Key result
Fully automated AI successfully extracts post-TAVI prosthesis measurements from 4D-CT scans.
Why the study?
Potential complications after TAVI demand CT scan analysis to assess implant results, requiring methods that reduce clinician time and effort in image interpretation.
Does a fully automatic AI method successfully extract prosthesis measurements from post-TAVI 4D-CT scans in patients with severe aortic stenosis?
Observational (n=13)
Does a fully automatic AI method successfully extract prosthesis measurements from post-TAVI 4D-CT scans in patients with severe aortic stenosis?
A novel fully automatic AI method successfully extracts key prosthesis measurements from post-TAVI 4D-CT scans, potentially reducing clinician time and effort in image interpretation.
Feasibility shown for automated extraction across prostheses; leaves open generalizability and clinical utility pending larger validation.
Transcatheter aortic valve implantation (TAVI) is a procedure to treat severe aortic stenosis. There are several clinical concerns related to potential complications after the procedure, which demand the analysis of computerized tomography (CT) scans after TAVI to assess the implant’s result. This work introduces a novel, fully automatic method for the analysis of post-TAVI 4D-CT scans to characterize the prosthesis and its relationship with the patient’s anatomy. The method enables measurement extraction, including prosthesis volume, center of mass, cross-sectional area (CSA) along the prosthesis axis, and CSA difference between the aortic root and prosthesis, all the variables studied throughout the cardiac cycle. The method has been implemented and evaluated with a cohort of 13 patients with five different prosthesis models, successfully extracting all the measurements from each patient in an automatic way. For Allegra patients, the mean of the obtained inner volume values ranged from 10,798.20 mm3 to 18,172.35 mm3, and CSA in the maximum diameter plane varied from 396.35 mm2 to 485.34 mm2. The implantation of this new method could provide information of the important clinical value that would contribute to the improvement of TAVI, significantly reducing the time and effort invested by clinicians in the image interpretation process.
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Busto et al. (2023) conducted an observational in Severe aortic stenosis (n=13). Automatic assessment of post-TAVI 4D-CT scans using AI was evaluated on Extraction of prosthesis measurements including volume, center of mass, and cross-sectional area. A fully automatic AI method successfully extracted post-TAVI measurements from 4D-CT scans in 13 patients, with Allegra prosthesis inner volumes ranging from 10,798.20 to 18,172.35 mm3.
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