P21 Artificial intelligence assessment of the thoracic aorta is accurate, reliable and has potential clinical impact. (21st September 2022)
- Record Type:
- Journal Article
- Title:
- P21 Artificial intelligence assessment of the thoracic aorta is accurate, reliable and has potential clinical impact. (21st September 2022)
- Main Title:
- P21 Artificial intelligence assessment of the thoracic aorta is accurate, reliable and has potential clinical impact
- Authors:
- Harris, Maredudd
Graby, John
Jones, Calum
Waring, Harry
Lyen, Stephen
Hudson, Ben
Rodrigues, Jonathan - Abstract:
- Abstract : Objective: To assess the diagnostic accuracy, reliability and clinical impact of artificial intelligence (AI) derived thoracic aorta analysis (AI-Rad Companion, Siemens) on routine clinical gated and non-gated chest CT. Methods: This was a single centre retrospective study. AI diagnostic accuracy was assessed on 210 consecutive CT aortas and compared to cardiothoracic radiologist reference standard. AI test-retest accuracy was assessed on immediate sequential pre- and post-contrast CT aortas in 29 patients. Real-world AI clinical impact was assessed in 197 non-gated CT chests with comparison to manual radiology reports and patient electronic records to establish the detection rate of previously unknown aortopathy. Results: AI analysis was feasible in 97% (421/436 scans). Diagnostic accuracy of AI was good to excellent (intraclass correlation coefficient [ICC] 0.87–0.96). Test-retest accuracy of expert reader (ICC 0.88–0.98) and AI (ICC 0.82–0.94) for the ascending aorta were good to excellent. AI identified new aortopathy in 27% of non-gated scans versus routine clinical reports (X2 51, p<0.001). Conclusion: AI provides measurements of the thoracic aorta comparable to an expert reader with similar reliability. Whereas manual reporting of non-dedicated studies significantly underreports thoracic aneurysms, AI identifies previously unknown aortopathy in a significant proportion (27%) of non-gated CT chests. The use of AI software in non-dedicated CT chest imagingAbstract : Objective: To assess the diagnostic accuracy, reliability and clinical impact of artificial intelligence (AI) derived thoracic aorta analysis (AI-Rad Companion, Siemens) on routine clinical gated and non-gated chest CT. Methods: This was a single centre retrospective study. AI diagnostic accuracy was assessed on 210 consecutive CT aortas and compared to cardiothoracic radiologist reference standard. AI test-retest accuracy was assessed on immediate sequential pre- and post-contrast CT aortas in 29 patients. Real-world AI clinical impact was assessed in 197 non-gated CT chests with comparison to manual radiology reports and patient electronic records to establish the detection rate of previously unknown aortopathy. Results: AI analysis was feasible in 97% (421/436 scans). Diagnostic accuracy of AI was good to excellent (intraclass correlation coefficient [ICC] 0.87–0.96). Test-retest accuracy of expert reader (ICC 0.88–0.98) and AI (ICC 0.82–0.94) for the ascending aorta were good to excellent. AI identified new aortopathy in 27% of non-gated scans versus routine clinical reports (X2 51, p<0.001). Conclusion: AI provides measurements of the thoracic aorta comparable to an expert reader with similar reliability. Whereas manual reporting of non-dedicated studies significantly underreports thoracic aneurysms, AI identifies previously unknown aortopathy in a significant proportion (27%) of non-gated CT chests. The use of AI software in non-dedicated CT chest imaging could support earlier diagnosis of thoracic aneurysms before potentially fatal complications. … (more)
- Is Part Of:
- Heart. Volume 108(2022)Supplement 2
- Journal:
- Heart
- Issue:
- Volume 108(2022)Supplement 2
- Issue Display:
- Volume 108, Issue 2 (2022)
- Year:
- 2022
- Volume:
- 108
- Issue:
- 2
- Issue Sort Value:
- 2022-0108-0002-0000
- Page Start:
- A10
- Page End:
- A10
- Publication Date:
- 2022-09-21
- Subjects:
- Heart -- Diseases -- Treatment -- Periodicals
Cardiology -- Periodicals
616.12 - Journal URLs:
- http://www.bmj.com/archive ↗
http://heart.bmj.com ↗
http://www.heartjnl.com ↗ - DOI:
- 10.1136/heartjnl-2022-BSCI.26 ↗
- Languages:
- English
- ISSNs:
- 1355-6037
- Deposit Type:
- Legaldeposit
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- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - BLDSS-3PM
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