Artificial intelligence as a diagnostic aid in cross-sectional radiological imaging of the abdominopelvic cavity: a protocol for a systematic review
Introduction The application of artificial intelligence (AI) technologies as a diagnostic aid in healthcare is increasing. Benefits include applications to improve health systems, such as rapid and accurate interpretation of medical images. This may improve the performance of diagnostic, prognostic...
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BMJ Publishing Group
2021-10-01
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| Series: | BMJ Open |
| Online Access: | https://bmjopen.bmj.com/content/11/10/e054411.full |
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| author | Natalie S Blencowe Neil J Smart George E Fowler Rhiannon C Macefield Conor Hardacre Mark P Callaway |
| author_facet | Natalie S Blencowe Neil J Smart George E Fowler Rhiannon C Macefield Conor Hardacre Mark P Callaway |
| author_sort | Natalie S Blencowe |
| collection | DOAJ |
| description | Introduction The application of artificial intelligence (AI) technologies as a diagnostic aid in healthcare is increasing. Benefits include applications to improve health systems, such as rapid and accurate interpretation of medical images. This may improve the performance of diagnostic, prognostic and management decisions. While a large amount of work has been undertaken discussing the role of AI little is understood regarding the performance of such applications in the clinical setting. This systematic review aims to critically appraise the diagnostic performance of AI algorithms to identify disease from cross-sectional radiological images of the abdominopelvic cavity, to identify current limitations and inform future research.Methods and analysis A systematic search will be conducted on Medline, EMBASE and the Cochrane Central Register of Controlled Trials to identify relevant studies. Primary studies where AI-based technologies have been used as a diagnostic aid in cross-sectional radiological images of the abdominopelvic cavity will be included. Diagnostic accuracy of AI models, including reported sensitivity, specificity, predictive values, likelihood ratios and the area under the receiver operating characteristic curve will be examined and compared with standard practice. Risk of bias of included studies will be assessed using the QUADAS-2 tool. Findings will be reported according to the Synthesis Without Meta-analysis guidelines.Ethics and dissemination No ethical approval is required as primary data will not be collected. The results will inform further research studies in this field. Findings will be disseminated at relevant conferences, on social media and published in a peer-reviewed journal.PROSPERO registration number CRD42021237249. |
| format | Article |
| id | doaj-art-768b8a9d71ce48ce8bfe86e8f8618825 |
| institution | OA Journals |
| issn | 2044-6055 |
| language | English |
| publishDate | 2021-10-01 |
| publisher | BMJ Publishing Group |
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| series | BMJ Open |
| spelling | doaj-art-768b8a9d71ce48ce8bfe86e8f86188252025-08-20T02:23:53ZengBMJ Publishing GroupBMJ Open2044-60552021-10-01111010.1136/bmjopen-2021-054411Artificial intelligence as a diagnostic aid in cross-sectional radiological imaging of the abdominopelvic cavity: a protocol for a systematic reviewNatalie S Blencowe0Neil J Smart1George E Fowler2Rhiannon C Macefield3Conor Hardacre4Mark P Callaway5National Institute for Health Research Bristol Biomedical Research Centre Surgical and Orthopaedic Innovation Theme, Bristol Centre for Surgical Research, Bristol Medical School, University of Bristol Medical School, Bristol, UKExeter Surgical Health Services Research Unit (HeSRU), Royal Devon and Exeter NHS Foundation Trust, Exeter, UKCentre for Surgical Research, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UKNational Institute for Health and Care Research Bristol Biomedical Research Centre, Bristol Centre for Surgical Research, Bristol Medical School: Population Health Sciences, University of Bristol, Bristol, UKfifth year medical studentDepartment of Clinical Radiology, University Hospital Bristol and Weston NHS Foundation Trust, Bristol, UKIntroduction The application of artificial intelligence (AI) technologies as a diagnostic aid in healthcare is increasing. Benefits include applications to improve health systems, such as rapid and accurate interpretation of medical images. This may improve the performance of diagnostic, prognostic and management decisions. While a large amount of work has been undertaken discussing the role of AI little is understood regarding the performance of such applications in the clinical setting. This systematic review aims to critically appraise the diagnostic performance of AI algorithms to identify disease from cross-sectional radiological images of the abdominopelvic cavity, to identify current limitations and inform future research.Methods and analysis A systematic search will be conducted on Medline, EMBASE and the Cochrane Central Register of Controlled Trials to identify relevant studies. Primary studies where AI-based technologies have been used as a diagnostic aid in cross-sectional radiological images of the abdominopelvic cavity will be included. Diagnostic accuracy of AI models, including reported sensitivity, specificity, predictive values, likelihood ratios and the area under the receiver operating characteristic curve will be examined and compared with standard practice. Risk of bias of included studies will be assessed using the QUADAS-2 tool. Findings will be reported according to the Synthesis Without Meta-analysis guidelines.Ethics and dissemination No ethical approval is required as primary data will not be collected. The results will inform further research studies in this field. Findings will be disseminated at relevant conferences, on social media and published in a peer-reviewed journal.PROSPERO registration number CRD42021237249.https://bmjopen.bmj.com/content/11/10/e054411.full |
| spellingShingle | Natalie S Blencowe Neil J Smart George E Fowler Rhiannon C Macefield Conor Hardacre Mark P Callaway Artificial intelligence as a diagnostic aid in cross-sectional radiological imaging of the abdominopelvic cavity: a protocol for a systematic review BMJ Open |
| title | Artificial intelligence as a diagnostic aid in cross-sectional radiological imaging of the abdominopelvic cavity: a protocol for a systematic review |
| title_full | Artificial intelligence as a diagnostic aid in cross-sectional radiological imaging of the abdominopelvic cavity: a protocol for a systematic review |
| title_fullStr | Artificial intelligence as a diagnostic aid in cross-sectional radiological imaging of the abdominopelvic cavity: a protocol for a systematic review |
| title_full_unstemmed | Artificial intelligence as a diagnostic aid in cross-sectional radiological imaging of the abdominopelvic cavity: a protocol for a systematic review |
| title_short | Artificial intelligence as a diagnostic aid in cross-sectional radiological imaging of the abdominopelvic cavity: a protocol for a systematic review |
| title_sort | artificial intelligence as a diagnostic aid in cross sectional radiological imaging of the abdominopelvic cavity a protocol for a systematic review |
| url | https://bmjopen.bmj.com/content/11/10/e054411.full |
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