Evidence map›Paper›PMID 41603941›Full record

ArticleClinical research in cardiology : official journal of the German Cardiac Society2026

AI-assisted computed tomography analysis for pre-procedural planning prior to TAVI.

Mani Arsalan, Hanna Schneider, Kerstin Piayda, Philipp Christian Seppelt, Arnaud Van Linden, Stephan Fichtlscherer, Florian Hecker, David Leistner, Thomas Walther

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Article in Clinical research in cardiology : official journal of the German Cardiac Society, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Impact of fully-automated AI based CT-analysis on pre-procedural TAVI planning.Clinical research in cardiology : official journal of the German Cardiac Society · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Mani ArsalanDepartment of Cardiac Surgery, University Hospital of the Goethe University, Theodor-Stern-Kai 7, 60590, Frankfurt, Germany. mani.arsalan@gmail.com.ORCID http://orcid.org/0000-0001-5861-891X
Hanna SchneiderDepartment of Cardiac Surgery, University Hospital of the Goethe University, Theodor-Stern-Kai 7, 60590, Frankfurt, Germany.
Kerstin PiaydaDepartment of Cardiology and Angiology, Medical Clinic I, University Hospital of the Justus Liebig University, Klinikstrasse 33, 35392, Giessen, Germany.
Philipp Christian SeppeltDepartment of Cardiology & Angiology, Medical Clinic III, University Hospital of the Goethe University, Theodor-Stern-Kai 7, 60590, Frankfurt, Germany.
Arnaud Van LindenDepartment of Cardiac Surgery, University Hospital of the Goethe University, Theodor-Stern-Kai 7, 60590, Frankfurt, Germany.
Stephan FichtlschererDepartment of Cardiology & Angiology, Medical Clinic III, University Hospital of the Goethe University, Theodor-Stern-Kai 7, 60590, Frankfurt, Germany.
Florian HeckerDepartment of Cardiac Surgery, University Hospital of the Goethe University, Theodor-Stern-Kai 7, 60590, Frankfurt, Germany.
David LeistnerDepartment of Cardiology & Angiology, Medical Clinic III, University Hospital of the Goethe University, Theodor-Stern-Kai 7, 60590, Frankfurt, Germany.
Thomas WaltherDepartment of Cardiac Surgery, University Hospital of the Goethe University, Theodor-Stern-Kai 7, 60590, Frankfurt, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSeveral software programs have specifically been developed to analyse cardiac computed tomography prior to transcatheter aortic valve implantation (TAVI). However, they are not able to perform a complete analysis independently. We report the performance of a fully automated, deep learning-based algorithm for pre-procedural CT analysis as compared to the current clinical standard.

methodsPatients with symptomatic severe aortic stenosis undergoing TAVI were retrospectively enrolled. The pre-procedural dataset was analysed by both a standard TAVI CT-analysis software and by a fully automated CT analysis platform with a deep learning-based algorithm.

resultsNinety-eight patients were included in the analysis. The mean annulus diameter was 24.4 ± 2.4 mm (conventional = 3mensio, Pie Medical Imaging, 3 M) vs. 24.0 ± 2.4 mm (artificial intelligence = AI), mean absolute error (MAE): 0.64 mm, mean absolute percentage error (MAPE): 2.6%. The mean annulus perimeter was measured at 77.7 ± 7.4 mm (3 M) vs. 76.1 ± 7.5 mm (AI), MAE: 2.26 mm, MAPE: 2.9%. The mean annulus area was calculated at 468.9 ± 92.1 mm

conclusionIn this retrospective analysis, a deep learning-based analysis of pre-procedural CT datasets showed good to excellent correlation with conventional assessment for the preprocedural TAVI CT measurements. AI-based fully automated CT analysis could emerge to a valuable alternative to conventional CT assessment in the pre-procedural-planning for TAVI.

Indexed as

Aortic ValveAortic Valve StenosisArtificial IntelligenceDeep LearningRadiographic Image Interpretation, Computer-AssistedTomography, X-Ray ComputedTranscatheter Aortic Valve ReplacementAgedAged, 80 and overAlgorithmsFemaleHumansMaleRetrospective StudiesSeverity of Illness IndexAortic valveArtificial intelligenceComputed tomographyDeep-learningTranscatheter aortic valve implantation

Identifiers

PMID41603941
PMCPMC13249624

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.