Evidence map›Paper›PMID 38505490›Full record

ArticleEuropean heart journal. Digital health2024

Deep learning from atrioventricular plane displacement in patients with Takotsubo syndrome: lighting up the black-box.

Fahim Zaman, Nicholas Isom, Amanda Chang, Yi Grace Wang, Ahmed Abdelhamid, Arooj Khan, Majesh Makan, Mahmoud Abdelghany, Xiaodong Wu, Kan Liu

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in European heart journal. Digital health, 2024. 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
0.9field-weighted citation impact, top 22% of its field
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, 4 citations in OpenAlex.

  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

10 authors at 4 institutions in 1 country.

Fahim ZamanDepartment of Electrical and Computer Engineering, University of Iowa, 103 S. Capitol St., 3318 SC, Iowa City, IA 52242, USA.
Nicholas IsomDivision of Cardiology, Department of Internal Medicine, University of Iowa, 200 Hawkins Drive, Iowa City, IA 52242, USA.
Amanda ChangDivision of Cardiology, Department of Internal Medicine, University of Iowa, 200 Hawkins Drive, Iowa City, IA 52242, USA.
Yi Grace WangDepartment of Mathematics, California State University Dominguez Hills, 1000 E. Victoria Street, Carson, CA 90747, USA.
Ahmed AbdelhamidDivision of Cardiology, Department of Internal Medicine, University of Iowa, 200 Hawkins Drive, Iowa City, IA 52242, USA.
Arooj KhanDivision of Cardiology, Department of Internal Medicine, University of Iowa, 200 Hawkins Drive, Iowa City, IA 52242, USA.
Majesh MakanDivision of Cardiology, Department of Internal Medicine, Washington University, 4940 Parkview Place, St Louis, MO 63110, USA.
Mahmoud AbdelghanyDepartment of Cardiovascular Medicine, Cleveland Clinic, 9500 Euclid Avenue, Cleveland, OH 44195, USA.
Xiaodong WuDepartment of Electrical and Computer Engineering, University of Iowa, 103 S. Capitol St., 3318 SC, Iowa City, IA 52242, USA.
Kan LiuDivision of Cardiology, Department of Internal Medicine, University of Iowa, 200 Hawkins Drive, Iowa City, IA 52242, USA.ORCID https://orcid.org/0000-0001-5236-9166
University of Iowa · USCalifornia State University, Dominguez Hills · USCleveland Clinic · USWashington University in St. Louis · US

Funding

Implications of Spatiotemporal Deep Learning Neural Networks in Echocardiographic Diagnosis and Prognostication of Takotsubo SyndromeR01HL171624 · NHLBI · UNIVERSITY OF IOWA · PI Kan Liu, Yi Wang · 2024 to 2026
$1.3M
NHLBI NIH HHS R01 HL171624
6 · The paper itself

Abstract

Aims: The spatiotemporal deep convolutional neural network (DCNN) helps reduce echocardiographic readers' erroneous 'judgement calls' on Takotsubo syndrome (TTS). The aim of this study was to improve the interpretability of the spatiotemporal DCNN to discover latent imaging features associated with causative TTS pathophysiology. Methods and results: We applied gradient-weighted class activation mapping analysis to visualize an established spatiotemporal DCNN based on the echocardiographic videos to differentiate TTS (150 patients) from anterior wall ST-segment elevation myocardial infarction (STEMI, 150 patients). Forty-eight human expert readers interpreted the same echocardiographic videos and prioritized the regions of interest on myocardium for the differentiation. Based on visualization results, we completed optical flow measurement, myocardial strain, and Doppler/tissue Doppler echocardiography studies to investigate regional myocardial temporal dynamics and diastology. While human readers' visualization predominantly focused on the apex of the heart in TTS patients, the DCNN temporal arm's saliency visualization was attentive on the base of the heart, particularly at the atrioventricular (AV) plane. Compared with STEMI patients, TTS patients consistently showed weaker peak longitudinal displacement (in pixels) in the basal inferoseptal (systolic: 2.15 ± 1.41 vs. 3.10 ± 1.66, Conclusion: The spatiotemporal DCNN saliency visualization helps identify the pattern of myocardial temporal dynamics and navigates the quantification of regional myocardial mechanics. Reduced AV plane displacement in TTS patients likely correlates with impaired diastolic mechanics.

Indexed as

Atrioventricular planeDiastolic dysfunctionSaliency visualizationSpatiotemporal deep convolutional neural networksST-segment elevation myocardial infarctionTakotsubo syndrome

Identifiers

PMID38505490
PMCPMC10944681
OpenAlexW4389499050

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.