Evidence map›Paper›PMID 38294678›Full record

ArticlePhysical and engineering sciences in medicine2024

CT angiography prior to endovascular procedures: can artificial intelligence improve reporting?

Enrico Boninsegna, Stefano Piffer, Emilio Simonini, Michele Romano, Corrado Lettieri, Stefano Colopi, Giampietro Barai

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Article in Physical and engineering sciences in medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
  4. Harnessing Artificial Intelligence for Innovation in Interventional Cardiovascular Care.Journal of the Society for Cardiovascular Angiography & Interventions · 2025
    Review
  5. Review
  6. Article
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

7 authors.

Enrico BoninsegnaDepartment of Radiology, Azienda Socio Sanitaria Territoriale di Mantova, St. Lago Paiolo 10, 46100, Mantova, Italy. boninsegnae@gmail.com.ORCID http://orcid.org/0000-0002-7509-6786
Stefano PifferDepartment of Medical Physics, Azienda Socio Sanitaria Territoriale di Mantova, Mantova, Italy.
Emilio SimoniniDepartment of Radiology, Azienda Socio Sanitaria Territoriale di Mantova, St. Lago Paiolo 10, 46100, Mantova, Italy.
Michele RomanoDepartment of Cardiology, Azienda Socio Sanitaria Territoriale di Mantova, Mantova, Italy.
Corrado LettieriDepartment of Cardiology, Azienda Socio Sanitaria Territoriale di Mantova, Mantova, Italy.
Stefano ColopiDepartment of Radiology, Azienda Socio Sanitaria Territoriale di Mantova, St. Lago Paiolo 10, 46100, Mantova, Italy.
Giampietro BaraiDepartment of Medical Physics, Azienda Socio Sanitaria Territoriale di Mantova, Mantova, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

CT angiography prior to endovascular aortic surgery is the standard non-invasive imaging method for evaluation of aortic dimensions and access sites. A detailed report is crucial to a proper planning. We assessed Artificial Intelligence (AI)-algorithm accuracy to measure vessels diameters at CT prior to transcatheter aortic valve implantation (TAVI). CT scans of 50 patients were included. Two Radiologists with experience in vascular imaging together manually assessed diameters at nine landmark positions according to the American Heart Association guidelines: 450 values were obtained. We implemented TOST (Two One-Sided Test) to determine whether the measurements were equivalent to the values obtained from the AI algorithm. When the equivalence bound was a range of ± 2 mm the test showed equivalence for every point; if the range was equal to ± 1 mm the two measurements were not equivalent in 6 points out of 9 (p-value > 0.05), close to the aortic valve. The time for automatic evaluation (average 1 min 47 s) was significantly lower compared with manual measurements (5 min 41 s) (p < 0.01). In conclusion, our results indicate that AI-algorithms can measure aortic diameters at CT prior to endovascular surgery with high accuracy. AI-assisted reporting promises high efficiency, reduced inter-reader variabilities and time saving. In order to perform optimal TAVI procedure planning aortic root analysis could be improved, including annulus dimensions.

Indexed as

Artificial IntelligenceComputed Tomography AngiographyEndovascular ProceduresAgedAged, 80 and overAlgorithmsAortaFemaleHumansMaleTranscatheter Aortic Valve ReplacementAortaArtificial IntelligenceComputed tomography angiographySoftwareTranscatheter aortic valve replacement

Identifiers

PMID38294678

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Registered trials

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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.