Evidence map›Paper›PMID 34413061›Full record

ArticleAJNR. American journal of neuroradiology2021

Automating Quantitative Measures of an Established Conventional MRI Scoring System for Preterm-Born Infants Scanned between 29 and 47 Weeks' Postmenstrual Age.

L van Eijk, M Seidel, K Pannek, J M George, S Fiori, A Guzzetta, A Coulthard, J Bursle, R S Ware, D Bradford and 4 more

Open access · hybridAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in AJNR. American journal of neuroradiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact, top 89% 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.

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

1 citing paper in PubMed, 0 citations in OpenAlex.

  1. Article
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

14 authors at 6 institutions in 2 countries.

L van EijkFrom The Australian e-Health Research Centre (L.v.E., M.S., K.P., D.B., S.R., J.F.), Health and Biosecurity, Commonwealth Scientific and Industrial Research Organisation, Brisbane, Australia.ORCID 0000-0001-8254-1019
M SeidelFrom The Australian e-Health Research Centre (L.v.E., M.S., K.P., D.B., S.R., J.F.), Health and Biosecurity, Commonwealth Scientific and Industrial Research Organisation, Brisbane, Australia.ORCID 0000-0002-6416-1861
K PannekFrom The Australian e-Health Research Centre (L.v.E., M.S., K.P., D.B., S.R., J.F.), Health and Biosecurity, Commonwealth Scientific and Industrial Research Organisation, Brisbane, Australia Kerstin.Pannek@csiro.au.ORCID 0000-0002-6512-7630
J M GeorgeQueensland Cerebral Palsy and Rehabilitation Research Centre (J.M.G., R.N.B.), Centre for Children's Health Research, The University of Queensland, Brisbane, Australia.ORCID 0000-0003-4893-6564
S FioriDepartment of Developmental Neuroscience (S.F., A.G.), Istituto di Ricovero e Cura a Carattere Scientifico Stella Maris, Pisa, Italy.ORCID 0000-0002-0933-6120
A GuzzettaDepartment of Developmental Neuroscience (S.F., A.G.), Istituto di Ricovero e Cura a Carattere Scientifico Stella Maris, Pisa, Italy.ORCID 0000-0003-3868-0059
A CoulthardDepartment of Medical Imaging (A.C., J.B.), Royal Brisbane and Women's Hospital, Brisbane, Australia.ORCID 0000-0003-1110-9207
J BursleDepartment of Medical Imaging (A.C., J.B.), Royal Brisbane and Women's Hospital, Brisbane, Australia.ORCID 0000-0002-7233-104X
R S WareMenzies Health Institute Queensland (R.S.W.), Griffith University, Brisbane, Australia.ORCID 0000-0002-6129-6736
D BradfordFrom The Australian e-Health Research Centre (L.v.E., M.S., K.P., D.B., S.R., J.F.), Health and Biosecurity, Commonwealth Scientific and Industrial Research Organisation, Brisbane, Australia.ORCID 0000-0002-2939-3018
S RoseFrom The Australian e-Health Research Centre (L.v.E., M.S., K.P., D.B., S.R., J.F.), Health and Biosecurity, Commonwealth Scientific and Industrial Research Organisation, Brisbane, Australia.ORCID 0000-0003-1558-6297
P B ColditzPerinatal Research Centre (P.B.C.), University of Queenland Centre for Clinical Research, Faculty of Medicine, The University of Queensland, Brisbane, Australia.ORCID 0000-0002-6408-8238
R N BoydQueensland Cerebral Palsy and Rehabilitation Research Centre (J.M.G., R.N.B.), Centre for Children's Health Research, The University of Queensland, Brisbane, Australia.ORCID 0000-0002-4919-5975
J FrippFrom The Australian e-Health Research Centre (L.v.E., M.S., K.P., D.B., S.R., J.F.), Health and Biosecurity, Commonwealth Scientific and Industrial Research Organisation, Brisbane, Australia.ORCID 0000-0001-9705-0079
Commonwealth Scientific and Industrial Research Organisation · AUThe University of Queensland · AUFondazione Stella Maris · ITGriffith University · AURoyal Brisbane and Women's Hospital · AUUniversity of Pisa · IT

Funding

European Research Council 319456
6 · The paper itself

Abstract

background and purposeConventional MR imaging scoring is a valuable tool for risk stratification and prognostication of outcomes, but manual scoring is time-consuming, operator-dependent, and requires high-level expertise. This study aimed to automate the regional measurements of an established brain MR imaging scoring system for preterm neonates scanned between 29 and 47 weeks' postmenstrual age. MATERIALS AND

methodsThis study used T2WI from the longitudinal Prediction of PREterm Motor Outcomes cohort study and the developing Human Connectome Project. Measures of biparietal width, interhemispheric distance, callosal thickness, transcerebellar diameter, lateral ventricular diameter, and deep gray matter area were extracted manually (Prediction of PREterm Motor Outcomes study only) and automatically. Scans with poor quality, failure of automated analysis, or severe pathology were excluded. Agreement, reliability, and associations between manual and automated measures were assessed and compared against statistics for manual measures. Associations between measures with postmenstrual age, gestational age at birth, and birth weight were examined (Pearson correlation) in both cohorts.

resultsA total of 652 MRIs (86%) were suitable for analysis. Automated measures showed good-to-excellent agreement and good reliability with manual measures, except for interhemispheric distance at early MR imaging (scanned between 29 and 35 weeks, postmenstrual age; in line with poor manual reliability) and callosal thickness measures. All measures were positively associated with postmenstrual age (

conclusionsMeasures of brain injury and impaired brain growth can be automatically extracted from neonatal MR imaging, which could assist with clinical reporting.

Indexed as

Infant, PrematureMagnetic Resonance ImagingBrainCohort StudiesHumansInfantInfant, NewbornReproducibility of Results

Identifiers

PMID34413061
PMCPMC8562751
OpenAlexW3187547863

What OpenQuestion holds

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Registered trials

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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.