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
Introduction The 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...
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BMJ Publishing Group
2021-03-01
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| Series: | BMJ Open |
| Online Access: | https://bmjopen.bmj.com/content/11/3/e043665.full |
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| author | Sankar Prasad Gorthi Srinivasa Rao Kundeti Manikanda Krishnan Vaidyanathan Bharath Shivashankar |
| author_facet | Sankar Prasad Gorthi Srinivasa Rao Kundeti Manikanda Krishnan Vaidyanathan Bharath Shivashankar |
| author_sort | Sankar Prasad Gorthi |
| collection | DOAJ |
| description | Introduction The 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 analysis We 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 dissemination There 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 number CRD42020179652. |
| format | Article |
| id | doaj-art-d3d79cd3bdd84a0f8685635d0d1710b2 |
| institution | Kabale University |
| issn | 2044-6055 |
| language | English |
| publishDate | 2021-03-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | BMJ Open |
| spelling | doaj-art-d3d79cd3bdd84a0f8685635d0d1710b22024-11-17T22:50:09ZengBMJ Publishing GroupBMJ Open2044-60552021-03-0111310.1136/bmjopen-2020-043665Systematic review protocol to assess artificial intelligence diagnostic accuracy performance in detecting acute ischaemic stroke and large-vessel occlusions on CT and MR medical imagingSankar Prasad Gorthi0Srinivasa Rao Kundeti1Manikanda Krishnan Vaidyanathan2Bharath Shivashankar3Department of Neurology, Kasturba Medical College, Manipal Academy of Higher Education, Manipal, IndiaDepartment of Neurology, Kasturba Medical College, Manipal Academy of Higher Education, Manipal, IndiaPhilips Research, Philips Innovation Campus, Bangalore, IndiaPhilips Research, Philips Innovation Campus, Bangalore, IndiaIntroduction The 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 analysis We 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 dissemination There 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 number CRD42020179652.https://bmjopen.bmj.com/content/11/3/e043665.full |
| spellingShingle | Sankar Prasad Gorthi Srinivasa Rao Kundeti Manikanda Krishnan Vaidyanathan Bharath Shivashankar 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 |
| title | 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | 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 |
| url | https://bmjopen.bmj.com/content/11/3/e043665.full |
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