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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 Ultrasound Jun 2023Necrotizing fasciitis (NF) is a rapidly progressive necrosis of the fascial layer with a high mortality rate. It is a life-threatening medical emergency that requires... (Review)
Review
INTRODUCTION
Necrotizing fasciitis (NF) is a rapidly progressive necrosis of the fascial layer with a high mortality rate. It is a life-threatening medical emergency that requires urgent treatment. Lack of skin finding in NF made diagnosis difficult and required a high clinical index of suspicion. The use of ultrasound may guide clinicians in improving diagnostic speed and accuracy, thus leading to improved management decisions and patient outcomes. This literature search aims to review the use of point-of-care ultrasonography in diagnosing necrotizing fasciitis.
METHOD
We searched relevant electronic databases, including PUBMED, MEDLINE, and SCOPUS, and performed a systematic review. Keywords used were "necrotizing fasciitis" or "necrotising fasciitis" or "necrotizing soft tissue infections" and "point-of-care ultrasonography" "ultrasonography" or "ultrasound". No temporal limitation was set. An additional search was performed via google scholar, and the top 100 entry was screened.
RESULTS
Among 540 papers screened, only 21 were related to diagnosing necrotizing fasciitis using ultrasonography. The outcome includes three observational studies, 16 case reports, and two case series, covering the period from 1976 to 2022.
CONCLUSION
Although the use of ultrasonography in diagnosing NF was published in several papers with promising results, more studies are required to investigate its diagnostic accuracy and potential to reduce time delay before surgical intervention, morbidity, and mortality.
Topics: Humans; Point-of-Care Systems; Fasciitis, Necrotizing; Ultrasonography; Necrosis
PubMed: 36694072
DOI: 10.1007/s40477-022-00761-5 -
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 -
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 -
Journal of Alzheimer's Disease : JAD 2023Semantic and Phonological fluency (SF and PF) are routinely evaluated in patients with Alzheimer's disease (AD). There are disagreements in the literature regarding... (Meta-Analysis)
Meta-Analysis
BACKGROUND
Semantic and Phonological fluency (SF and PF) are routinely evaluated in patients with Alzheimer's disease (AD). There are disagreements in the literature regarding which fluency task is more affected while developing AD. Most studies focus on SF assessment, given its connection with the temporoparietal amnesic system. PF is less reported, it is related to working memory, which is also impaired in probable and diagnosed AD. Differentiating between performance on these tasks might be informative in early AD diagnosis, providing an accurate linguistic profile.
OBJECTIVE
Compare SF and PF performance in healthy volunteers, volunteers with probable AD, and patients with AD diagnosis, considering the heterogeneity of age, gender, and educational level variables.
METHODS
A total of 8 studies were included for meta-analysis, reaching a sample size of 1,270 individuals (568 patients diagnosed with AD, 340 with probable AD diagnosis, and 362 healthy volunteers).
RESULTS
The three groups consistently performed better on SF than PF. When progressing to a diagnosis of AD, we observed a significant difference in SF and PF performance across our 3 groups of interest (p = 0.04). The age variable explained a proportion of this difference in task performance across the groups, and as age increases, both tasks equally worsen.
CONCLUSION
The performance of SF and PF might play a differential role in early AD diagnosis. These tasks rely on partially different neural bases of language processing. They are thus worth exploring independently in diagnosing normal aging and its transition to pathological stages, including probable and diagnosed AD.
Topics: Humans; Semantics; Alzheimer Disease; Verbal Behavior; Neuropsychological Tests; Linguistics
PubMed: 37482994
DOI: 10.3233/JAD-221272 -
Cureus Jul 2020Lemierre's syndrome (LS), once known as "the forgotten disease," is a rare and potentially life-threatening condition that has had a gain in incidence over the last 30... (Review)
Review
Lemierre's syndrome (LS), once known as "the forgotten disease," is a rare and potentially life-threatening condition that has had a gain in incidence over the last 30 years due to a variety of factors that could include changes in antibody prescription patterns, particularly in regard to the treatment of pharyngitis/tonsillitis. Due to its low incidence and broad spectrum of symptoms, LS does not have an obvious clinical diagnosis and can confuse the clinician managing the patient. Furthermore, it is equally difficult to treat patients suffering from LS as it requires a multidisciplinary approach from multiple subspecialties. Thus, communication between hospitalists, radiologists, otolaryngologists, neurologists, and ophthalmologists is critical towards quickly diagnosing the disease condition so that prompt antibiotics, anticoagulation, and surgical intervention can occur. Atypical presentations can also exist, making the diagnosis and management exponentially more challenging. Ophthalmologic symptoms are a particularly rare and atypical presentation of LS. These rare symptoms in LS can be terrifying for patients and providers alike; yet, there does not seem to be any modern medical literature that summarizes ophthalmologic complications for LS patients. To our knowledge, this is the first systematic review of LS with a focus on ophthalmologic complications that has been done. The main objective of this review paper is to provide an up-to-date literature review of LS epidemiology, pathophysiology, diagnosis, and treatment while also performing a novel systematic review of reported cases of LS with ophthalmological complications. We hope to bring more awareness towards LS and its atypical presentations so that physicians will be better able to rapidly diagnose and treat their patients in order to minimize long-term morbidity and mortality.
PubMed: 32742884
DOI: 10.7759/cureus.9326 -
Acta Obstetricia Et Gynecologica... Mar 2024Depression and anxiety are significant contributors to maternal perinatal morbidity and a range of negative child outcomes. This systematic review and meta-analysis... (Meta-Analysis)
Meta-Analysis Review
INTRODUCTION
Depression and anxiety are significant contributors to maternal perinatal morbidity and a range of negative child outcomes. This systematic review and meta-analysis aimed to review and assess the diagnostic test accuracy of selected screening tools (Edinburgh Postnatal Depression Scale [EPDS], EPDS-3A, Patient Health Questionnaire [PHQ-9]-, PHQ-2, Matthey Generic Mood Question [MGMQ], Generalized Anxiety Disorder scale [GAD-7], GAD-2, and the Whooley questions) used to identify women with antenatal depression or anxiety in Western countries.
MATERIAL AND METHODS
On January 16, 2023, we searched 10 databases (CINAHL, Cochrane Library, CRD Database, Embase, Epistemonikos, International HTA Database, KSR Evidence, Ovid MEDLINE, PROSPERO and PsycINFO); the references of included studies were also screened. We included studies of any design that compared case-identification with a relevant screening tool to the outcome of a diagnostic interview based on the Diagnostic and Statistical Manual of Mental Disorders, fourth or fifth edition (DSM-IV or DSM-5), or the International Statistical Classification of Diseases and Related Health Problems, 10th revision (ICD-10). Diagnoses of interest were major depressive disorder and anxiety disorders. Two authors independently screened abstracts and full-texts for relevance and evaluated the risk of bias using QUADAS-2. Data extraction was performed by one person and checked by another team member for accuracy. For synthesis, a bivariate model was used. The certainty of evidence was assessed using Grading of Recommendations Assessment, Development and Evaluation (GRADE).
REGISTRATION
PROSPERO CRD42021236333.
RESULTS
We screened 8276 records for eligibility and included 16 original articles reporting on diagnostic test accuracy: 12 for the EPDS, one article each for the GAD-2, MGMQ, PHQ-9, PHQ-2, and Whooley questions, and no articles for the EPDS-3A or GAD-7. Most of the studies had moderate to high risk of bias. Ten of the EPDS articles provided data for synthesis at cutoffs ≥10 to ≥14 for diagnosing major depressive disorder. Cutoff ≥10 gave the optimal combined sensitivity (0.84, 95% confidence interval [CI]: 0.75-0.90) and specificity (0.87, 95% CI: 0.79-0.92).
CONCLUSIONS
Findings from the meta-analysis suggest that the EPDS alone is not perfectly suitable for detection of major depressive disorder during pregnancy. Few studies have evaluated the other instruments, therefore, their usefulness for identification of women with depression and anxiety during pregnancy remains very uncertain. At present, case-identification with any tool may best serve as a complement to a broader dialogue between healthcare professionals and their patients.
Topics: Child; Female; Humans; Pregnancy; Depressive Disorder, Major; Depression; Mass Screening; Anxiety Disorders; Anxiety; Depression, Postpartum
PubMed: 38014572
DOI: 10.1111/aogs.14734 -
Journal of Medical Internet Research Nov 2021Bipolar disorder (BD) is the 10th most common cause of frailty in young individuals and has triggered morbidity and mortality worldwide. Patients with BD have a life... (Review)
Review
BACKGROUND
Bipolar disorder (BD) is the 10th most common cause of frailty in young individuals and has triggered morbidity and mortality worldwide. Patients with BD have a life expectancy 9 to 17 years lower than that of normal people. BD is a predominant mental disorder, but it can be misdiagnosed as depressive disorder, which leads to difficulties in treating affected patients. Approximately 60% of patients with BD are treated for depression. However, machine learning provides advanced skills and techniques for better diagnosis of BD.
OBJECTIVE
This review aims to explore the machine learning algorithms used for the detection and diagnosis of bipolar disorder and its subtypes.
METHODS
The study protocol adopted the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. We explored 3 databases, namely Google Scholar, ScienceDirect, and PubMed. To enhance the search, we performed backward screening of all the references of the included studies. Based on the predefined selection criteria, 2 levels of screening were performed: title and abstract review, and full review of the articles that met the inclusion criteria. Data extraction was performed independently by all investigators. To synthesize the extracted data, a narrative synthesis approach was followed.
RESULTS
We retrieved 573 potential articles were from the 3 databases. After preprocessing and screening, only 33 articles that met our inclusion criteria were identified. The most commonly used data belonged to the clinical category (19, 58%). We identified different machine learning models used in the selected studies, including classification models (18, 55%), regression models (5, 16%), model-based clustering methods (2, 6%), natural language processing (1, 3%), clustering algorithms (1, 3%), and deep learning-based models (3, 9%). Magnetic resonance imaging data were most commonly used for classifying bipolar patients compared to other groups (11, 34%), whereas microarray expression data sets and genomic data were the least commonly used. The maximum ratio of accuracy was 98%, whereas the minimum accuracy range was 64%.
CONCLUSIONS
This scoping review provides an overview of recent studies based on machine learning models used to diagnose patients with BD regardless of their demographics or if they were compared to patients with psychiatric diagnoses. Further research can be conducted to provide clinical decision support in the health industry.
Topics: Algorithms; Bipolar Disorder; Data Management; Humans; Machine Learning; Natural Language Processing
PubMed: 34806996
DOI: 10.2196/29749 -
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 -
Journal of Stroke and Cerebrovascular... Nov 2023Stroke diagnosis is dependent on lengthy clinical and neuroimaging assessments, while rapid treatment initiation improves clinical outcome. Currently, more sensitive... (Meta-Analysis)
Meta-Analysis
BACKGROUND
Stroke diagnosis is dependent on lengthy clinical and neuroimaging assessments, while rapid treatment initiation improves clinical outcome. Currently, more sensitive biomarker assays of both non-coding RNA- and protein biomarkers have improved their detectability, which could accelerate stroke diagnosis. This systematic review and meta-analysis compares non-coding RNA- with protein biomarkers for their potential to diagnose and differentiate acute stroke (subtypes) in (pre-)hospital settings.
METHODS
We performed a systematic review and meta-analysis of studies evaluating diagnostic performance of non-coding RNA- and protein biomarkers to differentiate acute ischemic and hemorrhagic stroke, stroke mimics, and (healthy) controls. Quality appraisal of individual studies was assessed using the QUADAS-2 tool while the meta-analysis was performed with the sROC approach and by assessing pooled sensitivity and specificity, diagnostic odds ratios, positive- and negative likelihood ratios, and the Youden Index.
SUMMARY OF REVIEW
112 studies were included in the systematic review and 42 studies in the meta-analysis containing 11627 patients with ischemic strokes, 2110 patients with hemorrhagic strokes, 1393 patients with a stroke mimic, and 5548 healthy controls. Proteins (IL-6 and S100 calcium-binding protein B (S100B)) and microRNAs (miR-30a) have similar performance in ischemic stroke diagnosis. To differentiate between ischemic- or hemorrhagic strokes, glial fibrillary acidic protein (GFAP) levels and autoantibodies to the NR2 peptide (NR2aAb, a cleavage product of NMDA neuroreceptors) were best performing whereas no investigated protein or non-coding RNA biomarkers differentiated stroke from stroke mimics with high diagnostic potential.
CONCLUSIONS
Despite sampling time differences, circulating microRNAs (< 24 h) and proteins (< 4,5 h) perform equally well in ischemic stroke diagnosis. GFAP differentiates stroke subtypes, while a biomarker panel of GFAP and UCH-L1 improved the sensitivity and specificity of UCH-L1 alone to differentiate stroke.
Topics: Humans; Hemorrhagic Stroke; Stroke; Biomarkers; Ischemic Stroke; Glial Fibrillary Acidic Protein; MicroRNAs; RNA, Untranslated
PubMed: 37778160
DOI: 10.1016/j.jstrokecerebrovasdis.2023.107388