Prediction and Diagnosis of Venous Thromboembolism Using Artificial Intelligence Approaches: A Systematic Review and Meta-Analysis.
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ABSTRACT:
SUBMITTER: Wang Q
PROVIDER: S-EPMC8246532 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The aim was to systematically synthesize the current research and influence of artificial intelligence (AI) models on temporomandibular joint (TMJ) osteoarthritis (OA) diagnosis using cone-beam computed tomography (CBCT) or panoramic radiography. Seven databases (PubMed, Embase, Scopus, Web of Science, LILACS, ProQuest, and SpringerLink) were searched for TMJ OA and AI articles. We used QUADAS-2 to assess the risk of bias, while with MI-CLAIM we checked the minimum information about clinical artificial intelligence modeling. Two hundred and three records were identified, out of which seven were included, amounting to 10,077 TMJ images. Three studies focused on the diagnosis of TMJ OA using panoramic radiography with various transfer learning models (ResNet model) on which the meta-analysis was performed. The pooled sensitivity was 0.76 (95% CI 0.35–0.95) and the specificity was 0.79 (95% CI 0.75–0.83). The other studies investigated the 3D shape of the condyle and disease classification observed on CBCT images, as well as the numerous radiomics features that can be combined with clinical and proteomic data to investigate the most effective models and promising features for the diagnosis of TMJ OA. The accuracy of the methods was nearly equivalent; it was higher when the indeterminate diagnosis was excluded or when fine-tuning was used.
| S-EPMC9918072 | biostudies-literature
Project description:BackgroundArtificial intelligence (AI) has recently been applied to endoscopy and questionnaires for the evaluation of oesophageal diseases (ODs).AimWe performed a systematic review with meta-analysis to evaluate the performance of AI in the diagnosis of malignant and benign OD.MethodsWe searched MEDLINE, EMBASE, EMBASE Classic and the Cochrane Library. A bivariate random-effect model was used to calculate pooled diagnostic efficacy of AI models and endoscopists. The reference tests were histology for neoplasms and the clinical and instrumental diagnosis for gastro-oesophageal reflux disease (GERD). The pooled area under the summary receiver operating characteristic (AUROC), sensitivity, specificity, positive and negative likelihood ratio (PLR and NLR) and diagnostic odds ratio (DOR) were estimated.ResultsFor the diagnosis of Barrett's neoplasia, AI had AUROC of 0.90, sensitivity 0.89, specificity 0.86, PLR 6.50, NLR 0.13 and DOR 50.53. AI models' performance was comparable with that of endoscopists (P = 0.35). For the diagnosis of oesophageal squamous cell carcinoma, the AUROC, sensitivity, specificity, PLR, NLR and DOR were 0.97, 0.95, 0.92, 12.65, 0.05 and DOR 258.36, respectively. In this task, AI performed better than endoscopists although without statistically significant differences. In the detection of abnormal intrapapillary capillary loops, the performance of AI was: AUROC 0.98, sensitivity 0.94, specificity 0.94, PLR 14.75, NLR 0.07 and DOR 225.83. For the diagnosis of GERD based on questionnaires, the AUROC, sensitivity, specificity, PLR, NLR and DOR were 0.99, 0.97, 0.97, 38.26, 0.03 and 1159.6, respectively.ConclusionsAI demonstrated high performance in the clinical and endoscopic diagnosis of OD.
| S-EPMC9305819 | biostudies-literature
Project description:BackgroundArtificial 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.ObjectiveThis 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 methodsThe 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.ResultsA 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.ConclusionsVarious 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.
| S-EPMC9387829 | biostudies-literature
Project description:BackgroundArtificial intelligence (AI) has been extensively used in a range of medical fields to promote therapeutic development. The development of diverse AI techniques has also contributed to early detections, disease diagnoses, and referral management. However, concerns about the value of advanced AI in disease diagnosis have been raised by health care professionals, medical service providers, and health policy decision makers.ObjectiveThis review aimed to systematically examine the literature, in particular, focusing on the performance comparison between advanced AI and human clinicians to provide an up-to-date summary regarding the extent of the application of AI to disease diagnoses. By doing so, this review discussed the relationship between the current advanced AI development and clinicians with respect to disease diagnosis and thus therapeutic development in the long run.MethodsWe systematically searched articles published between January 2000 and March 2019 following the Preferred Reporting Items for Systematic reviews and Meta-Analysis in the following databases: Scopus, PubMed, CINAHL, Web of Science, and the Cochrane Library. According to the preset inclusion and exclusion criteria, only articles comparing the medical performance between advanced AI and human experts were considered.ResultsA total of 9 articles were identified. A convolutional neural network was the commonly applied advanced AI technology. Owing to the variation in medical fields, there is a distinction between individual studies in terms of classification, labeling, training process, dataset size, and algorithm validation of AI. Performance indices reported in articles included diagnostic accuracy, weighted errors, false-positive rate, sensitivity, specificity, and the area under the receiver operating characteristic curve. The results showed that the performance of AI was at par with that of clinicians and exceeded that of clinicians with less experience.ConclusionsCurrent AI development has a diagnostic performance that is comparable with medical experts, especially in image recognition-related fields. Further studies can be extended to other types of medical imaging such as magnetic resonance imaging and other medical practices unrelated to images. With the continued development of AI-assisted technologies, the clinical implications underpinned by clinicians' experience and guided by patient-centered health care principle should be constantly considered in future AI-related and other technology-based medical research.
| S-EPMC6716335 | biostudies-literature
Project description:We aimed to evaluate the performance of the Khorana score in predicting venous thromboembolic events in ambulatory cancer patients. Embase and MEDLINE were searched from January 2008 to June 2018 for studies which evaluated the Khorana score. Two authors independently screened studies for eligibility, extracted data, and assessed risk of bias. Additional data on the 6-month incidence of venous thromboembolism were sought by contacting corresponding authors. The incidence in each Khorana score risk group was estimated with random effects meta-analysis. A total of 45 articles and eight abstracts were included, comprising 55 cohorts enrolling 34,555 ambulatory cancer patients. For 27,849 patients (81%), 6-month follow-up data were obtained. Overall, 19% of patients had a Khorana score of 0 points, 64% a score of 1 or 2 points, and 17% a score of 3 or more points. The incidence of venous thromboembolism in the first six months was 5.0% (95%CI: 3.9-6.5) in patients with a low-risk Khorana score (0 points), 6.6% (95%CI: 5.6-7.7) in those with an intermediate-risk Khorana score (1 or 2 points), and 11.0% (95%CI: 8.8-13.8) in those with a high-risk Khorana score (3 points or higher). Of the patients with venous thromboembolism in the first six months, 23.4% (95%CI: 18.4-29.4) had been classified as high risk according to the Khorana score. In conclusion, the Khorana score can be used to select ambulatory cancer patients at high risk of venous thromboembolism for thromboprophylaxis; however, most events occur outside this high-risk group.
| S-EPMC6545838 | biostudies-literature
Project description:Diabetes mellitus (DM) may be a risk factor for venous thromboembolism (VTE) but results are inconsistent.We conducted a systematic review and meta-analysis of epidemiologic studies to quantify the association between DM and VTE.We included studies identified in PubMed, Web of Science, and CINAHL through 07/31/2014. We identified 19 studies that met our selection criteria. We pooled RRs using a random-effects model: the pooled RR for the association of DM with VTE was 1.10 (95% CI: 0.94-1.29). Between-study heterogeneity was explored with a forest plot, funnel plot, meta-regression, and a stratified analysis. Between-study heterogeneity was observed and not explained by study design, method of DM assessment, or degree of adjustment for confounding. Sensitivity analyses omitted one study at a time to assess the influence of any single study on the pooled estimate. These analyses indicated that one large study was highly influential; when this study was excluded, the pooled estimate increased and just reached statistical significance: 1.16 (95% CI: 1.01-1.34).This meta-analysis suggests either no association or a modest positive one between DM and VTE in the general population. DM is unlikely to play a major role in VTE development.
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Project description:Severe coronavirus disease 2019 (COVID-19) is associated with increased risk of venous thromboembolism events (VTE). This study performed a systematic review in PubMed/EMBASE of studies reporting the prevalence of VTE in patients with COVID-19 who were totally screened/assessed for deep vein thrombosis (DVT) and/or for pulmonary embolism (PE). Among 47 candidate studies (n = 6459; 33 in Europe), 17 studies (n = 3973; weighted age 63.0 years, males 60%, intensive care unit (ICU) 16%) reported the prevalence of PE with a pooled estimate of 32% (95% CI: 25, 40%), and 32 studies (n = 2552; weighted age 62.6 years, males 57%, ICU 49%) reported the prevalence of DVT with a pooled estimate of 27% (95% CI: 21, 34%). A total of 36 studies reported the use of at least prophylactic antithrombotic treatment in the majority of their patients. Meta-regression analysis showed that the prevalence of VTE was higher across studies with a higher percentage of ICU patients and higher study population mean D-dimer values, and lower in studies with mixed dosing of anticoagulation in ⩾ 50% of the population compared to studies with standard prophylactic dosing of anticoagulation in < 50% of the population. The pooled odds ratio for death in patients with COVID-19 and VTE versus those without VTE (17 studies, n = 2882) was 2.1 (95% CI: 1.2, 3.6). Hospitalized patients with severe COVID-19 are at high VTE risk despite prophylactic anticoagulation. Further research should investigate the individualized VTE risk of patients with COVID-19 and the optimal preventive antithrombotic therapy. PROSPERO Registration No.: CRD42020185543.
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Project description:BackgroundThe electrocardiogram (ECG) is an inexpensive and easily accessible investigation for the diagnosis of cardiovascular diseases including heart failure (HF). The application of artificial intelligence (AI) has contributed to clinical practice in terms of aiding diagnosis, prognosis, risk stratification and guiding clinical management. The aim of this study is to systematically review and perform a meta-analysis of published studies on the application of AI for HF detection based on the ECG.MethodsWe searched Embase, PubMed and Web of Science databases to identify literature using AI for HF detection based on ECG data. The quality of included studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) criteria. Random-effects models were used for calculating the effect estimates and hierarchical receiver operating characteristic curves were plotted. Subgroup analysis was performed. Heterogeneity and the risk of bias were also assessed.ResultsA total of 11 studies including 104,737 subjects were included. The area under the curve for HF diagnosis was 0.986, with a corresponding pooled sensitivity of 0.95 (95% CI: 0.86-0.98), specificity of 0.98 (95% CI: 0.95-0.99) and diagnostic odds ratio of 831.51 (95% CI: 127.85-5407.74). In the patient selection domain of QUADAS-2, eight studies were designated as high risk.ConclusionsAccording to the available evidence, the incorporation of AI can aid the diagnosis of HF. However, there is heterogeneity among machine learning algorithms and improvements are required in terms of quality and study design.
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Project description:Lung cancer is a common malignant tumor disease with high clinical disability and death rates. Currently, lung cancer diagnosis mainly relies on manual pathology section analysis, but the low efficiency and subjective nature of manual film reading can lead to certain misdiagnoses and omissions. With the continuous development of science and technology, artificial intelligence (AI) has been gradually applied to imaging diagnosis. Although there are reports on AI-assisted lung cancer diagnosis, there are still problems such as small sample size and untimely data updates. Therefore, in this study, a large amount of recent data was included, and meta-analysis was used to evaluate the value of AI for lung cancer diagnosis. With the help of STATA16.0, the value of AI-assisted lung cancer diagnosis was assessed by specificity, sensitivity, negative likelihood ratio, positive likelihood ratio, diagnostic ratio, and plotting the working characteristic curves of subjects. Meta-regression and subgroup analysis were used to investigate the value of AI-assisted lung cancer diagnosis. The results of the meta-analysis showed that the combined sensitivity of the AI-aided diagnosis system for lung cancer diagnosis was 0.87 [95% CI (0.82, 0.90)], specificity was 0.87 [95% CI (0.82, 0.91)] (CI stands for confidence interval.), the missed diagnosis rate was 13%, the misdiagnosis rate was 13%, the positive likelihood ratio was 6.5 [95% CI (4.6, 9.3)], the negative likelihood ratio was 0.15 [95% CI (0.11, 0.21)], a diagnostic ratio of 43 [95% CI (24, 76)] and a sum of area under the combined subject operating characteristic (SROC) curve of 0.93 [95% CI (0.91, 0.95)]. Based on the results, the AI-assisted diagnostic system for CT (Computerized Tomography), imaging has considerable diagnostic accuracy for lung cancer diagnosis, which is of significant value for lung cancer diagnosis and has greater feasibility of realizing the extension application in the field of clinical diagnosis.
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Project description:Oral cancer (OC) is a deadly disease with a high mortality and complex etiology. Artificial intelligence (AI) is one of the outstanding innovations in technology used in dental science. This paper intends to report on the application and performance of AI in diagnosis and predicting the occurrence of OC. In this study, we carried out data search through an electronic search in several renowned databases, which mainly included PubMed, Google Scholar, Scopus, Embase, Cochrane, Web of Science, and the Saudi Digital Library for articles that were published between January 2000 to March 2021. We included 16 articles that met the eligibility criteria and were critically analyzed using QUADAS-2. AI can precisely analyze an enormous dataset of images (fluorescent, hyperspectral, cytology, CT images, etc.) to diagnose OC. AI can accurately predict the occurrence of OC, as compared to conventional methods, by analyzing predisposing factors like age, gender, tobacco habits, and bio-markers. The precision and accuracy of AI in diagnosis as well as predicting the occurrence are higher than the current, existing clinical strategies, as well as conventional statistics like cox regression analysis and logistic regression.
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