Evidence map›Paper›PMID 37379272›Full record

ArticlePloS one2023

Feasibility and clinical usefulness of deep learning-accelerated MRI for acute painful fracture patients wearing a splint: A prospective comparative study.

Seunghyeon Roh, Jae In Park, Gun Young Kim, Hye Jin Yoo, Dominik Nickel, Gregor Koerzdoerfer, JaeKon Sung, Jiseon Oh, Hee Dong Chae, Sung Hwan Hong and 1 more

Abstract read
In one paragraph

Article in PloS one, 2023. 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

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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

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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.

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4 · The record

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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.

Seunghyeon RohDepartment of Radiology, Seoul National University Hospital, Seoul, Republic of Korea.
Jae In ParkDepartment of Radiology, Seoul National University Hospital, Seoul, Republic of Korea.
Gun Young KimDepartment of Radiology, Seoul National University Hospital, Seoul, Republic of Korea.
Hye Jin YooDepartment of Radiology, Seoul National University Hospital, Seoul, Republic of Korea.
Dominik NickelSiemens Healthcare GmbH, Erlangen, Germany.
Gregor KoerzdoerferSiemens Healthcare GmbH, Erlangen, Germany.
JaeKon SungSiemens Healthineers Ltd, Seoul, Republic of Korea.
Jiseon OhDepartment of Radiology, Seoul National University Hospital, Seoul, Republic of Korea.
Hee Dong ChaeDepartment of Radiology, Seoul National University Hospital, Seoul, Republic of Korea.
Sung Hwan HongDepartment of Radiology, Seoul National University Hospital, Seoul, Republic of Korea.
Ja-Young ChoiDepartment of Radiology, Seoul National University Hospital, Seoul, Republic of Korea.ORCID 0000-0002-3363-0629

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo evaluate the feasibility and clinical usefulness of deep learning (DL)-accelerated turbo spin echo (TSEDL) sequences relative to standard TSE sequences (TSES) for acute radius fracture patients wearing a splint.

methodsThis prospective consecutive study investigated 50 patients' preoperative wrist MRI scans acquired between July 2021 and January 2022. Examinations were performed at 3 Tesla MRI with body array coils due to the wrist splint. Besides TSES obtained according to the routine protocol, TSEDL sequences for axial T2-, coronal T1-, and coronal PD-weighted TSE sequences were scanned for comparison. For quantitative assessment, the relative signal-to-noise ratio (rSNR), the relative contrast-to-noise ratio (rCNR), and the relative contrast ratio (rCR) were measured. For qualitative assessment, all images were assessed by two independent musculoskeletal radiologists in terms of perceived SNR, image contrast, image sharpness, artifacts disturbing evaluation, overall image quality and diagnostic confidence for injuries using a four- or five-point Likert scale.

resultsThe scan time was shortened approximately by a factor of two for TSEDL compared to TSES. TSEDL images showed significantly better rSNR, rCNR, and rCR values for all sequences, and scored significantly better in terms of both image quality and diagnostic confidence for both readers than TSES images (all p < .05). Interrater reliabilities were in almost perfect agreement.

conclusionThe DL-accelerated technique proved to be very helpful not only to reduce scan time but also to improve image quality for acute painful fracture patients wearing a splint despite using body array coils instead of a wrist-specific coil. Based on our study, the DL-accelerated technique can be very useful for MRI of any part of the extremities in trauma settings just with body array coils.

Indexed as

Deep LearningRadius FracturesArtifactsFeasibility StudiesHumansMagnetic Resonance ImagingProspective StudiesSplints

Identifiers

PMID37379272
PMCPMC10306199

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