Evidence map›Paper›PMID 38560740›Full record

ArticleArXiv2025

A computational pipeline for clustering left atrial appendage morphology via elastic shape analysis.

Zan Ahmad, Minglang Yin, Yashil Sukurdeep, Noam Rotenberg, Ritu Yadav, Jenna Milstein, Linh Thi My Tran, Calvin O'Donnell, Sarah Schumacher, Craig Cronin and 5 more

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PubMed Full text at PMC
In one paragraph

Article in ArXiv, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

5 · Who and what money

Authors and funding

15 authors.

Zan AhmadDepartment of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, USA.
Minglang YinDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
Yashil SukurdeepDepartment of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, USA.
Noam RotenbergDepartment of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, USA.
Ritu YadavJohns Hopkins Hospital, Baltimore, MD, USA.
Jenna MilsteinJohns Hopkins Hospital, Baltimore, MD, USA.
Linh Thi My TranJohns Hopkins Hospital, Baltimore, MD, USA.
Calvin O'DonnellJohns Hopkins Hospital, Baltimore, MD, USA.
Sarah SchumacherJohns Hopkins Hospital, Baltimore, MD, USA.
Craig CroninJohns Hopkins Hospital, Baltimore, MD, USA.
Robert WeinsteinJohns Hopkins Hospital, Baltimore, MD, USA.
Danish Iltaf SattiJohns Hopkins Hospital, Baltimore, MD, USA.
David SpraggJohns Hopkins Hospital, Baltimore, MD, USA.
Eugene KholmovksiDepartment of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, USA.
Natalia TrayanovaDepartment of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, USA.

Funding

CARDIOVASCULAR EPIDEMIOLOGY INSTITUTIONAL TRAININGT32HL007024 · NHLBI · JOHNS HOPKINS UNIVERSITY · PI Elizabeth Selvin · 1985 to 2026
$17.5M
Artificial intelligence analysis of atrial remodeling evolution in patients with atrial fibrillation: Towards optimal ablation strategiesR01HL166759 · NHLBI · JOHNS HOPKINS UNIVERSITY · PI Eugene Kholmovski, David Spragg · 2023 to 2026
$3.2M
Infarct-related Ventricular Tachycardia Mechanisms: From Micro to ClinicalR01HL142496 · NHLBI · JOHNS HOPKINS UNIVERSITY · PI TRAYANOVA, NATALIA A. · 2019 to 2022
$3.1M
NHLBI NIH HHS R01 HL142496NHLBI NIH HHS R01 HL166759NHLBI NIH HHS T32 HL007024
6 · The paper itself

Abstract

Morphological variations in the left atrial appendage (LAA) are associated with different levels of ischemic stroke risk for patients with atrial fibrillation (AF). Studying LAA morphology can elucidate mechanisms behind this association and lead to the development of advanced stroke risk stratification tools. However, current categorical descriptions of LAA morphologies are qualitative in nature, and inconsistent across studies, which impedes advancements in our understanding of stroke pathogenesis in AF. To mitigate these issues, we introduce a quantitative pipeline that combines elastic shape analysis with unsupervised learning for the categorization of LAA morphology in AF patients. We demonstrate that our method reliably clusters LAAs based on their geometric features, and thus provides an avenue to overcome the limitations of current qualitative LAA categorization systems.

Indexed as

atrial fibrillationleft atrial appendageshape analysisstroke

Identifiers

PMID38560740
PMCPMC10980091

What Socratic holds

Textmetadata
LicenceCC BY-NC-SA
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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.