Evidence map›Paper›PMID 34142089›Full record

ArticleRadiology. Artificial intelligence2021

Automatic Segmentation of Diffuse White Matter Abnormality on T2-weighted Brain MR Images Using Deep Learning in Very Preterm Infants.

Hailong Li, Ming Chen, Jinghua Wang, Venkata Sita Priyanka Illapani, Nehal A Parikh, Lili He

Registry-linked trialOpen access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Radiology. Artificial intelligence, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03345069 (Early Prediction of Cognitive and Motor Deficits Using Advanced MRI in Very Preterm Infants), which is not on this map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
3.0field-weighted citation impact, top 10% of its field
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.

NCT03345069 active not recruitingnot on this map

Early Prediction of Cognitive and Motor Deficits Using Advanced MRI in Very Preterm Infants

TypeobservationalSponsorChildren's Hospital Medical Center, CincinnatiRan2016 to 2027Enrolled393ConditionsPremature Infant
3 · Its place in the literature

Who cites it

11 citing papers in PubMed, 13 citations in OpenAlex.

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  5. Improved robustness for deep learning-based segmentation of multi-center myocardial perfusion cardiovascular MRI datasets using data-adaptive uncertainty-guided space-time analysis.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2024
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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

6 authors at 1 institution in 1 country.

Hailong LiImaging Research Center, Department of Radiology (H.L., L.H.), and Perinatal Institute (H.L., V.S.P.I., N.A.P., L.H.), Cincinnati Children's Hospital Medical Center, 3333 Burnet Ave, MLC 5033, Cincinnati, OH 45229; Department of Electronic Engineering and Computing Systems, University of Cincinnati, Cincinnati, Ohio (M.C.); Deep MRI Imaging, Lewes, Del (J.W.); and Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio (N.A.P., L.H.).ORCID https://orcid.org/0000-0002-5267-2875
Ming ChenImaging Research Center, Department of Radiology (H.L., L.H.), and Perinatal Institute (H.L., V.S.P.I., N.A.P., L.H.), Cincinnati Children's Hospital Medical Center, 3333 Burnet Ave, MLC 5033, Cincinnati, OH 45229; Department of Electronic Engineering and Computing Systems, University of Cincinnati, Cincinnati, Ohio (M.C.); Deep MRI Imaging, Lewes, Del (J.W.); and Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio (N.A.P., L.H.).
Jinghua WangImaging Research Center, Department of Radiology (H.L., L.H.), and Perinatal Institute (H.L., V.S.P.I., N.A.P., L.H.), Cincinnati Children's Hospital Medical Center, 3333 Burnet Ave, MLC 5033, Cincinnati, OH 45229; Department of Electronic Engineering and Computing Systems, University of Cincinnati, Cincinnati, Ohio (M.C.); Deep MRI Imaging, Lewes, Del (J.W.); and Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio (N.A.P., L.H.).ORCID https://orcid.org/0000-0002-9237-4418
Venkata Sita Priyanka IllapaniImaging Research Center, Department of Radiology (H.L., L.H.), and Perinatal Institute (H.L., V.S.P.I., N.A.P., L.H.), Cincinnati Children's Hospital Medical Center, 3333 Burnet Ave, MLC 5033, Cincinnati, OH 45229; Department of Electronic Engineering and Computing Systems, University of Cincinnati, Cincinnati, Ohio (M.C.); Deep MRI Imaging, Lewes, Del (J.W.); and Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio (N.A.P., L.H.).ORCID https://orcid.org/0000-0002-6186-3286
Nehal A ParikhImaging Research Center, Department of Radiology (H.L., L.H.), and Perinatal Institute (H.L., V.S.P.I., N.A.P., L.H.), Cincinnati Children's Hospital Medical Center, 3333 Burnet Ave, MLC 5033, Cincinnati, OH 45229; Department of Electronic Engineering and Computing Systems, University of Cincinnati, Cincinnati, Ohio (M.C.); Deep MRI Imaging, Lewes, Del (J.W.); and Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio (N.A.P., L.H.).ORCID https://orcid.org/0000-0002-1375-1247
Lili HeImaging Research Center, Department of Radiology (H.L., L.H.), and Perinatal Institute (H.L., V.S.P.I., N.A.P., L.H.), Cincinnati Children's Hospital Medical Center, 3333 Burnet Ave, MLC 5033, Cincinnati, OH 45229; Department of Electronic Engineering and Computing Systems, University of Cincinnati, Cincinnati, Ohio (M.C.); Deep MRI Imaging, Lewes, Del (J.W.); and Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio (N.A.P., L.H.).
Cincinnati Children's Hospital Medical Center · US

Funding

A New Model to Identify Preterm Neonates at High-Risk for Cognitive Impairments and School ReadinessR01NS094200 · NINDS · CINCINNATI CHILDRENS HOSP MED CTR · PI NEHAL A. PARIKH · 2016 to 2026
$6.7M
Early Prediction of Cerebral Palsy in Premature Infants using Advanced MRI BiomarkersR01NS096037 · NINDS · CINCINNATI CHILDRENS HOSP MED CTR · PI PARIKH, NEHAL A. · 2016 to 2020
$2.2M
MRI and Deep Learning for Early Prediction of Neurodevelopmental Deficits in Very Preterm InfantsR01EB029944 · NIBIB · CINCINNATI CHILDRENS HOSP MED CTR · PI HE, LILI · 2020 to 2023
$2.1M
Early Prediction of Cognitive Deficits in Very Preterm Infants using Machine Learning and Brain ConnectomeR21HD094085 · NICHD · CINCINNATI CHILDRENS HOSP MED CTR · PI HE, LILI · 2018 to 2019
$437k
NIBIB NIH HHS R01 EB029944NICHD NIH HHS R21 HD094085NINDS NIH HHS R01 NS094200NINDS NIH HHS R01 NS096037
6 · The paper itself

Abstract

About 50%-80% of very preterm infants (VPIs) (≤ 32 weeks gestational age) exhibit diffuse white matter abnormality (DWMA) on their MR images at term-equivalent age. It remains unknown if DWMA is associated with developmental impairments, and further study is warranted. To aid in the assessment of DWMA, a deep learning model for DWMA quantification on T2-weighted MR images was developed. This secondary analysis of prospective data was performed with an internal cohort of 98 VPIs (data collected from December 2014 to April 2016) and an external cohort of 28 VPIs (data collected from January 2012 to August 2014) who had already undergone MRI at term-equivalent age. Ground truth DWMA regions were manually annotated by two human experts with the guidance of a prior published semiautomated algorithm. In a twofold cross-validation experiment using the internal cohort of 98 infants, the three-dimensional (3D) ResU-Net model accurately segmented DWMA with a Dice similarity coefficient of 0.907 ± 0.041 (standard deviation) and balanced accuracy of 96.0% ± 2.1, outperforming multiple peer deep learning models. The 3D ResU-Net model that was trained with the whole internal cohort (

Identifiers

PMID34142089
PMCPMC8166113
OpenAlexW3127069665

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

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.