Evidence map›Paper›PMID 42249942›Full record

ArticlePediatric radiology2026

Deep learning reconstruction improves appendix visualization on pediatric magnetic resonance imaging (MRI): a single-center experience.

Sebastian Gallo-Bernal, Valeria Pena-Trujillo, Navaira Shoaib, Fedel Machado-Rivas, Teresa Victoria, Rimaz Khadir, Eugene Milshteyn, Arnaud Guidon, John Conklin, Michael S Gee

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Article in Pediatric radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

10 authors.

Sebastian Gallo-BernalMassachusetts General Hospital/Mass General Brigham, Boston, MA, USA. sgallobernal@mgh.harvard.edu.
Valeria Pena-TrujilloMassachusetts General Hospital/Mass General Brigham, Boston, MA, USA.
Navaira ShoaibMassachusetts General Hospital/Mass General Brigham, Boston, MA, USA.
Fedel Machado-RivasMassachusetts General Hospital/Mass General Brigham, Boston, MA, USA.
Teresa VictoriaMassachusetts General Hospital/Mass General Brigham, Boston, MA, USA.
Rimaz KhadirMassachusetts General Hospital/Mass General Brigham, Boston, MA, USA.
Eugene MilshteynGeneral Electric Healthcare, Chicago, IL, USA.
Arnaud GuidonGeneral Electric Healthcare, Chicago, IL, USA.
John ConklinMassachusetts General Hospital/Mass General Brigham, Boston, MA, USA.
Michael S GeeMassachusetts General Hospital/Mass General Brigham, Boston, MA, USA. MSGEE@MGH.HARVARD.EDU.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThin-slice MRI may improve appendix visualization in children with suspected appendicitis, but reducing slice thickness decreases signal-to-noise ratio (SNR) and can degrade image quality. Deep learning reconstruction (DLR) enables denoising of thin-slice images and may mitigate these limitations.

objectiveTo evaluate whether 2-mm T2-weighted magnetic resonance imaging (MRI) with DLR improves appendix visualization and diagnostic performance compared with conventional 4-mm MRI in pediatric appendicitis. MATERIALS AND

methodsIn this prospective, Institutional Review Board-approved, single-center study (January 2022-June 2025), 82 pediatric patients (mean age 9.6 years; age range 5-20 years) undergoing MRI for suspected appendicitis were included. MRI protocols contained both standard 4-mm and thin-slice 2-mm single-shot T2-weighted images; DLR was applied to 2-mm images (2-mm+DLR). Three blinded radiologists independently assessed paired series for appendix visualization, confidence, appendicitis diagnosis, ancillary findings, and overall image quality. Objective SNR was measured in psoas muscle, bladder, and fat. Diagnostic performance was calculated against surgical or clinical reference standards using paired statistical tests.

resultsIn the first 16 exploratory cases, quantitative analysis showed that the signal-to-noise ratio of 2-mm+DLR images was comparable to that of standard 4-mm images. Across the full cohort, pooled analysis showed improved appendix visualization with 2-mm+DLR compared with standard 4-mm MRI (88.2% [217/246] vs 83.3% [205/246], P=0.006). Overall diagnostic image quality was also higher with 2-mm+DLR (median 5 vs 4, P<0.001), while confidence in appendix visualization and appendicitis diagnosis was similar between techniques (both medians 4, P>0.1). Exploratory pooled diagnostic performance numerically favored 2-mm+DLR, with higher sensitivity (89% [78/87] vs 81% [71/87]) and accuracy (89% [218/246] vs 86% [210/246]), although paired comparisons did not reach statistical significance (Table 4).

conclusionDLR-enhanced 2-mm MRI improves appendix visualization and overall diagnostic image quality in pediatric appendicitis without reducing reader confidence or specificity. Thin-slice DLR MRI supports improved depiction of the appendix and alternative abdominal pathology in children.

Indexed as

AppendicitisArtificial intelligenceDeep learningMagnetic resonance imagingPediatricsSignal-to-noise ratio

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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.