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Seminars in Arthritis and Rheumatism Oct 2022Giant cell arteritis (GCA) and polymyalgia rheumatica (PMR) can be concurrent diseases. We aimed to estimate the point-prevalence of concurrent GCA and PMR.... (Meta-Analysis)
Meta-Analysis Review
INTRODUCTION
Giant cell arteritis (GCA) and polymyalgia rheumatica (PMR) can be concurrent diseases. We aimed to estimate the point-prevalence of concurrent GCA and PMR. Additionally, an incidence rate (IR) of GCA presenting after PMR diagnosis in patients was estimated.
METHODS
Two authors performed a systematic literature search, data extraction and risk of bias assessment independently. Studies assessing cohorts of patients presenting with both GCA and PMR were included. The outcomes were point-prevalence of concurrent GCA and PMR and IR for development of GCA after PMR diagnosis. A meta-analysis was performed to calculate a pooled prevalence of concurrent PMR and GCA.
RESULTS
We identified 29 studies investigating concurrent GCA and PMR. Only two studies applied imaging systematically to diagnose GCA and none to diagnose PMR. GCA presenting after PMR diagnosis was assessed in 12 studies but imaging was not applied systematically. The point-prevalence of concurrent GCA present at PMR diagnosis ranged from 6%-66%. The pooled estimate of the point-prevalence from the meta-analysis was 22%. The point-prevalence of PMR present at GCA diagnosis ranged from 16%-65%. The pooled estimate of the point-prevalence from the meta-analysis was 42%. The IR ranged between 2-78 cases of GCA presenting after PMR per 1000 person-years.
CONCLUSION
This review and meta-analysis support that concurrent GCA and PMR is frequently present at the time of diagnosis. Additionally, we present the current evidence of GCA presenting in patients after PMR diagnosis. These results emphasize the need for studies applying imaging modalities to diagnose GCA.
Topics: Diagnostic Imaging; Giant Cell Arteritis; Humans; Incidence; Polymyalgia Rheumatica; Prevalence
PubMed: 35858507
DOI: 10.1016/j.semarthrit.2022.152069 -
Journal of Obstetrics and Gynaecology :... Dec 2024The diagnosis of endometriomas in patients with endometriosis is of primary importance because it influences the management and prognosis of infertility and pain.... (Meta-Analysis)
Meta-Analysis Review
INTRODUCTION
The diagnosis of endometriomas in patients with endometriosis is of primary importance because it influences the management and prognosis of infertility and pain. Imaging techniques are evolving constantly. This study aimed to systematically assess the diagnostic accuracy of transvaginal ultrasound (TVUS) and magnetic resonance imaging (MRI) in detecting endometrioma using the surgical visualisation of lesions with or without histopathological confirmation as reference standards in patients of reproductive age with suspected endometriosis.
METHODS
PubMed, Embase, Web of Science, Cumulative Index to Nursing and Allied Health Literature and ClinicalTrials.gov databases were searched from their inception to 12 October 2022, using a manual search for additional articles. Two authors independently performed title, abstract and full-text screening of the identified records, extracted study details and quantitative data and assessed the quality of the studies using the 'Quality Assessment of Diagnostic Accuracy Study 2' tool. Bivariate random-effects models were used to determine the pooled sensitivity and specificity, compare the two imaging modalities and evaluate the sources of heterogeneity.
RESULTS
Sixteen prospective studies (10 assessing TVUS, 4 assessing MRI and 2 assessing both TVUS and MRI) were included, representing 1976 participants. Pooled TVUS and MRI sensitivities for endometrioma were 0.89 (95% confidence interval 'CI', 0.86-0.92) and 0.94 (95% CI, 0.74-0.99), respectively (indirect comparison -value of 0.47). Pooled TVUS and MRI specificities for endometrioma were 0.95 (95% CI, 0.92-0.97) and 0.94 (95% CI, 0.89-0.97), respectively (indirect comparison p-value of 0.51). These studies had a high or unclear risk of bias. A direct comparison (all participants undergoing TVUS and MRI) of the modalities was available in only two studies.
CONCLUSION
TVUS and MRI have high accuracy for diagnosing endometriomas; however, high-quality studies comparing the two modalities are lacking.
Topics: Female; Humans; Endometriosis; Prospective Studies; Ultrasonography; Magnetic Resonance Imaging; Sensitivity and Specificity; Diagnostic Tests, Routine
PubMed: 38348799
DOI: 10.1080/01443615.2024.2311664 -
Journal of Magnetic Resonance Imaging :... Jun 2016To perform a systematic review and meta-analysis of all published studies since 2005 that evaluate the accuracy of magnetic resonance imaging (MRI) for the diagnosis of... (Meta-Analysis)
Meta-Analysis Review
PURPOSE
To perform a systematic review and meta-analysis of all published studies since 2005 that evaluate the accuracy of magnetic resonance imaging (MRI) for the diagnosis of acute appendicitis in the general population presenting to emergency departments.
MATERIALS AND METHODS
All retrospective and prospective studies evaluating the accuracy of MRI to diagnose appendicitis published in English and listed in PubMed, Web of Science, Cinahl Plus, and the Cochrane Library since 2005 were included. Excluded studies were those without an explicitly stated reference standard, with insufficient data to calculate the study outcomes, or if the population enrolled was limited to pregnant women or children. Data were abstracted by one investigator and confirmed by another. Data included the number of true positives, true negatives, false positives, false negatives, number of equivocal cases, type of MRI scanner, type of MRI sequence, and demographic data including study setting and gender distribution. Summary test characteristics were calculated. Forest plots and a summary receiver operator characteristic plot were generated.
RESULTS
Ten studies met eligibility criteria, representing patients from seven countries. Nine were prospective and two were multicenter studies. A total of 838 subjects were enrolled; 406 (48%) were women. All studies routinely used unenhanced MR images, although two used intravenous contrast-enhancement and three used diffusion-weighted imaging. Using a bivariate random-effects model the summary sensitivity was 96.6% (95% confidence interval [CI]: 92.3%-98.5%) and summary specificity was 95.9% (95% CI: 89.4%-98.4%).
CONCLUSION
MRI has a high sensitivity and specificity for the diagnosis of appendicitis, similar to that reported previously for computed tomography. J. Magn. Reson. Imaging 2016;43:1346-1354.
Topics: Adolescent; Adult; Aged; Aged, 80 and over; Appendicitis; Child; Emergency Medical Services; Evidence-Based Medicine; Female; Humans; Magnetic Resonance Imaging; Male; Middle Aged; Prevalence; Reproducibility of Results; Risk Factors; Sensitivity and Specificity; Young Adult
PubMed: 26691590
DOI: 10.1002/jmri.25115 -
PloS One 2022Artificial intelligence (AI) algorithms have been applied to diagnose temporomandibular disorders (TMDs). However, studies have used different patient selection... (Meta-Analysis)
Meta-Analysis
BACKGROUND
Artificial intelligence (AI) algorithms have been applied to diagnose temporomandibular disorders (TMDs). However, studies have used different patient selection criteria, disease subtypes, input data, and outcome measures. Resultantly, the performance of the AI models varies.
OBJECTIVE
This study aimed to systematically summarize the current literature on the application of AI technologies for diagnosis of different TMD subtypes, evaluate the quality of these studies, and assess the diagnostic accuracy of existing AI models.
MATERIALS AND METHODS
The study protocol was carried out based on the preferred reporting items for systematic review and meta-analysis protocols (PRISMA). The PubMed, Embase, and Web of Science databases were searched to find relevant articles from database inception to June 2022. Studies that used AI algorithms to diagnose at least one subtype of TMD and those that assessed the performance of AI algorithms were included. We excluded studies on orofacial pain that were not directly related to the TMD, such as studies on atypical facial pain and neuropathic pain, editorials, book chapters, and excerpts without detailed empirical data. The risk of bias was assessed using the QUADAS-2 tool. We used Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) to provide certainty of evidence.
RESULTS
A total of 17 articles for automated diagnosis of masticatory muscle disorders, TMJ osteoarthrosis, internal derangement, and disc perforation were included; they were retrospective studies, case-control studies, cohort studies, and a pilot study. Seven studies were subjected to a meta-analysis for diagnostic accuracy. According to the GRADE, the certainty of evidence was very low. The performance of the AI models had accuracy and specificity ranging from 84% to 99.9% and 73% to 100%, respectively. The pooled accuracy was 0.91 (95% CI 0.76-0.99), I2 = 97% (95% CI 0.96-0.98), p < 0.001.
CONCLUSIONS
Various AI algorithms developed for diagnosing TMDs may provide additional clinical expertise to increase diagnostic accuracy. However, it should be noted that a high risk of bias was present in the included studies. Also, certainty of evidence was very low. Future research of higher quality is strongly recommended.
Topics: Artificial Intelligence; Facial Pain; Humans; Pilot Projects; Retrospective Studies; Temporomandibular Joint Disorders
PubMed: 35980894
DOI: 10.1371/journal.pone.0272715 -
Diseases of the Esophagus : Official... Nov 2023Early detection of esophageal cancer is limited by accurate endoscopic diagnosis of subtle macroscopic lesions. Endoscopic interpretation is subject to expertise,... (Meta-Analysis)
Meta-Analysis
Early detection of esophageal cancer is limited by accurate endoscopic diagnosis of subtle macroscopic lesions. Endoscopic interpretation is subject to expertise, diagnostic skill, and thus human error. Artificial intelligence (AI) in endoscopy is increasingly bridging this gap. This systematic review and meta-analysis consolidate the evidence on the use of AI in the endoscopic diagnosis of esophageal cancer. The systematic review was carried out using Pubmed, MEDLINE and Ovid EMBASE databases and articles on the role of AI in the endoscopic diagnosis of esophageal cancer management were included. A meta-analysis was also performed. Fourteen studies (1590 patients) assessed the use of AI in endoscopic diagnosis of esophageal squamous cell carcinoma-the pooled sensitivity and specificity were 91.2% (84.3-95.2%) and 80% (64.3-89.9%). Nine studies (478 patients) assessed AI capabilities of diagnosing esophageal adenocarcinoma with the pooled sensitivity and specificity of 93.1% (86.8-96.4) and 86.9% (81.7-90.7). The remaining studies formed the qualitative summary. AI technology, as an adjunct to endoscopy, can assist in accurate, early detection of esophageal malignancy. It has shown superior results to endoscopists alone in identifying early cancer and assessing depth of tumor invasion, with the added benefit of not requiring a specialized skill set. Despite promising results, the application in real-time endoscopy is limited, and further multicenter trials are required to accurately assess its use in routine practice.
Topics: Humans; Esophageal Neoplasms; Esophageal Squamous Cell Carcinoma; Artificial Intelligence; Endoscopy; Adenocarcinoma
PubMed: 37480192
DOI: 10.1093/dote/doad048 -
BMJ Clinical Evidence Jan 2016The incidence of malignant melanoma has increased over the past 25 years in the UK, but death rates have remained fairly constant. The 5-year survival rate ranges from... (Review)
Review
INTRODUCTION
The incidence of malignant melanoma has increased over the past 25 years in the UK, but death rates have remained fairly constant. The 5-year survival rate ranges from 20% to 95%, depending on disease stage. Risks are greater in white populations and in people with higher numbers of skin naevi.
METHODS AND OUTCOMES
We conducted a systematic overview, aiming to answer the following clinical question: What is the evidence for performing a sentinel lymph node biopsy in people with malignant melanoma with clinically uninvolved lymph nodes? We searched: Medline, Embase, The Cochrane Library and other important databases up to October 2014 (Clinical Evidence overviews are updated periodically; please check our website for the most up-to-date version of this overview).
RESULTS
At this update, searching of electronic databases retrieved 221 studies. After deduplication and removal of conference abstracts, 99 records were screened for inclusion in the overview. Appraisal of titles and abstracts led to the exclusion of 58 studies and the further review of 41 full publications. Of the 41 full articles evaluated, one systematic review and three RCTs were added at this update. We performed a GRADE evaluation for two PICO combinations.
CONCLUSIONS
In this systematic overview, we evaluated the evidence for performing sentinel lymph node biopsy in people with malignant melanoma with clinically uninvolved lymph nodes.
Topics: Humans; Melanoma; Sentinel Lymph Node Biopsy
PubMed: 26788739
DOI: No ID Found -
The Lancet. Digital Health Dec 2023Machine learning and deep learning models have been increasingly used to predict long-term disease progression in patients with chronic obstructive pulmonary disease... (Meta-Analysis)
Meta-Analysis
Machine learning and deep learning predictive models for long-term prognosis in patients with chronic obstructive pulmonary disease: a systematic review and meta-analysis.
BACKGROUND
Machine learning and deep learning models have been increasingly used to predict long-term disease progression in patients with chronic obstructive pulmonary disease (COPD). We aimed to summarise the performance of such prognostic models for COPD, compare their relative performances, and identify key research gaps.
METHODS
We conducted a systematic review and meta-analysis to compare the performance of machine learning and deep learning prognostic models and identify pathways for future research. We searched PubMed, Embase, the Cochrane Library, ProQuest, Scopus, and Web of Science from database inception to April 6, 2023, for studies in English using machine learning or deep learning to predict patient outcomes at least 6 months after initial clinical presentation in those with COPD. We included studies comprising human adults aged 18-90 years and allowed for any input modalities. We reported area under the receiver operator characteristic curve (AUC) with 95% CI for predictions of mortality, exacerbation, and decline in forced expiratory volume in 1 s (FEV). We reported the degree of interstudy heterogeneity using Cochran's Q test (significant heterogeneity was defined as p≤0·10 or I>50%). Reporting quality was assessed using the TRIPOD checklist and a risk-of-bias assessment was done using the PROBAST checklist. This study was registered with PROSPERO (CRD42022323052).
FINDINGS
We identified 3620 studies in the initial search. 18 studies were eligible, and, of these, 12 used conventional machine learning and six used deep learning models. Seven models analysed exacerbation risk, with only six reporting AUC and 95% CI on internal validation datasets (pooled AUC 0·77 [95% CI 0·69-0·85]) and there was significant heterogeneity (I 97%, p<0·0001). 11 models analysed mortality risk, with only six reporting AUC and 95% CI on internal validation datasets (pooled AUC 0·77 [95% CI 0·74-0·80]) with significant degrees of heterogeneity (I 60%, p=0·027). Two studies assessed decline in lung function and were unable to be pooled. Machine learning and deep learning models did not show significant improvement over pre-existing disease severity scores in predicting exacerbations (p=0·24). Three studies directly compared machine learning models against pre-existing severity scores for predicting mortality and pooled performance did not differ (p=0·57). Of the five studies that performed external validation, performance was worse than or equal to regression models. Incorrect handling of missing data, not reporting model uncertainty, and use of datasets that were too small relative to the number of predictive features included provided the largest risks of bias.
INTERPRETATION
There is limited evidence that conventional machine learning and deep learning prognostic models demonstrate superior performance to pre-existing disease severity scores. More rigorous adherence to reporting guidelines would reduce the risk of bias in future studies and aid study reproducibility.
FUNDING
None.
Topics: Adult; Humans; Reproducibility of Results; Deep Learning; Quality of Life; Pulmonary Disease, Chronic Obstructive; Prognosis
PubMed: 38000872
DOI: 10.1016/S2589-7500(23)00177-2 -
PLoS Neglected Tropical Diseases Mar 2018Diagnosing scrub typhus clinically is difficult, hence laboratory tests play a very important role in diagnosis. As performing sophisticated laboratory tests in... (Meta-Analysis)
Meta-Analysis Review
BACKGROUND
Diagnosing scrub typhus clinically is difficult, hence laboratory tests play a very important role in diagnosis. As performing sophisticated laboratory tests in resource-limited settings is not feasible, accurate point-of-care testing (POCT) for scrub typhus diagnosis would be invaluable for patient diagnosis and management. Here we summarise the existing evidence on the accuracy of scrub typhus POCTs to inform clinical practitioners in resource-limited settings of their diagnostic value.
METHODOLOGY/PRINCIPAL FINDINGS
Studies on POCTs which can be feasibly deployed in primary health care or outpatient settings were included. Thirty-one studies were identified through PubMed and manual searches of reference lists. The quality of the studies was assessed with the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2). About half (n = 14/31) of the included studies were of moderate quality. Meta-analysis showed the pooled sensitivity and specificity of commercially available immunochromatographic tests (ICTs) were 66.0% (95% CI 0.37-0.86) and 92.0% (95% CI 0.83-0.97), respectively. There was a significant and high degree of heterogeneity between the studies (I2 value = 97.48%, 95% CI 96.71-98.24 for sensitivity and I2 value = 98.17%, 95% CI 97.67-98.67 for specificity). Significant heterogeneity was observed for total number of samples between studies (p = 0.01), study design (whether using case-control design or not, p = 0.01), blinding during index test interpretation (p = 0.02), and QUADAS-2 score (p = 0.01).
CONCLUSIONS/SIGNIFICANCE
There was significant heterogeneity between the scrub typhus POCT diagnostic accuracy studies examined. Overall, the commercially available scrub typhus ICTs demonstrated better performance when 'ruling in' the diagnosis. There is a need for standardised methods and reporting of diagnostic accuracy to decrease between-study heterogeneity and increase comparability among study results, as well as development of an affordable and accurate antigen-based POCT to tackle the inherent weaknesses associated with serological testing.
Topics: Chromatography, Affinity; Humans; Orientia tsutsugamushi; Point-of-Care Testing; Scrub Typhus; Sensitivity and Specificity; Serologic Tests
PubMed: 29579046
DOI: 10.1371/journal.pntd.0006330 -
Journal of Investigative Medicine High... 2021Jejunal Dieulafoy's lesion is an exceedingly rare but important cause of gastrointestinal bleeding. It frequently presents as a diagnostic and therapeutic conundrum due...
Jejunal Dieulafoy's lesion is an exceedingly rare but important cause of gastrointestinal bleeding. It frequently presents as a diagnostic and therapeutic conundrum due to the rare occurrence, intermittent bleeding symptoms often requiring prompt clinical action, variability in the detection and treatment methods, and the risk of rebleeding. We performed a systematic literature search of MEDLINE, Cochrane, Embase, and Scopus databases regarding jejunal Dieulafoy's lesio from inception till June 2020. A total of 136 cases were retrieved from 76 articles. The mean age was 55 ± 24 years, with 55% of cases reported in males. Patients commonly presented with melena (33%), obscure-overt gastrointestinal bleeding (29%), and hemodynamic compromise (20%). Hypertension (26%), prior gastrointestinal surgery (14%), and valvular heart disease (13%) were the major underlying disorders. Conventional endoscopy often failed but single- and double-balloon enteroscopy identified the lesion in 96% and 98% of patients, respectively. There was no consensus on the treatment. Endoscopic therapy was instituted in 64% of patients. Combination therapy (34%) with two or more endoscopic modalities, was the preferred approach. With regard to endoscopic monotherapy, hemoclipping (19%) and argon plasma coagulation (4%) were frequently employed procedures. Furthermore, direct surgical intervention in 32% and angiographic embolization was performed in 4% of patients. The rebleeding rate was 13.4%, with a mean follow-up duration of 17.6 ± 21.98 months. The overall mortality rate was 4.4%. Jejunal Dieulafoy's lesion is still difficult to diagnose and manage. Although the standard diagnostic and therapeutic modalities remain to be determined, device-assisted enteroscopy might yield promising outcomes.
Topics: Endoscopy, Gastrointestinal; Gastrointestinal Hemorrhage; Humans; Male; Middle Aged
PubMed: 33472441
DOI: 10.1177/2324709620987703 -
Journal of Medical Internet Research Dec 2021Interpretation of capsule endoscopy images or movies is operator-dependent and time-consuming. As a result, computer-aided diagnosis (CAD) has been applied to enhance... (Meta-Analysis)
Meta-Analysis Review
BACKGROUND
Interpretation of capsule endoscopy images or movies is operator-dependent and time-consuming. As a result, computer-aided diagnosis (CAD) has been applied to enhance the efficacy and accuracy of the review process. Two previous meta-analyses reported the diagnostic performance of CAD models for gastrointestinal ulcers or hemorrhage in capsule endoscopy. However, insufficient systematic reviews have been conducted, which cannot determine the real diagnostic validity of CAD models.
OBJECTIVE
To evaluate the diagnostic test accuracy of CAD models for gastrointestinal ulcers or hemorrhage using wireless capsule endoscopic images.
METHODS
We conducted core databases searching for studies based on CAD models for the diagnosis of ulcers or hemorrhage using capsule endoscopy and presenting data on diagnostic performance. Systematic review and diagnostic test accuracy meta-analysis were performed.
RESULTS
Overall, 39 studies were included. The pooled area under the curve, sensitivity, specificity, and diagnostic odds ratio of CAD models for the diagnosis of ulcers (or erosions) were .97 (95% confidence interval, .95-.98), .93 (.89-.95), .92 (.89-.94), and 138 (79-243), respectively. The pooled area under the curve, sensitivity, specificity, and diagnostic odds ratio of CAD models for the diagnosis of hemorrhage (or angioectasia) were .99 (.98-.99), .96 (.94-0.97), .97 (.95-.99), and 888 (343-2303), respectively. Subgroup analyses showed robust results. Meta-regression showed that published year, number of training images, and target disease (ulcers vs erosions, hemorrhage vs angioectasia) was found to be the source of heterogeneity. No publication bias was detected.
CONCLUSIONS
CAD models showed high performance for the optical diagnosis of gastrointestinal ulcer and hemorrhage in wireless capsule endoscopy.
Topics: Capsule Endoscopy; Computers; Diagnostic Tests, Routine; Hemorrhage; Humans; Ulcer
PubMed: 34904949
DOI: 10.2196/33267