Evidence map›Paper›PMID 39112675›Full record

ArticleSkeletal radiology2025

Deep learning MR reconstruction in knees and ankles in children and young adults. Is it ready for clinical use?

M Alejandra Bedoya, Jade Iwasaka-Neder, Andy Tsai, Patrick R Johnston, Gregor Körzdörfer, Dominik Nickel, Peter Kollasch, Sarah D Bixby

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Article in Skeletal radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed
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1 · What the graph read from it

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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

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3 · Its place in the literature

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2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

M Alejandra BedoyaDepartment of Radiology, Boston Children's Hospital, 300 Longwood Ave, Boston, MA, 02115, USA.
Jade Iwasaka-NederDepartment of Radiology, Boston Children's Hospital, 300 Longwood Ave, Boston, MA, 02115, USA. jade.iwasaka-neder@childrens.harvard.edu.ORCID http://orcid.org/0000-0002-4727-0134
Andy TsaiDepartment of Radiology, Boston Children's Hospital, 300 Longwood Ave, Boston, MA, 02115, USA.
Patrick R JohnstonDepartment of Radiology, Boston Children's Hospital, 300 Longwood Ave, Boston, MA, 02115, USA.
Gregor KörzdörferSiemens Medical Solutions USA, Inc, 40 Liberty Boulevard, Malvern, PA, 19355, USA.
Dominik NickelSiemens Healthineers AG, Erlangen, Germany.
Peter KollaschSiemens Medical Solutions USA, Inc, 40 Liberty Boulevard, Malvern, PA, 19355, USA.
Sarah D BixbyDepartment of Radiology, Boston Children's Hospital, 300 Longwood Ave, Boston, MA, 02115, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo evaluate the diagnostic performance and image quality of accelerated Turbo Spin Echo sequences using deep-learning (DL) reconstructions compared to conventional sequences in knee and ankle MRIs of children and young adults. MATERIALS AND

methodsIRB-approved prospective study consisting of 49 MRIs from 48 subjects (10 males, mean age 16.4 years, range 7-29 years), with each MRI consisting of both conventional and DL sequences. Sequences were evaluated blindly to determine predictive values, sensitivity, and specificity of DL sequences using conventional sequences and knee arthroscopy (if available) as references. Physeal patency and appearance were evaluated. Qualitative parameters were compared. Presence of undesired image alterations was assessed.

resultsThe prevalence of abnormal findings in the knees and ankles were 11.7% (75/640), and 11.5% (19/165), respectively. Using conventional sequences as reference, sensitivity and specificity of DL sequences in knees were 90.7% and 99.3%, and in ankles were 100.0% and 100.0%. Using arthroscopy as reference, sensitivity and specificity of DL sequences were 80.0% and 95.8%, and of conventional sequences were 80.0% and 97.9%. Agreement of physeal status was 100.0%. DL sequences were qualitatively "same-or-better" compared to conventional (p < 0.032), except for pixelation artifact for the PDFS sequence (p = 0.233). No discrete image alteration was identified in the knee DL sequences. In the ankle, we identified one DL artifact involving a tendon (0.8%, 1/125). DL sequences were faster than conventional sequences by a factor of 2 (p < 0.001).

conclusionIn knee and ankle MRIs, DL sequences provided similar diagnostic performance and "same-or-better" image quality than conventional sequences at half the acquisition time.

Indexed as

Ankle JointDeep LearningImage Interpretation, Computer-AssistedKnee JointMagnetic Resonance ImagingAdolescentAdultChildFemaleHumansMaleProspective StudiesSensitivity and SpecificityYoung AdultDeep learningImage qualityMRIMusculoskeletal

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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.