Evidence map›Paper›PMID 40703128›Full record

ReviewEuropean heart journal. Digital health2025

Review and recommendations for using artificial intelligence in intracoronary optical coherence tomography analysis.

Xu Chen, Yuan Huang, Benn Jessney, Jason Sangha, Sophie Gu, Carola-Bibiane Schönlieb, Martin Bennett, Michael Roberts

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in European heart journal. Digital health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

8 authors.

Xu ChenDepartment of Medicine, University of Cambridge, Puddicombe Way, Cambridge CB2 0AW, UK.
Yuan HuangDepartment of Medicine, University of Cambridge, Puddicombe Way, Cambridge CB2 0AW, UK.
Benn JessneyDepartment of Medicine, University of Cambridge, Puddicombe Way, Cambridge CB2 0AW, UK.
Jason SanghaDepartment of Medicine, University of Cambridge, Puddicombe Way, Cambridge CB2 0AW, UK.
Sophie GuDepartment of Medicine, University of Cambridge, Puddicombe Way, Cambridge CB2 0AW, UK.
Carola-Bibiane SchönliebDepartment of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK.
Martin BennettDepartment of Medicine, University of Cambridge, Puddicombe Way, Cambridge CB2 0AW, UK.
Michael RobertsDepartment of Medicine, University of Cambridge, Puddicombe Way, Cambridge CB2 0AW, UK.ORCID https://orcid.org/0000-0002-3484-5031

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) tools hold great promise for the rapid and accurate diagnosis of coronary artery disease (CAD) from intravascular optical coherent tomography (IVOCT) images. Numerous papers have been published describing AI-based models for different diagnostic tasks, yet it remains unclear, which models have potential clinical utility and have been properly validated. This systematic review considered published literature between January 2015 and December 2024 describing AI-based diagnosis of CAD using IVOCT. Our search identified 8600 studies, with 629 included after initial screening and 39 studies included in the final systematic review after quality screening. Our findings indicate that most of the identified models are not currently suitable for clinical use, primarily due to methodological flaws and underlying biases. To address these issues, we provide recommendations to improve model quality and research practices to enhance the development of clinically useful AI products.

Indexed as

Artificial intelligenceDeep learningIntravascular imagingMachine learningOptical coherance tomography

Identifiers

PMID40703128
PMCPMC12282360

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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 Socratic graph.