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Sleep Medicine Reviews Jun 2023This meta-analysis aimed to assess the effectiveness and safety of (adeno)tonsillectomy (AT) for uncomplicated pediatric obstructive sleep apnea (OSA) across different... (Meta-Analysis)
Meta-Analysis Review
This meta-analysis aimed to assess the effectiveness and safety of (adeno)tonsillectomy (AT) for uncomplicated pediatric obstructive sleep apnea (OSA) across different age groups. Four electronic databases were searched until April 2022, and 93 studies (9087 participants) were selected, including before-after studies, cohort studies, and randomized controlled trials. It has been suggested that age, disease severity, and length of follow-up are associated with surgical effects. Compared with older children (>7 years), patients receiving AT surgery before the age of 7 exhibited a significantly greater release of disease severity, as well as a greater decrease in hypoxemic burden, improvement in sleep quality, and better cardiovascular function. Cognitive/behavioral performance also improved after AT, although it was more related to the length of follow-up than the age at surgery. Notably, the surgical complication rate was considerably higher in patients younger than 3 years old. Overall, we suggest that the age of 3-7 years might be optimal for AT in polysomnography-diagnosed uncomplicated OSA to maximize potential benefits for both disease and comorbidities and balance the risks of surgery.
Topics: Child; Humans; Adolescent; Child, Preschool; Tonsillectomy; Sleep Apnea, Obstructive; Polysomnography; Adenoidectomy
PubMed: 37121134
DOI: 10.1016/j.smrv.2023.101782 -
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 Medical Internet Research Jul 2023Tuberculosis (TB) was the leading infectious cause of mortality globally prior to COVID-19 and chest radiography has an important role in the detection, and subsequent... (Meta-Analysis)
Meta-Analysis
BACKGROUND
Tuberculosis (TB) was the leading infectious cause of mortality globally prior to COVID-19 and chest radiography has an important role in the detection, and subsequent diagnosis, of patients with this disease. The conventional experts reading has substantial within- and between-observer variability, indicating poor reliability of human readers. Substantial efforts have been made in utilizing various artificial intelligence-based algorithms to address the limitations of human reading of chest radiographs for diagnosing TB.
OBJECTIVE
This systematic literature review (SLR) aims to assess the performance of machine learning (ML) and deep learning (DL) in the detection of TB using chest radiography (chest x-ray [CXR]).
METHODS
In conducting and reporting the SLR, we followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. A total of 309 records were identified from Scopus, PubMed, and IEEE (Institute of Electrical and Electronics Engineers) databases. We independently screened, reviewed, and assessed all available records and included 47 studies that met the inclusion criteria in this SLR. We also performed the risk of bias assessment using Quality Assessment of Diagnostic Accuracy Studies version 2 (QUADAS-2) and meta-analysis of 10 included studies that provided confusion matrix results.
RESULTS
Various CXR data sets have been used in the included studies, with 2 of the most popular ones being Montgomery County (n=29) and Shenzhen (n=36) data sets. DL (n=34) was more commonly used than ML (n=7) in the included studies. Most studies used human radiologist's report as the reference standard. Support vector machine (n=5), k-nearest neighbors (n=3), and random forest (n=2) were the most popular ML approaches. Meanwhile, convolutional neural networks were the most commonly used DL techniques, with the 4 most popular applications being ResNet-50 (n=11), VGG-16 (n=8), VGG-19 (n=7), and AlexNet (n=6). Four performance metrics were popularly used, namely, accuracy (n=35), area under the curve (AUC; n=34), sensitivity (n=27), and specificity (n=23). In terms of the performance results, ML showed higher accuracy (mean ~93.71%) and sensitivity (mean ~92.55%), while on average DL models achieved better AUC (mean ~92.12%) and specificity (mean ~91.54%). Based on data from 10 studies that provided confusion matrix results, we estimated the pooled sensitivity and specificity of ML and DL methods to be 0.9857 (95% CI 0.9477-1.00) and 0.9805 (95% CI 0.9255-1.00), respectively. From the risk of bias assessment, 17 studies were regarded as having unclear risks for the reference standard aspect and 6 studies were regarded as having unclear risks for the flow and timing aspect. Only 2 included studies had built applications based on the proposed solutions.
CONCLUSIONS
Findings from this SLR confirm the high potential of both ML and DL for TB detection using CXR. Future studies need to pay a close attention on 2 aspects of risk of bias, namely, the reference standard and the flow and timing aspects.
TRIAL REGISTRATION
PROSPERO CRD42021277155; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=277155.
Topics: Humans; Artificial Intelligence; COVID-19; Deep Learning; Radiography; Reproducibility of Results; Tuberculosis; X-Rays
PubMed: 37399055
DOI: 10.2196/43154 -
Chest Jul 2023Monocyte distribution width (MDW) is an emerging biomarker for infection. It is available easily and quickly as part of the CBC count, which is performed routinely on... (Meta-Analysis)
Meta-Analysis
BACKGROUND
Monocyte distribution width (MDW) is an emerging biomarker for infection. It is available easily and quickly as part of the CBC count, which is performed routinely on hospital admission. The increasing availability and promising results of MDW as a biomarker in sepsis has prompted an expansion of its use to other infectious diseases.
RESEARCH QUESTION
What is the diagnostic performance of MDW across multiple infectious disease outcomes and care settings?
STUDY DESIGN AND METHODS
A systematic review of the diagnostic performance of MDW across multiple infectious disease outcomes was conducted by searching PubMed, Embase, Scopus, and Web of Science through February 4, 2022. Meta-analysis was performed for outcomes with three or more reports identified (sepsis and COVID-19). Diagnostic performance measures were calculated for individual studies with pooled estimates created by linear mixed-effects models.
RESULTS
We identified 29 studies meeting inclusion criteria. Most examined sepsis (19 studies) and COVID-19 (six studies). Pooled estimates of diagnostic performance for sepsis differed by reference standard (Second vs Third International Consensus Definitions for Sepsis and Septic Shock criteria) and tube anticoagulant used and ranged from an area under the receiver operating characteristic curve (AUC) of 0.74 to 0.94, with mean sensitivity of 0.69 to 0.79 and mean specificity of 0.57 to 0.86. For COVID-19, the pooled AUC of MDW was 0.76, mean sensitivity was 0.79, and mean specificity was 0.59.
INTERPRETATION
MDW exhibited good diagnostic performance for sepsis and COVID-19. Diagnostic thresholds for sepsis should be chosen with consideration of reference standard and tube type used.
TRIAL REGISTRY
Prospero; No.: CRD42020210074; URL: https://www.crd.york.ac.uk/prospero/.
Topics: Humans; Monocytes; COVID-19; Sepsis; Biomarkers; Communicable Diseases; COVID-19 Testing
PubMed: 36681146
DOI: 10.1016/j.chest.2022.12.049 -
Parasite (Paris, France) 2023Serological methods should meet the needs of leishmaniasis diagnosis due to their high sensitivity and specificity, economical and adaptable rapid diagnostic test...
Serological methods should meet the needs of leishmaniasis diagnosis due to their high sensitivity and specificity, economical and adaptable rapid diagnostic test format, and ease of use. Currently, the performances of serological diagnostic tests, despite improvements with recombinant proteins, vary greatly depending on the clinical form of leishmaniasis and the endemic area. Peptide-based serological tests are promising as they could compensate for antigenic variability and improve performance, independently of Leishmania species and subspecies circulating in the endemic areas. The objective of this systematic review was to inventory all studies published from 2002 to 2022 that evaluate synthetic peptides for serological diagnosis of human leishmaniases and also to highlight the performance (e.g., sensitivity and specificity) of each peptide reported in these studies. All clinical forms of leishmaniasis, visceral and tegumentary, and all Leishmania species responsible for these diseases were considered. Following PRISMA statement recommendations, 1,405 studies were identified but only 22 articles met the selection criteria and were included in this systematic review. These original research articles described 77 different peptides, of which several have promising performance for visceral or tegumentary leishmaniasis diagnosis. This review highlights the importance of and growing interest in synthetic peptides used for serological diagnosis of leishmaniases, and their performances compared to some widely used tests with recombinant proteins.
Topics: Humans; Animals; Dogs; Leishmaniasis, Visceral; Leishmania; Serologic Tests; Leishmaniasis; Peptides; Sensitivity and Specificity; Leishmaniasis, Cutaneous; Recombinant Proteins; Antigens, Protozoan; Enzyme-Linked Immunosorbent Assay; Dog Diseases
PubMed: 37010451
DOI: 10.1051/parasite/2023011 -
Computational Intelligence and... 2022Epileptic seizure is one of the most chronic neurological diseases that instantaneously disrupts the lifestyle of affected individuals. Toward developing novel and... (Review)
Review
Epileptic seizure is one of the most chronic neurological diseases that instantaneously disrupts the lifestyle of affected individuals. Toward developing novel and efficient technology for epileptic seizure management, recent diagnostic approaches have focused on developing machine/deep learning model (ML/DL)-based electroencephalogram (EEG) methods. Importantly, EEG's noninvasiveness and ability to offer repeated patterns of epileptic-related electrophysiological information have motivated the development of varied ML/DL algorithms for epileptic seizure diagnosis in the recent years. However, EEG's low amplitude and nonstationary characteristics make it difficult for existing ML/DL models to achieve a consistent and satisfactory diagnosis outcome, especially in clinical settings, where environmental factors could hardly be avoided. Though several recent works have explored the use of EEG-based ML/DL methods and statistical feature for seizure diagnosis, it is unclear what the advantages and limitations of these works are, which might preclude the advancement of research and development in the field of epileptic seizure diagnosis and appropriate criteria for selecting ML/DL models and statistical feature extraction methods for EEG-based epileptic seizure diagnosis. Therefore, this paper attempts to bridge this research gap by conducting an extensive systematic review on the recent developments of EEG-based ML/DL technologies for epileptic seizure diagnosis. In the review, current development in seizure diagnosis, various statistical feature extraction methods, ML/DL models, their performances, limitations, and core challenges as applied in EEG-based epileptic seizure diagnosis were meticulously reviewed and compared. In addition, proper criteria for selecting appropriate and efficient feature extraction techniques and ML/DL models for epileptic seizure diagnosis were also discussed. Findings from this study will aid researchers in deciding the most efficient ML/DL models with optimal feature extraction methods to improve the performance of EEG-based epileptic seizure detection.
Topics: Algorithms; Deep Learning; Electroencephalography; Epilepsy; Humans; Seizures; Signal Processing, Computer-Assisted; Support Vector Machine
PubMed: 35755757
DOI: 10.1155/2022/6486570 -
Hand (New York, N.Y.) May 2023The scratch-collapse test (SCT) is a provocative maneuver used to diagnose compressive neuropathies. Despite multiple studies supporting its use, the SCT remains a...
BACKGROUND
The scratch-collapse test (SCT) is a provocative maneuver used to diagnose compressive neuropathies. Despite multiple studies supporting its use, the SCT remains a controversial point in the literature in regard to its exact clinical application. We performed a systematic review and statistical analysis to provide statistical data on SCT outcomes and elucidate its role in diagnosing compressive conditions.
METHODS
We performed a systematic review of the literature according to Preferred Reporting for Systematic Reviews and Meta-Analyses guidelines. We extracted data of patients with outcomes on the SCT (yes/no) and on an accepted gold standard examination (electrodiagnostic studies). These data were analyzed using a statistical software program to generate the sensitivity and specificity values of the pooled data, as well as kappa agreement statistics.
RESULTS
For patients with carpal tunnel, cubital tunnel, peroneal, and pronator compressive neuropathies, the overall sensitivity of the SCT was 38%, and the specificity was 94%, with the kappa statistic approximately 0.4. Sensitivity and specificity values were higher for cubital tunnel syndrome and peroneal compression syndrome but lower for carpal tunnel syndrome. Pronator syndrome was also examined, but the data were inadequate for analysis.
CONCLUSIONS
The SCT is a useful adjunct in the armament of diagnostic tools for the hand surgeon. Given its low sensitivity and high specificity, SCT should be used as a confirmatory test, rather than as a diagnostic screening test. More analyses are needed to identify subtler applications.
PubMed: 37222286
DOI: 10.1177/15589447231174483 -
Journal of Nephrology May 2023In this systematic review we aimed at assessing how artificial intelligence (AI), including machine learning (ML) techniques have been deployed to predict, diagnose, and... (Review)
Review
OBJECTIVES
In this systematic review we aimed at assessing how artificial intelligence (AI), including machine learning (ML) techniques have been deployed to predict, diagnose, and treat chronic kidney disease (CKD). We systematically reviewed the available evidence on these innovative techniques to improve CKD diagnosis and patient management.
METHODS
We included English language studies retrieved from PubMed. The review is therefore to be classified as a "rapid review", since it includes one database only, and has language restrictions; the novelty and importance of the issue make missing relevant papers unlikely. We extracted 16 variables, including: main aim, studied population, data source, sample size, problem type (regression, classification), predictors used, and performance metrics. We followed the Preferred Reporting Items for Systematic Reviews (PRISMA) approach; all main steps were done in duplicate.
RESULTS
From a total of 648 studies initially retrieved, 68 articles met the inclusion criteria. Models, as reported by authors, performed well, but the reported metrics were not homogeneous across articles and therefore direct comparison was not feasible. The most common aim was prediction of prognosis, followed by diagnosis of CKD. Algorithm generalizability, and testing on diverse populations was rarely taken into account. Furthermore, the clinical evaluation and validation of the models/algorithms was perused; only a fraction of the included studies, 6 out of 68, were performed in a clinical context.
CONCLUSIONS
Machine learning is a promising tool for the prediction of risk, diagnosis, and therapy management for CKD patients. Nonetheless, future work is needed to address the interpretability, generalizability, and fairness of the models to ensure the safe application of such technologies in routine clinical practice.
Topics: Humans; Renal Insufficiency, Chronic; Artificial Intelligence; Machine Learning
PubMed: 36786976
DOI: 10.1007/s40620-023-01573-4 -
Journal of Ultrasound Jun 2023The goal of this study was to perform a comprehensive meta-analysis to assess the overall diagnostic value of Doppler twinkling for the diagnosis of urolithiasis. (Meta-Analysis)
Meta-Analysis
OBJECTIVE
The goal of this study was to perform a comprehensive meta-analysis to assess the overall diagnostic value of Doppler twinkling for the diagnosis of urolithiasis.
METHODS
We systematically searched the PubMed, EMBASE, and Cochrane Library databases from inception through May 31, 2021. Studies including patients with urolithiasis who underwent color flow Doppler sampling to highlight the twinkling artifact and computed tomography were included. Diagnostic test meta-analysis was performed with a bivariate model. We used summary receiver operating characteristic curves to summarize the overall diagnostic performance. The weighted sensitivity, specificity, positive likelihood ratio, and negative likelihood ratio were calculated.
RESULTS
Sixteen studies involving 4572 patients were included in the systematic review and meta-analysis. The weighted sensitivity was 0.86 (95% confidence interval [CI] 0.72-0.94), specificity 0.92 (95% CI 0.75-0.98), positive likelihood ratio 11.3, negative likelihood ratio 0.2, and diagnostic odds ratio 75.5.
CONCLUSION
The Doppler twinkling artifact has good diagnostic value for the diagnosis of urolithiasis and should be used as a complementary tool in the diagnosis of urolithiasis.
Topics: Humans; Artifacts; Sensitivity and Specificity; Urolithiasis; Ultrasonography, Doppler; ROC Curve
PubMed: 36705851
DOI: 10.1007/s40477-022-00759-z -
Journal of Clinical Medicine May 2023Recent years have seen the emergence and application of artificial intelligence (AI) in diagnostic decision support systems. There are approximately 80 etiologies that... (Review)
Review
Recent years have seen the emergence and application of artificial intelligence (AI) in diagnostic decision support systems. There are approximately 80 etiologies that can underly uveitis, some very rare, and AI may lend itself to their detection. This synthesis of the literature selected articles that focused on the use of AI in determining the diagnosis, classification, and underlying etiology of uveitis. The AI-based systems demonstrated relatively good performance, with a classification accuracy of 93-99% and a sensitivity of at least 80% for identifying the two most probable etiologies underlying uveitis. However, there were limitations to the evidence. Firstly, most data were collected retrospectively with missing data. Secondly, ophthalmic, demographic, clinical, and ancillary tests were not reliably integrated into the algorithms' dataset. Thirdly, patient numbers were small, which is problematic when aiming to discriminate rare and complex diagnoses. In conclusion, the data indicate that AI has potential as a diagnostic decision support system, but clinical applicability is not yet established. Future studies and technologies need to incorporate more comprehensive clinical data and larger patient populations. In time, these should improve AI-based diagnostic tools and help clinicians diagnose, classify, and manage patients with uveitis.
PubMed: 37297939
DOI: 10.3390/jcm12113746