Evidence map›Paper›PMID 40400533›Full record

ArticleEuropean heart journal. Imaging methods and practice2025

Continuous three-dimensional transesophageal echocardiography and deep learning for perioperative monitoring of left ventricular longitudinal function.

Jinyang Yu, Anders Austlid Taskén, Erik Andreas Rye Berg, Tomas Dybos Tannvik, Katrine Hordnes Slagsvold, Idar Kirkeby-Garstad, Bjørnar Grenne, Gabriel Kiss, Svend Aakhus

Abstract read
In one paragraph

Article in European heart journal. Imaging methods and practice, 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

9 authors.

Jinyang YuDepartment of Circulation and Medical Imaging, Fakultet for medisin og helsevitenskap, Norwegian University of Science and Technology, Postboks 8905, 7491 Trondheim, Norway.ORCID https://orcid.org/0000-0001-5090-4102
Anders Austlid TaskénDepartment of Computer Science, Norwegian University of Science and Technology, Trondheim, Norway.ORCID https://orcid.org/0000-0002-5618-5556
Erik Andreas Rye BergDepartment of Circulation and Medical Imaging, Fakultet for medisin og helsevitenskap, Norwegian University of Science and Technology, Postboks 8905, 7491 Trondheim, Norway.ORCID https://orcid.org/0000-0001-6658-7594
Tomas Dybos TannvikDepartment of Anesthesia and Intensive Care, St. Olav's Hospital, Trondheim University Hospital, Prinsesse Kristinas gate 3, 7030 Trondheim, Norway.ORCID https://orcid.org/0000-0003-3755-6312
Katrine Hordnes SlagsvoldDepartment of Circulation and Medical Imaging, Fakultet for medisin og helsevitenskap, Norwegian University of Science and Technology, Postboks 8905, 7491 Trondheim, Norway.ORCID https://orcid.org/0000-0001-9163-6298
Idar Kirkeby-GarstadDepartment of Circulation and Medical Imaging, Fakultet for medisin og helsevitenskap, Norwegian University of Science and Technology, Postboks 8905, 7491 Trondheim, Norway.ORCID https://orcid.org/0000-0002-2941-6187
Bjørnar GrenneDepartment of Circulation and Medical Imaging, Fakultet for medisin og helsevitenskap, Norwegian University of Science and Technology, Postboks 8905, 7491 Trondheim, Norway.ORCID https://orcid.org/0000-0002-2984-6865
Gabriel KissDepartment of Computer Science, Norwegian University of Science and Technology, Trondheim, Norway.ORCID https://orcid.org/0000-0001-5024-1548
Svend AakhusDepartment of Circulation and Medical Imaging, Fakultet for medisin og helsevitenskap, Norwegian University of Science and Technology, Postboks 8905, 7491 Trondheim, Norway.ORCID https://orcid.org/0000-0002-0840-4013

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Aims: Continuous monitoring of left ventricular (LV) function may improve cardiopulmonary management. Therefore, we have developed Methods and results: This prospective observational study included 50 adult intensive care patients after cardiac surgery. Single-beat full-volume 3D recordings were obtained every 5 min over a 120-min period using a hands-free method that simulated continuous monitoring with transesophageal echocardiography. Precision and agreement with manual MAPSE were determined by a test-retest study design during hemodynamic stability. Our results show that continuous monitoring by 3D autoMAPSE had excellent feasibility (99%). It analysed 10 cycles instantaneously (55 ± 15 s) with high precision (least significant change 1.6 mm). 3D autoMAPSE had adequate agreement with manual MAPSE (bias -1.4 mm, limits of agreement -4.0 to 1.2 mm). Continuous 3D autoMAPSE was associated with both N-terminal pro B-type natriuretic peptide ( Conclusion: 3D autoMAPSE provided continuous monitoring of LV function in perioperative patients by obtaining highly feasible and precise measurements that agree with manual echocardiography and postoperative biomarkers. Thus, continuous 3D autoMAPSE may be an attractive complement to hemodynamic monitoring that can aid cardiopulmonary management.

Indexed as

3D transesophageal echocardiographyautoMAPSEdeep learninghemodynamic monitoringLV functionMAPSE

Identifiers

PMID40400533
PMCPMC12092336

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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.