by M4 was non-inferior to NICE, but specificity was better for the NICE algorithm. No between group differences were observed for clinical outcomes. NCT03461835, https://www. gov.

Application value of different imaging methods in the early diagnosis of small hepatocellular carcinoma: a network meta-analysis.

To determine the diagnostic value of ultrasound, multi-phase enhanced computed tomography, and magnetic resonance imaging of small hepatocellular carcinoma. Experimental studies on diagnosing small hepatocellular carcinoma in four databases: PubMed, Cochrane Library, Web of Science, and Embase, were comprehensively searched from October 2007 to October 2024. Relevant diagnostic accuracy data were extracted and a Bayesian model that combined direct and indirect evidence was used for analysis. 16 original studies were included and data from 2,447 patients were collated to assess the diagnostic value of 10 different methods. The methodological quality of the included studies was good and there was no obvious publication bias. The pooled DOR of all diagnostic methods was 19.61, which was statistically significant (I2 = 76.0%, P < 0.01, 95% CI:13.30 - 28.92). Normal US + CEUS + ultrasonic elastic imaging had the highest specificity (92.9), accuracy (93.6), and positive predictive value (94.4). Unenhanced MRI + Contrast-enhanced MRI had the highest sensitivity (96.6) and negative predictive value (96.6), but specificity (12.5) and positive predictive value (34.4) were extremely poor. Contrast-enhanced MRI had the highest diagnostic value in individual imaging methods (sensitivity: 66, specificity: 55.5, accuracy: 67.9, positive predictive value: 64.4, negative predictive value: 66.5). There was significant inconsistency and high heterogeneity in this study. https://www.crd.york.ac.uk/PROSPERO/, identifier CRD42024507883.

Cost-effectiveness of targeted Next-Generation Sequencing (tNGS) for detection of tuberculosis drug resistance in India, South Africa and Georgia: a modeling analysis

Targeted next-generation sequencing (tNGS) is promising alternative to phenotypic drug susceptibility testing (pDST) for detecting drug-resistant tuberculosis (DRTB). This study explored the potential cost-effectiveness of tNGS for the diagnosis of DR-TB across 3 settings: India, South Africa and Georgia. To inform WHO guideline development group (GDG) on tNGS we developed a stochastic decision analysis model and assessed cost-effectiveness of tNGS for DST among rifampicin resistance individuals. We also assessed tNGS as initial test for TB drug resistance in bacteriologically confirmed TB. Diagnostic accuracy and cost data were sourced from a systematic review conducted for GDG, covering studies published until September 2022. The primary outcome was incremental cost (2021 US$) per disability-adjusted life year (DALY) averted. tNGS when compared with in-country DST, tNGS proved cost-effective in South Africa (ICER: $15,619/DALY averted, WTP: $21,165) but not in Georgia (ICER: $18,375/DALY averted, WTP: $15,069). In India, tNGS dominated in-country DST practice, providing greater health impact at lower cost. When comparing tNGS with universal pDST, tNGS was dominated by pDST in all three countries. In Georgia, using tNGS as initial test for TB drug-resistance compared to Xpert MTB/Rif followed by pDST appeared cost-effective. Scenario with 50% reduction in tNGS test kit costs made tNGS cost-effective across all three countries, while a high Bedaquiline resistance prevalence (30%) led to a worsening cost-effectiveness. tNGS may be cost-effective in India, South Africa and Georgia when comprehensive DST is not routinely performed. Thus, existing DST practice and healthcare infrastructure should be considered before implementation and scale-up of tNGS. Global Tuberculosis Program, World Health Organization (2022/1249364-0).

Efficacy of 18FDG-PET/CT in Detecting Synchronous Malignancies in Patients With Head and Neck Cancer: A Systematic Review and Meta-analysis.

To assess the diagnostic accuracy of fluorine-18 fluorodeoxyglucose positron emission tomography and computed tomography (18FDG-PET/CT) in detecting second primary malignancies (SPMs) in patients with treatment naïve head and neck squamous cell carcinoma (HNSCC). Medline, Embase, Cochrane Library, and Scopus searched from 1946 to December 2022. Studies reporting the performance of 18FDG-PET/CT in patients with treatment-naïve, index HNSCC for detection of SPMs were included. The reference standard was histopathology, clinical follow-up over the duration of study, and other imaging modalities. Multiple investigators completed depth full-text analysis. Preferred Reporting Items for Systematic Reviews and Meta-analysis guidelines were followed. Methodologic and diagnostic accuracy data were abstracted independently by multiple investigators. Risk of bias assessment was conducted using the Quality Assessment of Diagnostic Accuracy Studies tool independently. Bivariate random-effects model meta-analysis and multivariable meta-regression modeling were used. Seventeen studies examining 4624 patients with a total of 475 SPMs were included in the final analysis. Eleven studies were found to be at low risk for bias, while the rest were in the high-risk category. 18FDG-PET/CT demonstrated pooled sensitivity and specificity of 0.73 (95% confidence interval [CI]: 0.49-0.88) and 0.99 (95% CI: 0.98-1.00) in detecting SPMs. Further subsite analysis revealed varied diagnostic performance across different anatomical regions, with sensitivity and specificity of esophageal SPMs being 0.47 (0.30-0.64) and 0.99 (0.98-1.00), and sensitivity and specificity of 0.86 (0.73-0.94) and 0.99 (0.98-1.00) for head and neck SPMs. Finally, this imaging modality showed sensitivity and specificity of 0.92 (0.84-0.96) and 0.99 (0.98-1.00) for lung SPMs. The findings of this study suggest varied accuracy of 18FDG-PET/CT in detecting SPMs during initial workup for HNSCC, highlighting the importance of screening modalities such as esophagoscopy in high-risk patients.

Diagnostic performance of dual-energy computed tomography in detecting anterior cruciate ligament injuries: a systematic review and meta-analysis.

Anterior cruciate ligament (ACL) injuries are common and lead to significant physical limitations. While MRI is the diagnostic gold standard, its use is restricted in acute trauma cases due to contraindications and longer imaging times. Dual-energy computed tomography (DECT) has emerged as a potential alternative. This meta-analysis evaluates the diagnostic accuracy of DECT for ACL injuries. Following PRISMA guidelines, a comprehensive literature search was conducted using PubMed, Web of Science, Scopus, and Embase for studies published up to June 2024. Studies that provided diagnostic accuracy data for DECT in ACL ruptures were included. Metrics of diagnostic accuracy were aggregated using a bivariate random effects model. The meta-analysis, which included five studies with a total of 191 patients, found that DECT had a pooled sensitivity of 88.1% (95% CI, 78.0-93.9%) and a specificity of 82.0% (95% CI, 62.0-92.7%) for diagnosing ACL ruptures, with an AUC of 0.92 (95% CI, 0.72-0.96). For complete ruptures, sensitivity was 83.2% (95% CI, 68.2-92.0%), and specificity was 94.9% (95% CI, 92.2-96.7%), with an AUC of 0.96 (95% CI, 0.81-0.98). In acute/subacute settings, sensitivity was 89.4% (95% CI, 76.8-95.6%), and specificity was 82.1% (95% CI, 56.2-94.2%), with an AUC of 0.93 (95% CI, 0.71-0.97). Our findings suggest that DECT is a valuable diagnostic tool for ACL injuries, particularly as an adjunct or alternative when MRI is unavailable or contraindicated, enabling timely and accurate diagnosis.

Comparing assisting technologies for proficiency in cardiac morphology: 3D printing and mixed reality versus CT slice images for morphological understanding of congenital heart defects by medical students.

Learning cardiac morphology largely involves spatial abilities and studies indicate benefits from innovative 3D visualization technologies that speed up and increase the learning output. Studies comparing these teaching tools and their educational output are rare and few studies include complex congenital heart defects. This study compared the effects of 3D prints, mixed reality (MR) viewing of 3D meshes and standard cardiac CT slice images on medical students' understanding of complex congenital heart defect morphology, measuring both objective level of understanding and subjective educational experience. The objective of this study was to compare morphological understanding and user experiences of 3D printed models, MR 3D visualization and axial 2D CT slices, in medical students examining morphological details in complex congenital heart defects. Medical students in the median 4th year of study (range 2nd to 6th) examined three of five different complex congenital heart defects by three different modalities: 3D printed model, MR viewed 3D mesh, and cardiac CT slices, answering a questionnaire on morphology and user experience. Time to complete task, diagnostic accuracy, and user experience data were collected and compared on group level. Task times were similar for all modalities. The percentage of correct answers was higher with MR visualization, which was also the preferred modality overall. Medical students both prefer and better understand the morphology of complex congenital heart disease with 3D models viewed using MR, without spending more time than with 3D prints or 2D CT images.

Modelling the joint accuracy of two diagnostic tests using evidence synthesis methods incorporating linked electronic health records

Background and ObjectivesTo compare the accuracy of different diagnostic pathways comprising of multiple diagnostic tests, statistical methods that evaluate combined or sequential test accuracy are required. This can only be achieved through joint synthesis of diagnostic accuracy data on all tests of interest, using models that account for dependencies between multiple tests. The aim of this research is to develop joint models to assess the diagnostic accuracy of two tests, using real-world evidence to inform within-study dependencies between tests. ApproachUsing a case study in Alzheimer’s disease dementia (ADD), we will develop Bayesian meta-analysis models to evaluate the combined diagnostic accuracy of two tests. A novel application of trivariate copulas will be used to capture within-study dependencies between two tests assessed in the same patients. Individual participant data from Dementias Platform UK (DPUK) Data Portal – a repository of linked electronic health record data optimised for dementia research – will be used to estimate dependencies between diagnostic tests for ADD. ResultsThe DPUK Data Portal includes records of over 3 million people from 51 long-term cohort studies, including individual-level test results for ADD verified against confirmed diagnoses. Analysis will focus on modelling the diagnostic accuracy of different testing strategies for ADD, combining cognitive function, imaging, and biomarker tests. Conclusions and ImplicationsResults of this study have the potential to inform healthcare decision-making regarding optimal diagnostic pathways by enabling the comparison of different testing strategies. The incorporation of real-world data from the DPUK Data Portal makes better use of the available evidence.

Diagnostic Accuracy of Optic Nerve Sheath Diameter Using Ultrasonography for Raised Intracranial Pressure in Pediatric Patients - A Systematic Review and Meta-Analysis

PurposeDetecting increased intracranial pressure early in pediatric patients is essential, as early initiation of therapy prevents morbidity and mortality. The objective of this study was to determine the diagnostic accuracy of the ONSD measured via ultrasound for the prediction of increased intracranial pressure. MethodsFour databases, namely, PubMed, EMBASE, Scopus & CINAHL, were searched for this systematic review and meta-analysis. The study's predefined inclusion criteria considered diagnostic accuracy, cross-sectional, prospective observational, and retrospective studies with a focus on children with elevated intracranial pressure from causes such as traumatic brain injury and cerebral edema, the diagnostic accuracy of the optic nerve sheath diameter measured using ultrasound was assessed. The primary outcome measures included sensitivity, specificity. The study included invasive monitoring (EVD) and noninvasive measures as the gold standards for increased intracranial pressure. Two authors extracted and reviewed the data. Baseline data, outcome measures, and diagnostic accuracy data were extracted. ResultsTwenty-five studies with 1,591 patients and 3,143 ONSD measurements via ultrasound were analyzed. The pooled sensitivity and specificity of the ONSD measured via ultrasound for the prediction of increased intracranial pressure were 92% (86–96%) and 89% (77–96%), respectively. The pooled positive and negative likelihood ratios were 8.6 and 0.08, respectively. Conclusion and RelevanceOptic nerve ultrasonography stands out as a precise and valuable diagnostic tool applicable across diverse patient populations and clinical scenarios. We recommend routine ocular ultrasound for optic nerve sheath diameter measurement in pediatric patients to increase the accuracy of diagnosing increased intracranial pressure.

Deep learning algorithms for melanoma detection using dermoscopic images: A systematic review and meta-analysis

BackgroundMelanoma is a serious risk to human health and early identification is vital for treatment success. Deep learning (DL) has the potential to detect cancer using imaging technologies and many studies provide evidence that DL algorithms can achieve high accuracy in melanoma diagnostics. ObjectivesTo critically assess different DL performances in diagnosing melanoma using dermatoscopic images and discuss the relationship between dermatologists and DL. MethodsOvid-Medline, Embase, IEEE Xplore, and the Cochrane Library were systematically searched from inception until 7th December 2021. Studies that reported diagnostic DL model performances in detecting melanoma using dermatoscopic images were included if they had specific outcomes and histopathologic confirmation. Binary diagnostic accuracy data and contingency tables were extracted to analyze outcomes of interest, which included sensitivity (SEN), specificity (SPE), and area under the curve (AUC). Subgroup analyses were performed according to human-machine comparison and cooperation. The study was registered in PROSPERO, CRD42022367824. Results2309 records were initially retrieved, of which 37 studies met our inclusion criteria, and 27 provided sufficient data for meta-analytical synthesis. The pooled SEN was 82 % (range 77–86), SPE was 87 % (range 84–90), with an AUC of 0.92 (range 0.89–0.94). Human-machine comparison had pooled AUCs of 0.87 (0.84–0.90) and 0.83 (0.79–0.86) for DL and dermatologists, respectively. Pooled AUCs were 0.90 (0.87–0.93), 0.80 (0.76–0.83), and 0.88 (0.85–0.91) for DL, and junior and senior dermatologists, respectively. Analyses of human-machine cooperation were 0.88 (0.85–0.91) for DL, 0.76 (0.72–0.79) for unassisted, and 0.87 (0.84–0.90) for DL-assisted dermatologists. ConclusionsEvidence suggests that DL algorithms are as accurate as senior dermatologists in melanoma diagnostics. Therefore, DL could be used to support dermatologists in diagnostic decision-making. Although, further high-quality, large-scale multicenter studies are required to address the specific challenges associated with medical AI-based diagnostics.

Diagnostic accuracy of active pulmonary tuberculosis screening during detention admission: a systematic review.

Individuals entering incarceration are at high risk for infectious diseases, other ill conditions, and risky behavior. Typically, the status of active pulmonary tuberculosis (PTB) is not known at the time of admission. Early detection and treatment are essential for effective TB control. So far, no study has compared the diagnostic accuracy of various TB screening tools in detention using a network meta-analysis (NMA). We aimed to investigate the diagnostic accuracy of active PTB screening tests upon detention admission. We searched PubMed, Global Index Medicus, the Cochrane Library electronic databases, and grey literature for publications reporting detention TB entry screening in March 2022 and January 2024. Inclusion was non-restrictive regarding time, language, location, reference standards, or screening tests. Eligible study designs comprised comparative, observational, and diagnostic studies. Publications had to report TB screening of individuals entering confinement and provide data for diagnostic accuracy calculations. The QUADAS-2 tool was designed to assess the quality of primary diagnostic accuracy studies. This systematic review was registered with PROSPERO (CRD42022307863) and conducted without external funding. We screened a total of 2,455 records. Despite extensive searching, no studies met our inclusion criteria. However, we identified evidence revealing key differences in screening algorithm application. In conclusion, more diagnostic accuracy data on TB screening algorithms for detention admission worldwide needs to be collected. We recommend that global TB initiatives set up multi-site studies to investigate the diagnostic accuracy of TB screening on admission in low- and high-prevalence criminal justice systems. Further network meta-analyses of these studies could inform policymakers and public health experts to establish or fine-tune TB control in detention settings.

Application of CT and MRI images based on artificial intelligence to predict lymph node metastases in patients with oral squamous cell carcinoma: a subgroup meta-analysis.

The performance of artificial intelligence (AI) in the prediction of lymph node (LN) metastasis in patients with oral squamous cell carcinoma (OSCC) has not been quantitatively evaluated. The purpose of this study was to conduct a systematic review and meta-analysis of published data on the diagnostic performance of CT and MRI based on AI algorithms for predicting LN metastases in patients with OSCC. We searched the Embase, PubMed (Medline), Web of Science, and Cochrane databases for studies on the use of AI in predicting LN metastasis in OSCC. Binary diagnostic accuracy data were extracted to obtain the outcomes of interest, namely, the area under the curve (AUC), sensitivity, and specificity, and compared the diagnostic performance of AI with that of radiologists. Subgroup analyses were performed with regard to different types of AI algorithms and imaging modalities. Fourteen eligible studies were included in the meta-analysis. The AUC, sensitivity, and specificity of the AI models for the diagnosis of LN metastases were 0.92 (95% CI 0.89-0.94), 0.79 (95% CI 0.72-0.85), and 0.90 (95% CI 0.86-0.93), respectively. Promising diagnostic performance was observed in the subgroup analyses based on algorithm types [machine learning (ML) or deep learning (DL)] and imaging modalities (CT vs. MRI). The pooled diagnostic performance of AI was significantly better than that of experienced radiologists. In conclusion, AI based on CT and MRI imaging has good diagnostic accuracy in predicting LN metastasis in patients with OSCC and thus has the potential for clinical application. https://www.crd.york.ac.uk/PROSPERO/#recordDetails, PROSPERO (No. CRD42024506159).

Topological 3D reconstruction of multiple anatomical structures from volumetric medical data

In the medical field, usually, practitioners mainly base their analysis on 2D slices produced from MRI or CT-scans that correspond to restricted views of a pathology. To facilitate the work of doctors, increase diagnostic accuracy and cross-reference multimodal data, a 3D reconstruction is required. However, most of the time, reconstruction methods fail at visualizing complex and noisy data made up of several tissues. Indeed, these methods often build each tissue independently so that the consistency of the global model is not ensured: overlaps may appear between segments whereas some disjointed volumes exhibit empty spaces. This paper presents a complete topologically consistent reconstruction system from 3D medical acquisitions such as MRI or CT-scans. Compared to other methods, our system offers a single volumetric representation of an organ corresponding to a 3D space partition, where a semantic label is associated to each volume to identify the represented tissue and adjacency between volumes is explicitly and precisely defined. This partition is controlled and free from topological and geometric defects usually found in other 3D reconstruction approaches. Experimental studies were conducted on MRI datasets of brains resulting in consistent reconstructions. An application of the model for calculating the distribution of physiological data in brain tissue is also shown.

The influence of manual segmentation strategies and different phases selection on machine learning-based computed tomography in renal tumors: a systematic review and meta-analysis.

Delineating the region/volume of interest (ROI/VOI) and selecting the phases are of importance in developing machine learning (ML). The results will change when choosing different methods of drawing the ROI/VOI and selecting different phases. However, there is no related standard for delineating the ROI/VOI and selecting the phases in renal tumors to develop ML based on computed tomography (CT). The PubMed and Web of Science were searched for related studies published until March 1, 2023. Inclusion criteria were studies that developed ML models in renal tumors from CT images. And the binary diagnostic accuracy data were extracted to obtain the outcomes, such as sensitivity (SE), specificity (SP), accuracy (ACC), and area under the curve (AUC). Twenty-three papers were included in the meta-analysis with a pooled SE of 87% (95% CI 85-88%), SP of 82% (95% CI 79-85%), and AUC of 91% (95% CI 89-93%) in phases; a pooled SE of 82% (95% CI 80-84%), SP of 85% (95% CI 83-86%), and AUC of 90% (95% CI 88-93%) in phases combined with delineating strategies, respectively. In all different combinations, the contour-focused and single phase produce the highest AUC of 93% (95% CI 90-95%). In subgroup analyses (sample size, year of publication, and geographical distribution), the performance was acceptable on phases and phases combined strategies. To explore the effect of manual segmentation strategies and different phases selection on ML-based CT, we find that the method of single phase (CMP or NP) combined with contour-focused was considered a better strategy compared to the other strategies.

Point-of-care C-reactive protein and Xpert MTB/RIF Ultra for tuberculosis screening and diagnosis in unselected antiretroviral therapy initiators: a prospective, cross-sectional, diagnostic accuracy study

Tuberculosis, a major cause of death in people living with HIV, remains challenging to diagnose. Diagnostic accuracy data are scarce for promising triage and confirmatory tests such as C-reactive protein (CRP), sputum and urine Xpert MTB/RIF Ultra (Xpert Ultra), and urine Determine TB LAM Ag (a lateral flow lipoarabinomannan [LF-LAM] test), without symptom selection. We evaluated novel triage and confirmatory tests in ambulatory people with HIV initiating antiretroviral therapy (ART). 897 ART-initiators were recruited irrespective of symptoms and sputum induction offered. For triage (n=800), we evaluated point-of-care blood-based CRP testing, compared with the WHO-recommended four-symptom screen (W4SS). For sputum-based confirmatory testing (n=787), we evaluated Xpert Ultra versus Xpert MTB/RIF (Xpert). For urine-based confirmatory testing (n=732), we evaluated Xpert Ultra and LF-LAM. We used a sputum culture reference standard. 463 (52%) of 897 participants were female. The areas under the receiver operator characteristic curves for CRP was 0·78 (95% CI 0·73-0·83) and for number of W4SS symptoms was 0·70 (0·64-0·75). CRP (≥10 mg/L) had similar sensitivity to W4SS (77% [95% CI 68-85; 80/104] vs 77% [68-85; 80/104]; p>0·99] but higher specificity (64% [61-68; 445/696] vs 48% [45-52; 334/696]; p<0·0001]; reducing unnecessary confirmatory testing by 138 (95% CI 117-160) per 1000 people and number-needed-to-test from 6·91 (95% CI 6·25-7·81) to 4·87 (4·41-5·51). Sputum samples with Xpert Ultra, which required induction in 49 (31%) of 158 of people (95% CI 24-39), had higher sensitivity than Xpert (71% [95% CI 61-80; 74/104] vs 56% [46-66; 58/104]; p<0·0001). Of the people with one or more confirmatory sputum or urine test results that were positive, the proportion detected by Xpert Ultra increased from 45% (26-64) to 66% (46-82) with induction. Programmatically done haemoglobin, triage test combinations, and urine tests showed comparatively worse results. CRP is a more specific triage test than W4SS in those initiating ART. Sputum induction improves diagnostic yield. Sputum samples with Xpert Ultra is a more accurate confirmatory test than with Xpert. South African Medical Research Council, EDCTP2, US National Institutes of Health-National Institute of Allergy and Infectious Diseases.

The awareness and perception of teledentistry among Malaysian dental professionals in universities and private practices

The unexpected outbreak of COVID-19 has caused the temporary collapse and suspension of dental practices around the world. With the emergence of teledentistry, this incident unwittingly pushes for a revolutionary modification in the usual provision of dental care. The purpose of the study was to determine the level of awareness and perception of teledentistry among university and private-sector dental practitioners. This descriptive cross-sectional study used a pre-validated 27-item questionnaire with a 5-point Likert scale to assess the perceptions of university-based and private dental practitioners regarding teledentistry in four domains: the usefulness of teledentistry for patients, its benefits to dental practices, the potential improvements it can bring to dental practices, and concerns about the adverse effects of teledentistry. 150 dental practitioners participated in the research (30% overall response rate). More than 70% of respondents agreed that teledentistry can benefit both dentists and patients by improving communication and treatment monitoring. However, 45–80% of dentists had doubts regarding the technology's dependability, diagnostic accuracy, and patient data privacy. In conclusion, the results of this study suggest that Malaysian dentists are well-informed and prepared to implement teledentistry in their practices. However, additional research is necessary to determine the viability of commercial usage of teledentistry, both in Malaysia and internationally.

Deep learning performance compared to healthcare experts in detecting wrist fractures from radiographs: A systematic review and meta-analysis

ObjectiveTo perform a systematic review and meta-analysis of the diagnostic accuracy of deep learning (DL) algorithms in the diagnosis of wrist fractures (WF) on plain wrist radiographs, taking healthcare experts consensus as reference standard. MethodsEmbase, Medline, PubMed, Scopus and Web of Science were searched in the period from 1 Jan 2012 to 9 March 2023. Eligible studies were patients with wrist radiographs for radial and ulnar fractures as the target condition, studies using DL algorithms based on convolutional neural networks (CNN), and healthcare experts consensus as the minimum reference standard. Studies were assessed with a modified QUADAS-2 tool, and we applied a bivariate random-effects model for meta-analysis of diagnostic test accuracy data. ResultsOur study was registered at PROSPERO with ID: CRD42023431398. We included 6 unique studies for meta-analysis, with a total of 33,026 radiographs. CNN performance compared to reference standards for the included articles found a summary sensitivity of 92% (95% CI: 80%–97%) and a summary specificity of 93% (95% CI: 76%–98%). The generalized bivariate I-squared statistic indicated considerable heterogeneity between the studies (81.90%). Four studies had one or more domains at high risk of bias and two studies had concerns regarding applicability. ConclusionThe diagnostic accuracy of CNNs was comparable to that of healthcare experts in wrist radiographs for investigation of WF. There is a need for studies with a robust reference standard, external data-set validation and investigation of diagnostic performance of healthcare experts aided with CNNs. Clinical relevance statementDL matches healthcare experts in diagnosing WFs, which potentially benefits patient diagnosis.

Donor-derived cell-free DNA as a diagnostic marker for kidney-allograft rejection: A systematic review and meta-analysis.

Donor-derived cell-free DNA (dd-cfDNA) has emerged as a promising biomarker for detecting graft rejection. This study aimed to evaluate the diagnostic accuracy and clinical value of applying it to kidney transplant rejection. Relevant literature on dd-cfDNA diagnostics in kidney transplant rejection was reviewed from PubMed, Embase, Cochrane Library, and Web of Science databases up to 2023. Data and study characteristics were extracted independently by two researchers, and disagreements were resolved through discussion. Diagnostic accuracy data for any rejection (AR) and antibody-mediated rejection (ABMR) were analyzed separately. Potential heterogeneity was analyzed by subgroup analysis or meta-regression. Funnel plots were used to clarify the presence or absence of publication bias. Nine publications provided data on dd-cfDNA accuracy in diagnosing patients with AR. The pooled sensitivity, specificity, and the area under the receiver operating characteristic (AUROC) curve with 95% confidence intervals (CI) were 0.59 (95% CI, 0.48-0.69), 0.83 (95% CI, 0.76-0.88), and 0.80 (95% CI, 0.76-0.83), respectively. Additionally, 12 studies focused on the diagnostic accuracy of dd-cfDNA for ABMR, showing pooled sensitivity, specificity, and the AUROC curve with 95% CI of 0.81 (95% CI, 0.72-0.88), 0.80 (95% CI, 0.73-0.86), and 0.87 (95% CI, 0.84-0.90), respectively. Study type, age group, and sample size contributed to heterogeneity. In summary, our findings indicate that while plasma dd-cfDNA accuracy in diagnosing patients with AR is limited by significant heterogeneity, it is a valuable biomarker for diagnosing ABMR.

SAMe-TT2R2 Score to Predict Time in Therapeutic Range of Vitamin K Antagonists in Asian and Non-Asian patients: A Systematic Review and Meta-analysis.

Sex, age, medical history, treatment, tobacco use, and race (SAMe-TT2R2) score helps detect patients at risk of suboptimal anticoagulation control. A score above two suggests poor control; however, non-Caucasian status being assigned two points might hinder the recognition of poor control in patients of other races. To evaluate the SAMe-TT2R2 score's ability to predict poor anticoagulation control [defined as time in therapeutic range (TTR) < 60-70%] in Asian and non-Asian populations on vitamin K antagonists (VKAs). We searched PubMed, Cochrane Library, Scopus, SpringerLink, and Web of Science using the keyword "SAMe-TT2R2." Articles published before April 2022 were screened. We gathered mean TTR and diagnostic accuracy data for different SAMe-TT2R2 thresholds and conducted meta-analyses using random-effects models. A total of 30 studies were included (N = 36,690). The overall mean TTR differences were -4.88 and -6.41 for the cutoffs of ≥ 3 and ≥ 4, respectively. For non-Asian patients, the mean TTR differences were -3.86, -5.12, and -8.09 for the cutoffs ≥ 2, ≥ 3, and ≥ 4, respectively. For Asian patients, the mean TTR differences were -3.99 and -4.07 for the cut-offs ≥ 3 and ≥ 4, respectively. The highest positive likelihood ratio (LR+) for the Asian subgroup was 1.17 [95% confidence interval (CI): 1.06-1.28; I2 = 0%, p heterogeneity = 0.500] at cutoff ≥4 and for the non-Asian subgroup, at cut-off ≥ 3, the LR+ was 1.24 (95% CI 1.14-1.34; I2 = 0% p heterogeneity = 0.455). The lowest LR- was found at a lower cutoff for both races (at cutoff ≥ 3 and ≥ 2 for Asian and non-Asian subgroups, respectively). The pooled results of other accuracy parameters were modest at all cutoffs, except for the sensitivity at cutoff ≥ 3 in the Asian subgroup (83.05%). Our study results suggest that a higher SAMe-TT2R2 score resulted in a greater reduction of TTR among Asian and all races. The accuracy parameters showed the highest sensitivity for poor TTR at the SAMe-TT2R2 cutoff of ≥ 3 for Asian patients. However, the ability to identify patients likely to have poor TTR was limited. Further research is needed to enhance the risk assessment for poor anticoagulation control with VKAs. The protocol of this systematic review was registered in the International Prospective Register of Scientific Reviews: PROSPERO, registration number CRD42021291865.

Diagnostic performance of artificial intelligence-assisted PET imaging for Parkinson’s disease: a systematic review and meta-analysis

Artificial intelligence (AI)-assisted PET imaging is emerging as a promising tool for the diagnosis of Parkinson’s disease (PD). We aim to systematically review the diagnostic accuracy of AI-assisted PET in detecting PD. The Ovid MEDLINE, Ovid Embase, Web of Science, and IEEE Xplore databases were systematically searched for related studies that developed an AI algorithm in PET imaging for diagnostic performance from PD and were published by August 17, 2023. Binary diagnostic accuracy data were extracted for meta-analysis to derive outcomes of interest: area under the curve (AUC). 23 eligible studies provided sufficient data to construct contingency tables that allowed the calculation of diagnostic accuracy. Specifically, 11 studies were identified that distinguished PD from normal control, with a pooled AUC of 0.96 (95% CI: 0.94–0.97) for presynaptic dopamine (DA) and 0.90 (95% CI: 0.87–0.93) for glucose metabolism (18F-FDG). 13 studies were identified that distinguished PD from the atypical parkinsonism (AP), with a pooled AUC of 0.93 (95% CI: 0.91 − 0.95) for presynaptic DA, 0.79 (95% CI: 0.75–0.82) for postsynaptic DA, and 0.97 (95% CI: 0.96–0.99) for 18F-FDG. Acceptable diagnostic performance of PD with AI algorithms-assisted PET imaging was highlighted across the subgroups. More rigorous reporting standards that take into account the unique challenges of AI research could improve future studies.