Evidence map›Paper›PMID 37404797›Full record

ArticleRadiology. Cardiothoracic imaging2023

Deep Learning Synthetic Strain: Quantitative Assessment of Regional Myocardial Wall Motion at MRI.

Evan M Masutani, Rahul S Chandrupatla, Shuo Wang, Chiara Zocchi, Lewis D Hahn, Michael Horowitz, Kathleen Jacobs, Seth Kligerman, Francesca Raimondi, Amit Patel and 1 more

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

Article in Radiology. Cardiothoracic imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 1 pooled it
2.5field-weighted citation impact, top 11% 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

8 citing papers in PubMed, 1 synthesis or guideline pooled it, 11 citations in OpenAlex.

  1. Pooled it
  2. Review
  3. Circumferential strain recovery after human cardiomyocyte transplantation in minipigs using a novel frequency-based method for myocardial tagging quantification.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2026
    Article
  4. Review
  5. Review
  6. Characteristics of left ventricular dysfunction in repaired tetralogy of Fallot: A multi-institutional deep learning analysis of regional strain and dyssynchrony.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2025
    Article
  7. Review
  8. Radiology. Cardiothoracic imaging · 2024
    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

11 authors at 2 institutions in 3 countries.

Evan M MasutaniFrom the Departments of Bioengineering (E.M.M.) and Radiology (R.S.C., L.D.H., M.H., K.J., S.K., A.H.), University of California, San Diego, 9300 Campus Point Dr, MC 0841, La Jolla, CA 92037-0841; Department of Medicine, University of Virginia, Charlottesville, Va (S.W., A.P.); and Meyer Children's Hospital IRCCS, Cardiac Imaging Unit, Pediatric Cardiology, University of Florence, Florence, Italy (C.Z., F.R.).ORCID https://orcid.org/0000-0002-6743-5514
Rahul S ChandrupatlaFrom the Departments of Bioengineering (E.M.M.) and Radiology (R.S.C., L.D.H., M.H., K.J., S.K., A.H.), University of California, San Diego, 9300 Campus Point Dr, MC 0841, La Jolla, CA 92037-0841; Department of Medicine, University of Virginia, Charlottesville, Va (S.W., A.P.); and Meyer Children's Hospital IRCCS, Cardiac Imaging Unit, Pediatric Cardiology, University of Florence, Florence, Italy (C.Z., F.R.).ORCID 0000-0002-5189-5751
Shuo WangFrom the Departments of Bioengineering (E.M.M.) and Radiology (R.S.C., L.D.H., M.H., K.J., S.K., A.H.), University of California, San Diego, 9300 Campus Point Dr, MC 0841, La Jolla, CA 92037-0841; Department of Medicine, University of Virginia, Charlottesville, Va (S.W., A.P.); and Meyer Children's Hospital IRCCS, Cardiac Imaging Unit, Pediatric Cardiology, University of Florence, Florence, Italy (C.Z., F.R.).ORCID 0000-0003-3588-3475
Chiara ZocchiFrom the Departments of Bioengineering (E.M.M.) and Radiology (R.S.C., L.D.H., M.H., K.J., S.K., A.H.), University of California, San Diego, 9300 Campus Point Dr, MC 0841, La Jolla, CA 92037-0841; Department of Medicine, University of Virginia, Charlottesville, Va (S.W., A.P.); and Meyer Children's Hospital IRCCS, Cardiac Imaging Unit, Pediatric Cardiology, University of Florence, Florence, Italy (C.Z., F.R.).
Lewis D HahnFrom the Departments of Bioengineering (E.M.M.) and Radiology (R.S.C., L.D.H., M.H., K.J., S.K., A.H.), University of California, San Diego, 9300 Campus Point Dr, MC 0841, La Jolla, CA 92037-0841; Department of Medicine, University of Virginia, Charlottesville, Va (S.W., A.P.); and Meyer Children's Hospital IRCCS, Cardiac Imaging Unit, Pediatric Cardiology, University of Florence, Florence, Italy (C.Z., F.R.).
Michael HorowitzFrom the Departments of Bioengineering (E.M.M.) and Radiology (R.S.C., L.D.H., M.H., K.J., S.K., A.H.), University of California, San Diego, 9300 Campus Point Dr, MC 0841, La Jolla, CA 92037-0841; Department of Medicine, University of Virginia, Charlottesville, Va (S.W., A.P.); and Meyer Children's Hospital IRCCS, Cardiac Imaging Unit, Pediatric Cardiology, University of Florence, Florence, Italy (C.Z., F.R.).
Kathleen JacobsFrom the Departments of Bioengineering (E.M.M.) and Radiology (R.S.C., L.D.H., M.H., K.J., S.K., A.H.), University of California, San Diego, 9300 Campus Point Dr, MC 0841, La Jolla, CA 92037-0841; Department of Medicine, University of Virginia, Charlottesville, Va (S.W., A.P.); and Meyer Children's Hospital IRCCS, Cardiac Imaging Unit, Pediatric Cardiology, University of Florence, Florence, Italy (C.Z., F.R.).
Seth KligermanFrom the Departments of Bioengineering (E.M.M.) and Radiology (R.S.C., L.D.H., M.H., K.J., S.K., A.H.), University of California, San Diego, 9300 Campus Point Dr, MC 0841, La Jolla, CA 92037-0841; Department of Medicine, University of Virginia, Charlottesville, Va (S.W., A.P.); and Meyer Children's Hospital IRCCS, Cardiac Imaging Unit, Pediatric Cardiology, University of Florence, Florence, Italy (C.Z., F.R.).ORCID 0000-0002-1532-1371
Francesca RaimondiFrom the Departments of Bioengineering (E.M.M.) and Radiology (R.S.C., L.D.H., M.H., K.J., S.K., A.H.), University of California, San Diego, 9300 Campus Point Dr, MC 0841, La Jolla, CA 92037-0841; Department of Medicine, University of Virginia, Charlottesville, Va (S.W., A.P.); and Meyer Children's Hospital IRCCS, Cardiac Imaging Unit, Pediatric Cardiology, University of Florence, Florence, Italy (C.Z., F.R.).ORCID 0000-0003-2580-151X
Amit PatelFrom the Departments of Bioengineering (E.M.M.) and Radiology (R.S.C., L.D.H., M.H., K.J., S.K., A.H.), University of California, San Diego, 9300 Campus Point Dr, MC 0841, La Jolla, CA 92037-0841; Department of Medicine, University of Virginia, Charlottesville, Va (S.W., A.P.); and Meyer Children's Hospital IRCCS, Cardiac Imaging Unit, Pediatric Cardiology, University of Florence, Florence, Italy (C.Z., F.R.).ORCID 0000-0001-7621-6463
Albert HsiaoFrom the Departments of Bioengineering (E.M.M.) and Radiology (R.S.C., L.D.H., M.H., K.J., S.K., A.H.), University of California, San Diego, 9300 Campus Point Dr, MC 0841, La Jolla, CA 92037-0841; Department of Medicine, University of Virginia, Charlottesville, Va (S.W., A.P.); and Meyer Children's Hospital IRCCS, Cardiac Imaging Unit, Pediatric Cardiology, University of Florence, Florence, Italy (C.Z., F.R.).ORCID 0000-0002-9412-1369
University of California San Diego · USMeyer Children's Hospital · IL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: To assess the feasibility of a newly developed algorithm, called Materials and Methods: In this retrospective study, DLSS was developed by using a data set of 223 cardiac MRI examinations including cine SSFP images and four-dimensional flow velocity data (November 2017 to May 2021). To establish normal ranges, segmental strain was measured in 40 individuals (mean age, 41 years ± 17 [SD]; 30 men) without cardiac disease. Then, DLSS performance in the detection of wall motion abnormalities was assessed in a separate group of patients with coronary artery disease, and these findings were compared with consensus results of four independent cardiothoracic radiologists (ground truth). Algorithm performance was evaluated by using receiver operating characteristic curve analysis. Results: Median peak segmental radial strain in individuals with normal cardiac MRI findings was 38% (IQR: 30%-48%). Among patients with ischemic heart disease (846 segments in 53 patients; mean age, 61 years ± 12; 41 men), the Cohen κ among four cardiothoracic readers for detecting wall motion abnormalities was 0.60-0.78. DLSS achieved an area under the receiver operating characteristic curve of 0.90. Using a fixed 30% threshold for abnormal peak radial strain, the algorithm achieved a sensitivity, specificity, and accuracy of 86%, 85%, and 86%, respectively. Conclusion: The deep learning algorithm had comparable performance with subspecialty radiologists in inferring myocardial velocity from cine SSFP images and identifying myocardial wall motion abnormalities at rest in patients with ischemic heart disease.

Indexed as

CardiacIschemia/InfarctionMR ImagingNeural Networks

Identifiers

PMID37404797
PMCPMC10316298
OpenAlexW4376155261

What Socratic holds

Textmetadata
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.