Evidence map›Paper›PMID 39559783›Full record

ArticleIEEE open journal of engineering in medicine and biology2024

Anatomy-Informed Multimodal Learning for Myocardial Infarction Prediction.

Ivan-Daniel Sievering, Ortal Senouf, Thabo Mahendiran, David Nanchen, Stephane Fournier, Olivier Muller, Pascal Frossard, Emmanuel Abbe, Dorina Thanou

Abstract read
In one paragraph

Article in IEEE open journal of engineering in medicine and biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. AI-Driven multi-view learning from CCTA for myocardial infarction diagnosis.The international journal of cardiovascular imaging · 2026
    Article
  3. 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

9 authors.

Ivan-Daniel SieveringSignal Processing Laboratory 4EPFL 1015 Lausanne Switzerland.ORCID https://orcid.org/0009-0009-5423-6178
Ortal SenoufSignal Processing Laboratory 4EPFL 1015 Lausanne Switzerland.
Thabo MahendiranDepartment of Cardiology, CHUV 1005 Lausanne Switzerland.ORCID https://orcid.org/0000-0002-0025-8162
David NanchenUnisante 1011 Lausanne Switzerland.ORCID https://orcid.org/0000-0002-2493-3505
Stephane FournierDepartment of CardiologyCHUV 1005 Lausanne Switzerland.ORCID https://orcid.org/0000-0002-9422-9521
Olivier MullerDepartment of CardiologyCHUV 1005 Lausanne Switzerland.ORCID https://orcid.org/0000-0003-2441-5799
Pascal FrossardSignal Processing Laboratory 4EPFL 1015 Lausanne Switzerland.ORCID https://orcid.org/0000-0002-4010-714X
Emmanuel AbbeChair of Mathematical Data ScienceEPFL 1015 Lausanne Switzerland.
Dorina ThanouCenter for Intelligent SystemsEPFL 1015 Lausanne Switzerland.ORCID https://orcid.org/0000-0003-2319-4832

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

PubMed holds no abstract for this paper.

Indexed as

Coronary artery diseasedeep learninginvasive coronary angiographymultimodal datamyocardial infarction

Identifiers

PMID39559783
PMCPMC11573417

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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 OpenQuestion graph.