Evidence map›Paper›PMID 40381163›Full record

ArticleLa Radiologia medica2025

Accelerated deep learning-based function assessment in cardiovascular magnetic resonance.

Domenico De Santis, Federica Fanelli, Luca Pugliese, Giovanna Grazia Bona, Tiziano Polidori, Curzio Santangeli, Michela Polici, Antonella Del Gaudio, Giuseppe Tremamunno, Marta Zerunian and 2 more

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In one paragraph

Article in La Radiologia medica, 2025. 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
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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

12 authors.

Domenico De SantisDepartment of Medical-Surgical Sciences and Translational Medicine, School of Medicine and Psychology, Sapienza - University of Rome, Sant'Andrea University Hospital, Via Di Grottarossa, 1035-1039, 00189, Rome, Italy.ORCID http://orcid.org/0000-0002-1197-456X
Federica FanelliDepartment of Medical-Surgical Sciences and Translational Medicine, School of Medicine and Psychology, Sapienza - University of Rome, Sant'Andrea University Hospital, Via Di Grottarossa, 1035-1039, 00189, Rome, Italy.
Luca PuglieseDepartment of Medical-Surgical Sciences and Translational Medicine, School of Medicine and Psychology, Sapienza - University of Rome, Sant'Andrea University Hospital, Via Di Grottarossa, 1035-1039, 00189, Rome, Italy.ORCID http://orcid.org/0000-0001-9061-7247
Giovanna Grazia BonaDepartment of Medical-Surgical Sciences and Translational Medicine, School of Medicine and Psychology, Sapienza - University of Rome, Sant'Andrea University Hospital, Via Di Grottarossa, 1035-1039, 00189, Rome, Italy.
Tiziano PolidoriDepartment of Medical-Surgical Sciences and Translational Medicine, School of Medicine and Psychology, Sapienza - University of Rome, Sant'Andrea University Hospital, Via Di Grottarossa, 1035-1039, 00189, Rome, Italy.ORCID http://orcid.org/0000-0002-4633-6760
Curzio SantangeliDepartment of Medical-Surgical Sciences and Translational Medicine, School of Medicine and Psychology, Sapienza - University of Rome, Sant'Andrea University Hospital, Via Di Grottarossa, 1035-1039, 00189, Rome, Italy.ORCID http://orcid.org/0009-0004-4364-783X
Michela PoliciDepartment of Medical-Surgical Sciences and Translational Medicine, School of Medicine and Psychology, Sapienza - University of Rome, Sant'Andrea University Hospital, Via Di Grottarossa, 1035-1039, 00189, Rome, Italy.ORCID http://orcid.org/0000-0001-7331-6901
Antonella Del GaudioDepartment of Medical-Surgical Sciences and Translational Medicine, School of Medicine and Psychology, Sapienza - University of Rome, Sant'Andrea University Hospital, Via Di Grottarossa, 1035-1039, 00189, Rome, Italy.ORCID http://orcid.org/0000-0001-6017-8976
Giuseppe TremamunnoDepartment of Medical-Surgical Sciences and Translational Medicine, School of Medicine and Psychology, Sapienza - University of Rome, Sant'Andrea University Hospital, Via Di Grottarossa, 1035-1039, 00189, Rome, Italy.ORCID http://orcid.org/0000-0001-6484-9765
Marta ZerunianDepartment of Medical-Surgical Sciences and Translational Medicine, School of Medicine and Psychology, Sapienza - University of Rome, Sant'Andrea University Hospital, Via Di Grottarossa, 1035-1039, 00189, Rome, Italy.ORCID http://orcid.org/0000-0003-4635-7999
Andrea LaghiDepartment of Medical-Surgical Sciences and Translational Medicine, School of Medicine and Psychology, Sapienza - University of Rome, Sant'Andrea University Hospital, Via Di Grottarossa, 1035-1039, 00189, Rome, Italy. andrea.laghi@uniroma1.it.ORCID http://orcid.org/0000-0002-3091-7819
Damiano CarusoDepartment of Medical-Surgical Sciences and Translational Medicine, School of Medicine and Psychology, Sapienza - University of Rome, Sant'Andrea University Hospital, Via Di Grottarossa, 1035-1039, 00189, Rome, Italy.ORCID http://orcid.org/0000-0001-9285-4764

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTo evaluate diagnostic accuracy and image quality of deep learning (DL) cine sequences for LV and RV parameters compared to conventional balanced steady-state free precession (bSSFP) cine sequences in cardiovascular magnetic resonance (CMR). MATERIAL AND

methodsFrom January to April 2024, patients with clinically indicated CMR were prospectively included. LV and RV were segmented from short-axis bSSFP and DL cine sequences. LV and RV end-diastolic volume (EDV), end-systolic volume (EDV), stroke volume (SV), ejection fraction, and LV end-diastolic mass were calculated. The acquisition time of both sequences was registered. Results were compared with paired-samples t test or Wilcoxon signed-rank test. Agreement between DL cine and bSSFP was assessed using Bland-Altman plots. Image quality was graded by two readers based on blood-to-myocardium contrast, endocardial edge definition, and motion artifacts, using a 5-point Likert scale (1 = insufficient quality; 5 = excellent quality).

resultsSixty-two patients were included (mean age: 47 ± 17 years, 41 men). No significant differences between DL cine and bSSFP were found for all LV and RV parameters (P ≥ .176). DL cine was significantly faster (1.35 ± .55 m vs 2.83 ± .79 m; P < .001). The agreement between DL cine and bSSFP was strong, with bias ranging from 45 to 1.75% for LV and from - 0.38 to 2.43% for RV. Among LV parameters, the highest agreement was obtained for ESV and SV, which fell within the acceptable limit of agreement (LOA) in 84% of cases. EDV obtained the highest agreement among RV parameters, falling within the acceptable LOA in 90% of cases. Overall image quality was comparable (median: 5, IQR: 4-5; P = .330), while endocardial edge definition of DL cine (median: 4, IQR: 4-5) was lower than bSSFP (median: 5, IQR: 4-5; P = .002).

conclusionDL cine allows fast and accurate quantification of LV and RV parameters and comparable image quality with conventional bSSFP.

Indexed as

Deep LearningHeart VentriclesMagnetic Resonance Imaging, CineAdultAgedFemaleHumansImage Interpretation, Computer-AssistedMaleMiddle AgedProspective StudiesStroke VolumeArtificial intelligenceCardiac functionCardiac MRICardiovascular magnetic resonanceDeep learningFast imaging

Identifiers

PMID40381163
PMCPMC12367930

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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.