scholarly journals Artificial Intelligence for Detection of Cardiovascular-Related Diseases from Wearable Devices: A Systematic Review and Meta-Analysis

2022 ◽  
Vol 63 (Suppl) ◽  
pp. S93
Author(s):  
Solam Lee ◽  
Yuseong Chu ◽  
Jiseung Ryu ◽  
Young Jun Park ◽  
Sejung Yang ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Systematic Review ◽  
Meta Analysis ◽  
Wearable Devices

scholarly journals Systematic review protocol to assess artificial intelligence diagnostic accuracy performance in detecting acute ischaemic stroke and large-vessel occlusions on CT and MR medical imaging

BMJ Open ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. e043665
Author(s):  
Srinivasa Rao Kundeti ◽  
Manikanda Krishnan Vaidyanathan ◽  
Bharath Shivashankar ◽  
Sankar Prasad Gorthi
Keyword(s):  
Artificial Intelligence ◽  
Systematic Review ◽  
Diagnostic Accuracy ◽  
Ischaemic Stroke ◽  
Study Protocol ◽  
Systematic Reviews ◽  
Acute Ischaemic Stroke ◽  
Meta Analysis ◽  
Large Vessel ◽  
Sufficient Data

IntroductionThe use of artificial intelligence (AI) to support the diagnosis of acute ischaemic stroke (AIS) could improve patient outcomes and facilitate accurate tissue and vessel assessment. However, the evidence in published AI studies is inadequate and difficult to interpret which reduces the accountability of the diagnostic results in clinical settings. This study protocol describes a rigorous systematic review of the accuracy of AI in the diagnosis of AIS and detection of large-vessel occlusions (LVOs).Methods and analysisWe will perform a systematic review and meta-analysis of the performance of AI models for diagnosing AIS and detecting LVOs. We will adhere to the Preferred Reporting Items for Systematic Reviews and Meta-analyses Protocols guidelines. Literature searches will be conducted in eight databases. For data screening and extraction, two reviewers will use a modified Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies checklist. We will assess the included studies using the Quality Assessment of Diagnostic Accuracy Studies guidelines. We will conduct a meta-analysis if sufficient data are available. We will use hierarchical summary receiver operating characteristic curves to estimate the summary operating points, including the pooled sensitivity and specificity, with 95% CIs, if pooling is appropriate. Furthermore, if sufficient data are available, we will use Grading of Recommendations, Assessment, Development and Evaluations profiler software to summarise the main findings of the systematic review, as a summary of results.Ethics and disseminationThere are no ethical considerations associated with this study protocol, as the systematic review focuses on the examination of secondary data. The systematic review results will be used to report on the accuracy, completeness and standard procedures of the included studies. We will disseminate our findings by publishing our analysis in a peer-reviewed journal and, if required, we will communicate with the stakeholders of the studies and bibliographic databases.PROSPERO registration numberCRD42020179652.

scholarly journals Assessing the Performance of Artificial Intelligence Systems for the Screening of Diabetic Retinopathy: A Systematic Review and Meta-Analysis

2021 ◽  
Author(s):  
Ryan Sadjadi
Keyword(s):  
Artificial Intelligence ◽  
Systematic Review ◽  
Diabetic Retinopathy ◽  
Vision Loss ◽  
Meta Analysis ◽  
External Validation ◽  
Healthcare Providers ◽  
Vitreoretinal Surgery ◽  
Retinal Fundus ◽  
Screening Systems

Diabetic retinopathy is the most common microvascular complication of diabetes mellitus and one of the leading causes of blindness globally. Due to the progressive nature of the disease, earlier detection and timely treatment can lead to substantial reductions in the incidence of irreversible vision-loss. Artificial intelligence (AI) screening systems have offered clinically acceptable and quicker results in detecting diabetic retinopathy from retinal fundus and optical coherence tomography (OCT) images. Thus, this systematic review and meta-analysis of relevant investigations was performed to document the performance of AI screening systems that were applied to fundus and OCT images of patients from diverse geographic locations including North America, Europe, Africa, Asia, and Australia. A systematic literature search on Medline, Global Health, and PubMed was performed and studies published between October 2015 and January 2020 were included. The search strategy was based on the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guidelines, and AI-based investigations were mandatory for studies inclusion. The abstracts, titles, and full-texts of potentially eligible studies were screened against inclusion and exclusion criteria. Twenty-one studies were included in this systematic review; 18 met inclusion criteria for the meta-analysis. The pooled sensitivity of the evaluated AI screening systems in detecting diabetic retinopathy was 0.93 (95% CI: 0.92-0.94) and the specificity was 0.88 (95% CI: 0.86-0.89). The included studies detailed training and external validation datasets, criteria for diabetic retinopathy case ascertainment, imaging modalities, DR-grading scales, and compared AI results to those of human graders (e.g., ophthalmologists, retinal specialists, trained nurses, and other healthcare providers) as a reference standard. The findings of this study showed that the majority AI screening systems demonstrated clinically acceptable levels of sensitivity and specificity for detecting referable diabetic retinopathy from retinal fundus and OCT photographs. Further improvement depends on the continual development of novel algorithms with large and gradable sets of images for training and validation. If cost-effectiveness ratios can be optimized, AI can become a financially sustainable and clinically effective intervention that can be incorporated into the healthcare systems of low-to-middle income countries (LMICs) and geographically remote locations. Combining screening technologies with treatment interventions such as anti-VEGF therapy, acellular capillary laser treatment, and vitreoretinal surgery can lead to substantial reductions in the incidence of irreversible vision-loss due to proliferative diabetic retinopathy.

scholarly journals Artificial intelligence for pre-operative lymph node staging in colorectal cancer: a systematic review and meta-analysis

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Sergei Bedrikovetski ◽  
Nagendra N. Dudi-Venkata ◽  
Hidde M. Kroon ◽  
Warren Seow ◽  
Ryash Vather ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Colorectal Cancer ◽  
Systematic Review ◽  
Deep Learning ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Meta Analysis ◽  
Cochrane Library ◽  
Learning Models ◽  
Node Metastasis

Abstract Background Artificial intelligence (AI) is increasingly being used in medical imaging analysis. We aimed to evaluate the diagnostic accuracy of AI models used for detection of lymph node metastasis on pre-operative staging imaging for colorectal cancer. Methods A systematic review was conducted according to PRISMA guidelines using a literature search of PubMed (MEDLINE), EMBASE, IEEE Xplore and the Cochrane Library for studies published from January 2010 to October 2020. Studies reporting on the accuracy of radiomics models and/or deep learning for the detection of lymph node metastasis in colorectal cancer by CT/MRI were included. Conference abstracts and studies reporting accuracy of image segmentation rather than nodal classification were excluded. The quality of the studies was assessed using a modified questionnaire of the QUADAS-2 criteria. Characteristics and diagnostic measures from each study were extracted. Pooling of area under the receiver operating characteristic curve (AUROC) was calculated in a meta-analysis. Results Seventeen eligible studies were identified for inclusion in the systematic review, of which 12 used radiomics models and five used deep learning models. High risk of bias was found in two studies and there was significant heterogeneity among radiomics papers (73.0%). In rectal cancer, there was a per-patient AUROC of 0.808 (0.739–0.876) and 0.917 (0.882–0.952) for radiomics and deep learning models, respectively. Both models performed better than the radiologists who had an AUROC of 0.688 (0.603 to 0.772). Similarly in colorectal cancer, radiomics models with a per-patient AUROC of 0.727 (0.633–0.821) outperformed the radiologist who had an AUROC of 0.676 (0.627–0.725). Conclusion AI models have the potential to predict lymph node metastasis more accurately in rectal and colorectal cancer, however, radiomics studies are heterogeneous and deep learning studies are scarce. Trial registration PROSPERO CRD42020218004.

scholarly journals Artificial Intelligence Education and Tools for Medical and Health Informatics Students: Systematic Review (Preprint)

2020 ◽  
Author(s):  
A Hasan Sapci ◽  
H Aylin Sapci
Keyword(s):  
Artificial Intelligence ◽  
Systematic Review ◽  
Health Informatics ◽  
Data Analytics ◽  
Learning Experience ◽  
Meta Analysis ◽  
Improve Patient Care ◽  
Time Data ◽  
Current State ◽  
Intelligence Education

BACKGROUND The use of artificial intelligence (AI) in medicine will generate numerous application possibilities to improve patient care, provide real-time data analytics, and enable continuous patient monitoring. Clinicians and health informaticians should become familiar with machine learning and deep learning. Additionally, they should have a strong background in data analytics and data visualization to use, evaluate, and develop AI applications in clinical practice. OBJECTIVE The main objective of this study was to evaluate the current state of AI training and the use of AI tools to enhance the learning experience. METHODS A comprehensive systematic review was conducted to analyze the use of AI in medical and health informatics education, and to evaluate existing AI training practices. PRISMA-P (Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols) guidelines were followed. The studies that focused on the use of AI tools to enhance medical education and the studies that investigated teaching AI as a new competency were categorized separately to evaluate recent developments. RESULTS This systematic review revealed that recent publications recommend the integration of AI training into medical and health informatics curricula. CONCLUSIONS To the best of our knowledge, this is the first systematic review exploring the current state of AI education in both medicine and health informatics. Since AI curricula have not been standardized and competencies have not been determined, a framework for specialized AI training in medical and health informatics education is proposed.

The impact of deep convolutional neural network‐based artificial intelligence on colonoscopy outcomes: A systematic review with meta‐analysis

2020 ◽  
Vol 35 (10) ◽  
pp. 1676-1683 ◽  
Author(s):  
Muhammad Aziz ◽  
Rawish Fatima ◽  
Charles Dong ◽  
Wade Lee‐Smith ◽  
Ali Nawras
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Systematic Review ◽  
Convolutional Neural Network ◽  
Meta Analysis ◽  
Deep Convolutional Neural Network ◽  
The Impact

scholarly journals Radiology artificial intelligence, a systematic evaluation of methods (RAISE): a systematic review protocol

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Brendan Kelly ◽  
Conor Judge ◽  
Stephanie M. Bollard ◽  
Simon M. Clifford ◽  
Gerard M. Healy ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Systematic Review ◽  
Data Extraction ◽  
Meta Analysis ◽  
Group Analysis ◽  
Cochrane Collaboration ◽  
Systematic Evaluation ◽  
Future Research ◽  
Ethical Approval ◽  
Registration Number

Abstract Introduction There has been a recent explosion of research into the field of artificial intelligence as applied to clinical radiology with the advent of highly accurate computer vision technology. These studies, however, vary significantly in design and quality. While recent guidelines have been established to advise on ethics, data management and the potential directions of future research, systematic reviews of the entire field are lacking. We aim to investigate the use of artificial intelligence as applied to radiology, to identify the clinical questions being asked, which methodological approaches are applied to these questions and trends in use over time. Methods and analysis We will follow the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines and by the Cochrane Collaboration Handbook. We will perform a literature search through MEDLINE (Pubmed), and EMBASE, a detailed data extraction of trial characteristics and a narrative synthesis of the data. There will be no language restrictions. We will take a task-centred approach rather than focusing on modality or clinical subspecialty. Sub-group analysis will be performed by segmentation tasks, identification tasks, classification tasks, pegression/prediction tasks as well as a sub-analysis for paediatric patients. Ethics and dissemination Ethical approval will not be required for this study, as data will be obtained from publicly available clinical trials. We will disseminate our results in a peer-reviewed publication. Registration number PROSPERO: CRD42020154790

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