J Rueckel1, P Reidler2, N Fink3, J Sperl4, T Geyer2, M P Fabritius2, J Ricke2, M Ingrisch2, B O Sabel2. 1. Department of Radiology, University Hospital, LMU Munich, Munich, Germany. Electronic address: johannes.rueckel@med.uni-muenchen.de. 2. Department of Radiology, University Hospital, LMU Munich, Munich, Germany. 3. Department of Radiology, University Hospital, LMU Munich, Munich, Germany; Comprehensive Pneumology Center (CPC-M), Member of the German Center for Lung Research (DZL), Munich, Germany. 4. Siemens Healthineers AG, Erlangen, Germany.
Abstract
OBJECTIVE: Follow-up of aortic aneurysms by computed tomography (CT) is crucial to balance the risks of treatment and rupture. Artificial intelligence (AI)-assisted radiology reporting promises time savings and reduced inter-reader variabilities. METHODS: The influence of AI assistance on the efficiency and accuracy of aortic aneurysm reporting according to the AHA / ESC guidelines was quantified based on 324 AI measurements and 1944 radiological measurements: 18 aortic aneurysm patients, each with two CT scans (arterial contrast phase, electrocardiogram-gated) with an interval of at least six months have been included. One board-certified radiologist and two residents (8/4/2 years of experience in vascular imaging) independently assessed aortic diameters at nine landmark positions. Aneurysm extensions were compared with original CT reports. After three weeks washout period, CTs were re-assessed, based on graphically illustrated AI measurements. RESULTS: Time-consuming guideline-compliant aortic measurements revealed additional affections of the root / arch for 80 % of aneurysms that had initially been reported to be limited to the ascending aorta. AI assistance reduced mean reporting time by 63 % from 13:01 to 04:46 min including manual corrections of AI measurements (performed for 33.6 % of all measurements with predominance at the sinuses of Vasalva). AI assistance reduced total diameter inter-reader variability by 42.5 % (0.42 / 1.16 mm with / without AI assistance, mean of all patients and landmark positions, significant reduction for 6 out of 9 measuring positions). Conventional and AI-assisted quantification aneurysm progress varied to small extent (mean of 0.75 mm over all patients / landmark positions) not significantly exceeding radiologist's inter-reader variabilities. CONCLUSIONS: Guideline-compliant aorta measurement is crucial to report detailed aneurysm extension which might affect the strategy of interventional repair. AI assistance promises improved reporting efficiency and has high potential to reduce radiologist's inter-reader variabilities that can hamper diagnostic follow-up accuracy. KEY POINT: The time-consuming guideline-compliant aorta aneurysm assessment is crucial to report aneurysm extension in detail; AI-assisted measurement reduces reporting time, improves extension evaluation and reduces inter-reader variability.
OBJECTIVE: Follow-up of aortic aneurysms by computed tomography (CT) is crucial to balance the risks of treatment and rupture. Artificial intelligence (AI)-assisted radiology reporting promises time savings and reduced inter-reader variabilities. METHODS: The influence of AI assistance on the efficiency and accuracy of aortic aneurysm reporting according to the AHA / ESC guidelines was quantified based on 324 AI measurements and 1944 radiological measurements: 18 aortic aneurysm patients, each with two CT scans (arterial contrast phase, electrocardiogram-gated) with an interval of at least six months have been included. One board-certified radiologist and two residents (8/4/2 years of experience in vascular imaging) independently assessed aortic diameters at nine landmark positions. Aneurysm extensions were compared with original CT reports. After three weeks washout period, CTs were re-assessed, based on graphically illustrated AI measurements. RESULTS: Time-consuming guideline-compliant aortic measurements revealed additional affections of the root / arch for 80 % of aneurysms that had initially been reported to be limited to the ascending aorta. AI assistance reduced mean reporting time by 63 % from 13:01 to 04:46 min including manual corrections of AI measurements (performed for 33.6 % of all measurements with predominance at the sinuses of Vasalva). AI assistance reduced total diameter inter-reader variability by 42.5 % (0.42 / 1.16 mm with / without AI assistance, mean of all patients and landmark positions, significant reduction for 6 out of 9 measuring positions). Conventional and AI-assisted quantification aneurysm progress varied to small extent (mean of 0.75 mm over all patients / landmark positions) not significantly exceeding radiologist's inter-reader variabilities. CONCLUSIONS: Guideline-compliant aorta measurement is crucial to report detailed aneurysm extension which might affect the strategy of interventional repair. AI assistance promises improved reporting efficiency and has high potential to reduce radiologist's inter-reader variabilities that can hamper diagnostic follow-up accuracy. KEY POINT: The time-consuming guideline-compliant aorta aneurysm assessment is crucial to report aneurysm extension in detail; AI-assisted measurement reduces reporting time, improves extension evaluation and reduces inter-reader variability.
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