Evidence map›Paper›PMID 41106896›Full record

ArticleJACC. Case reports2025

Artificial Intelligence-Driven High-Resolution Coronary CT Angiography for PCI Planning in Severe Coronary Calcification.

Hirofumi Ohashi, Hirohiko Ando, Masanobu Fujimoto, Wataru Suzuki, Koshiro Sakai, Tetsuya Amano

Abstract readCase Reports
In one paragraph

Article in JACC. Case reports, 2025. 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. Review
  2. 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

6 authors.

Hirofumi OhashiDepartment of Cardiology, Aichi Medical University, Aichi, Japan. Electronic address: h.ohashi0712@gmail.com.
Hirohiko AndoDepartment of Cardiology, Aichi Medical University, Aichi, Japan.
Masanobu FujimotoDepartment of Cardiology, Aichi Medical University, Aichi, Japan.
Wataru SuzukiDepartment of Cardiology, Aichi Medical University, Aichi, Japan.
Koshiro SakaiDepartment of Medicine, Division of Cardiology, Showa University School of Medicine, Tokyo, Japan.
Tetsuya AmanoDepartment of Cardiology, Aichi Medical University, Aichi, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSevere calcified lesions are difficult to assess using conventional coronary computed tomography angiography (CCTA). Artificial intelligence (AI)-driven reconstruction may help address this limitation. CASE SUMMARY: A 93-year-old man with angina underwent AI-enhanced high-resolution CCTA using the Aquilion ONE/INSIGHT Edition (Canon Medical Systems). The AI-enhanced imaging clearly revealed severe eccentric calcification from the distal left main trunk to the proximal left anterior descending artery. Intravascular ultrasound confirmed this finding. Percutaneous coronary intervention (PCI) was performed with orbital atherectomy, cutting balloon predilation, and drug-eluting stent implantation, achieving good stent expansion. DISCUSSION: AI-enhanced CCTA enabled the accurate visualization and measurement of calcified plaques, including their arc and thickness, facilitating the selection of appropriate devices and lesion preparation for PCI. TAKE-HOME MESSAGES: AI-enhanced CCTA provides precise evaluation of severely calcified coronary plaques. It guides PCI strategy by accurately assessing calcium burden and plaque morphology.

Indexed as

artificial intelligencecalcified coronary lesioncoronary computed tomography angiographyhigh-resolution imagingpercutaneous coronary intervention

Identifiers

PMID41106896
PMCPMC12793862

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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.