Evidence map›Paper›PMID 41695568›Full record

ArticleEuropean heart journal. Digital health2026

Pilot clinical evaluation of artificial intelligence-driven guiding catheter simulation for optimizing percutaneous coronary intervention.

Masataka Yoshinaga, Hirooki Higami, Eiichi Watanabe, Takashi Muramatsu, Keisuke Murata, Toru Araki, Akane Miyazaki, Makoto Fujioka, Taishi Fukushima, Takehiro Ito and 7 more

Abstract read
In one paragraph

Article in European heart journal. Digital health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

17 authors.

Masataka YoshinagaDepartment of Cardiology, Fujita Health University Bantane Hospital, 3-6-10, Otobashi, Nakagawa-ku, Nagoya, Aichi 454-8509, Japan.ORCID https://orcid.org/0009-0008-3146-3768
Hirooki HigamiTeikyo University Innovation Lab, Okinaga Research Institute, Tokyo, Japan.
Eiichi WatanabeDepartment of Cardiology, Fujita Health University Bantane Hospital, 3-6-10, Otobashi, Nakagawa-ku, Nagoya, Aichi 454-8509, Japan.
Takashi MuramatsuDepartment of Cardiology, Fujita Health University Hospital, Aichi, Japan.
Keisuke MurataDepartment of Cardiology, Fujita Health University Bantane Hospital, 3-6-10, Otobashi, Nakagawa-ku, Nagoya, Aichi 454-8509, Japan.
Toru ArakiDepartment of Cardiology, Fujita Health University Bantane Hospital, 3-6-10, Otobashi, Nakagawa-ku, Nagoya, Aichi 454-8509, Japan.
Akane MiyazakiDepartment of Cardiology, Fujita Health University Bantane Hospital, 3-6-10, Otobashi, Nakagawa-ku, Nagoya, Aichi 454-8509, Japan.
Makoto FujiokaDepartment of Clinical Engineering, Fujita Health University Bantane Hospital, Aichi, Japan.
Taishi FukushimaDepartment of Cardiology, Nagoya University, Aichi, Japan.
Takehiro ItoDepartment of Cardiology, Fujita Health University Bantane Hospital, 3-6-10, Otobashi, Nakagawa-ku, Nagoya, Aichi 454-8509, Japan.
Tatsumasa UedaDepartment of Radiology, Fujita Health University Bantane Hospital, Aichi, Japan.
Yoshihiro SobueDepartment of Cardiology, Fujita Health University Bantane Hospital, 3-6-10, Otobashi, Nakagawa-ku, Nagoya, Aichi 454-8509, Japan.ORCID https://orcid.org/0000-0002-0767-3780
Wakaya FujiwaraDepartment of Cardiology, Fujita Health University Bantane Hospital, 3-6-10, Otobashi, Nakagawa-ku, Nagoya, Aichi 454-8509, Japan.
Kenya NasuDepartment of Cardiology, Mie Heart Center, Mie, Japan.
Hitoshi MatsuoDepartment of Cardiovascular Medicine, Gifu Heart Center, Gifu, Japan.
Ken KozumaDivision of Cardiology, Teikyo University Hospital, Tokyo, Japan.
Hideo IzawaDepartment of Cardiology, Fujita Health University Hospital, Aichi, Japan.ORCID https://orcid.org/0000-0003-2136-2855

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Aims: In percutaneous coronary intervention (PCI), a suboptimal choice of guiding catheter may compromise coaxial alignment and backup support, prolonging procedures and increasing radiation and contrast exposure. We assessed whether a computed tomography (CT)-driven, artificial intelligence (AI)-guided preprocedural simulation could improve procedural efficiency and safety. Methods and results: In a single-centre prospective registry with historical controls, 55 consecutive elective procedures performed with CT-based AI-assisted guiding-catheter selection were compared with 55 procedures performed without assistance. The primary endpoint was total procedure time from arterial access to completion. Secondary endpoints included time to coronary engagement, radiation dose, contrast volume, and guiding-catheter-related events. Computed tomography--based AI assistance was associated with shorter procedures (mean 68.5 vs. 91.8 min), shorter engagement time, lower radiation dose, and lower contrast use. Guiding-catheter exchanges were fewer, and catheter-related events were lower (3.6 vs. 16.4%; risk ratio 0.22; 95% confidence interval 0.05-0.98). Procedural success was 100% in both groups with no in-hospital major adverse cardiac or cerebrovascular events. Conclusion: A CT-driven, CT-based AI-guided simulation for guiding-catheter selection was associated with greater procedural efficiency and a favourable profile in elective PCI. This approach, which standardizes catheter choice and is associated with fewer empirical catheter exchanges, warrants confirmation in multicentre randomized studies and may help optimize resource utilization in routine PCI.

Indexed as

Artificial intelligenceChronic coronary syndromeCoronary computed tomography angiographyGuiding catheter simulationInnovationPercutaneous coronary intervention

Identifiers

PMID41695568
PMCPMC12897535

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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.