Application of Artificial Intelligence in Screening for Adverse Perinatal Outcomes-A Systematic Review.
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ABSTRACT:
SUBMITTER: Feduniw S
PROVIDER: S-EPMC9690973 | biostudies-literature | 2022 Oct
REPOSITORIES: biostudies-literature
Publications
Application of Artificial Intelligence in Screening for Adverse Perinatal Outcomes-A Systematic Review.
Feduniw Stepan S Golik Dawid D Kajdy Anna A Pruc Michał M Modzelewski Jan J Sys Dorota D Kwiatkowski Sebastian S Makomaska-Szaroszyk Elżbieta E Rabijewski Michał M
Healthcare (Basel, Switzerland) 20221029 11
(1) Background: AI-based solutions could become crucial for the prediction of pregnancy disorders and complications. This study investigated the evidence for applying artificial intelligence methods in obstetric pregnancy risk assessment and adverse pregnancy outcome prediction. (2) Methods: Authors screened the following databases: Pubmed/MEDLINE, Web of Science, Cochrane Library, EMBASE, and Google Scholar. This study included all the evaluative studies comparing artificial intelligence method ...[more]
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Project description:ObjectiveOur objective was to conduct a systematic review of the published literature on housing instability during pregnancy and adverse pregnancy outcomes and perinatal healthcare utilization.Data sourcesWe performed a systematic search in November 2020 using Embase, MEDLINE, Cochrane Library, and Scopus using terms related to housing instability during pregnancy, adverse pregnancy outcomes, and perinatal healthcare utilization. The search was limited to the United States.Study eligibility criteriaStudies examining housing instability (including homelessness) during pregnancy and adverse pregnancy outcomes (including preterm birth, low birthweight neonates, and maternal morbidity) and perinatal healthcare utilization were included.MethodsTwo authors screened abstracts and full-length articles for inclusion. The final cohort consisted of 14 studies. Two authors independently extracted data from each article and assessed the study quality using the Grading of Recommendations, Assessment, Development, and Evaluation tool. Risk of bias was assessed using the National Institutes of Health Study Quality Assessment Tools.ResultsAll included studies were observational, including retrospective cohort (n=10, 71.4%), cross-sectional observational (n=3, 21.4%), or prospective cohort studies (n=1, 7.1%). There was significant heterogeneity in the definitions of housing instability and homelessness. Most of the studies only examined homelessness (n=9, 64.3%) and not lesser degrees of housing instability. Housing instability and homelessness during pregnancy were significantly associated with preterm birth, low birthweight neonates, neonatal intensive care unit admission, and delivery complications. Among studies examining perinatal healthcare utilization, housing instability was associated with inadequate prenatal care and increased hospital utilization. All studies exhibited moderate, low, or very low study quality and fair or poor internal validity.ConclusionAlthough data on housing instability during pregnancy are limited by the lack of a standardized definition, a consistent relationship between housing instability and adverse pregnancy outcomes has been suggested by this systematic review. The evaluation and development of a standardized definition and measurement of housing instability among pregnant individuals is warranted to address future interventions targeted to housing instability during pregnancy.
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Project description:PurposeApplication of artificial intelligence (AI) in medicine is quickly expanding. Despite the amount of evidence and promising results, a thorough overview of the current state of AI in clinical practice of anesthesiology is needed. Therefore, our study aims to systematically review the application of AI in this context.MethodsA systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We searched Medline and Web of Science for articles published up to November 2022 using terms related with AI and clinical practice of anesthesiology. Articles that involved animals, editorials, reviews and sample size lower than 10 patients were excluded. Characteristics and accuracy measures from each study were extracted.ResultsA total of 46 articles were included in this review. We have grouped them into 4 categories with regard to their clinical applicability: (1) Depth of Anesthesia Monitoring; (2) Image-guided techniques related to Anesthesia; (3) Prediction of events/risks related to Anesthesia; (4) Drug administration control. Each group was analyzed, and the main findings were summarized. Across all fields, the majority of AI methods tested showed superior performance results compared to traditional methods.ConclusionAI systems are being integrated into anesthesiology clinical practice, enhancing medical professionals' skills of decision-making, diagnostic accuracy, and therapeutic response.
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Project description:BackgroundCoronavirus disease 2019 (COVID-19) has caused a large-scale global epidemic, impacting international politics and the economy. At present, there is no particularly effective medicine and treatment plan. Therefore, it is urgent and significant to find new technologies to diagnose early, isolate early, and treat early. Multimodal data drove artificial intelligence (AI) can potentially be the option. During the COVID-19 Pandemic, AI provided cutting-edge applications in disease, medicine, treatment, and target recognition. This paper reviewed the literature on the intersection of AI and medicine to analyze and compare different AI model applications in the COVID-19 Pandemic, evaluate their effectiveness, show their advantages and differences, and introduce the main models and their characteristics.MethodsWe searched PubMed, arXiv, medRxiv, and Google Scholar through February 2020 to identify studies on AI applications in the medical areas for the COVID-19 Pandemic.ResultsWe summarize the main AI applications in six areas: (I) epidemiology, (II) diagnosis, (III) progression, (IV) treatment, (V) psychological health impact, and (VI) data security. The ongoing development in AI has significantly improved prediction, contact tracing, screening, diagnosis, treatment, medication, and vaccine development for the COVID-19 Pandemic and reducing human intervention in medical practice.DiscussionThis paper provides strong advice for using AI-based auxiliary tools for related applications of human diseases. We also discuss the clinicians' role in the further development of AI. They and AI researchers can integrate AI technology with current clinical processes and information systems into applications. In the future, AI personnel and medical workers will further cooperate closely.
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Project description:BackgroundArtificial intelligence (AI) provides opportunities to identify the health risks of patients and thus influence patient safety outcomes.ObjectiveThe purpose of this systematic literature review was to identify and analyze quantitative studies utilizing or integrating AI to address and report clinical-level patient safety outcomes.MethodsWe restricted our search to the PubMed, PubMed Central, and Web of Science databases to retrieve research articles published in English between January 2009 and August 2019. We focused on quantitative studies that reported positive, negative, or intermediate changes in patient safety outcomes using AI apps, specifically those based on machine-learning algorithms and natural language processing. Quantitative studies reporting only AI performance but not its influence on patient safety outcomes were excluded from further review.ResultsWe identified 53 eligible studies, which were summarized concerning their patient safety subcategories, the most frequently used AI, and reported performance metrics. Recognized safety subcategories were clinical alarms (n=9; mainly based on decision tree models), clinical reports (n=21; based on support vector machine models), and drug safety (n=23; mainly based on decision tree models). Analysis of these 53 studies also identified two essential findings: (1) the lack of a standardized benchmark and (2) heterogeneity in AI reporting.ConclusionsThis systematic review indicates that AI-enabled decision support systems, when implemented correctly, can aid in enhancing patient safety by improving error detection, patient stratification, and drug management. Future work is still needed for robust validation of these systems in prospective and real-world clinical environments to understand how well AI can predict safety outcomes in health care settings.
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Project description:Objectives To assess the association between maternal glucose concentrations and adverse perinatal outcomes in women without gestational or existing diabetes and to determine whether clear thresholds for identifying women at risk of perinatal outcomes can be identified.Design Systematic review and meta-analysis of prospective cohort studies and control arms of randomised trials.Data sources Databases including Medline and Embase were searched up to October 2014 and combined with individual participant data from two additional birth cohorts.Eligibility criteria for selecting studies Studies including pregnant women with oral glucose tolerance (OGTT) or challenge (OGCT) test results, with data on at least one adverse perinatal outcome.Appraisal and data extraction Glucose test results were extracted for OGCT (50 g) and OGTT (75 g and 100 g) at fasting and one and two hour post-load timings. Data were extracted on induction of labour; caesarean and instrumental delivery; pregnancy induced hypertension; pre-eclampsia; macrosomia; large for gestational age; preterm birth; birth injury; and neonatal hypoglycaemia. Risk of bias was assessed with a modified version of the critical appraisal skills programme and quality in prognostic studies tools.Results 25 reports from 23 published studies and two individual participant data cohorts were included, with up to 207 172 women (numbers varied by the test and outcome analysed in the meta-analyses). Overall most studies were judged as having a low risk of bias. There were positive linear associations with caesarean section, induction of labour, large for gestational age, macrosomia, and shoulder dystocia for all glucose exposures across the distribution of glucose concentrations. There was no clear evidence of a threshold effect. In general, associations were stronger for fasting concentration than for post-load concentration. For example, the odds ratios for large for gestational age per 1 mmol/L increase of fasting and two hour post-load glucose concentrations (after a 75 g OGTT) were 2.15 (95% confidence interval 1.60 to 2.91) and 1.20 (1.13 to 1.28), respectively. Heterogeneity was low between studies in all analyses.Conclusions This review and meta-analysis identified a large number of studies in various countries. There was a graded linear association between fasting and post-load glucose concentration across the whole glucose distribution and most adverse perinatal outcomes in women without pre-existing or gestational diabetes. The lack of a clear threshold at which risk increases means that decisions regarding thresholds for diagnosing gestational diabetes are somewhat arbitrary. Research should now investigate the clinical and cost-effectiveness of applying different glucose thresholds for diagnosis of gestational diabetes on perinatal and longer term outcomes.Systematic review registration PROSPERO CRD42013004608.
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Project description:Screening for left ventricular systolic dysfunction (LVSD), defined as reduced left ventricular ejection fraction (LVEF), deserves renewed interest as the medical treatment for the prevention and progression of heart failure improves. We aimed to review the updated literature to outline the potential and caveats of using artificial intelligence-enabled electrocardiography (AIeECG) as an opportunistic screening tool for LVSD.We searched PubMed and Cochrane for variations of the terms "ECG," "Heart Failure," "systolic dysfunction," and "Artificial Intelligence" from January 2010 to April 2022 and selected studies that reported the diagnostic accuracy and confounders of using AIeECG to detect LVSD.Out of 40 articles, we identified 15 relevant studies; eleven retrospective cohorts, three prospective cohorts, and one case series. Although various LVEF thresholds were used, AIeECG detected LVSD with a median AUC of 0.90 (IQR from 0.85 to 0.95), a sensitivity of 83.3% (IQR from 73 to 86.9%) and a specificity of 87% (IQR from 84.5 to 90.9%). AIeECG algorithms succeeded across a wide range of sex, age, and comorbidity and seemed especially useful in non-cardiology settings and when combined with natriuretic peptide testing. Furthermore, a false-positive AIeECG indicated a future development of LVSD. No studies investigated the effect on treatment or patient outcomes.This systematic review corroborates the arrival of a new generic biomarker, AIeECG, to improve the detection of LVSD. AIeECG, in addition to natriuretic peptides and echocardiograms, will improve screening for LVSD, but prospective randomized implementation trials with added therapy are needed to show cost-effectiveness and clinical significance.
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Project description:ObjectiveWe aim to investigate the application and accuracy of artificial intelligence (AI) methods for automated medical literature screening for systematic reviews.Materials and methodsWe systematically searched PubMed, Embase, and IEEE Xplore Digital Library to identify potentially relevant studies. We included studies in automated literature screening that reported study question, source of dataset, and developed algorithm models for literature screening. The literature screening results by human investigators were considered to be the reference standard. Quantitative synthesis of the accuracy was conducted using a bivariate model.ResultsEighty-six studies were included in our systematic review and 17 studies were further included for meta-analysis. The combined recall, specificity, and precision were 0.928 [95% confidence interval (CI), 0.878-0.958], 0.647 (95% CI, 0.442-0.809), and 0.200 (95% CI, 0.135-0.287) when achieving maximized recall, but were 0.708 (95% CI, 0.570-0.816), 0.921 (95% CI, 0.824-0.967), and 0.461 (95% CI, 0.375-0.549) when achieving maximized precision in the AI models. No significant difference was found in recall among subgroup analyses including the algorithms, the number of screened literatures, and the fraction of included literatures.Discussion and conclusionThis systematic review and meta-analysis study showed that the recall is more important than the specificity or precision in literature screening, and a recall over 0.95 should be prioritized. We recommend to report the effectiveness indices of automatic algorithms separately. At the current stage manual literature screening is still indispensable for medical systematic reviews.
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