Evidence map›Paper›PMID 31275101›Full record

ArticleFrontiers in neuroscience2019

Objective and Automated Detection of Diffuse White Matter Abnormality in Preterm Infants Using Deep Convolutional Neural Networks.

Hailong Li, Nehal A Parikh, Jinghua Wang, Stephanie Merhar, Ming Chen, Milan Parikh, Scott Holland, Lili He

Abstract read
In one paragraph

Article in Frontiers in neuroscience, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
–field-weighted citation impact
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

9 citing papers in PubMed.

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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

8 authors.

Hailong LiThe Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States.
Nehal A ParikhThe Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States.
Jinghua WangDepartment of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, United States.
Stephanie MerharThe Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States.
Ming ChenThe Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States.
Milan ParikhThe Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States.
Scott HollandMedpace Inc., Cincinnati, OH, United States.
Lili HeThe Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States.

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

Diffuse white matter abnormality (DWMA), or diffuse excessive high signal intensity is observed in 50-80% of very preterm infants at term-equivalent age. It is subjectively defined as higher than normal signal intensity in periventricular and subcortical white matter in comparison to normal unmyelinated white matter on T

Indexed as

convolutional neural networksdeep learningdiffuse white matter abnormalityMRIvery preterm infants

Identifiers

PMID31275101
PMCPMC6591530

What OpenQuestion holds

Textmetadata
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Read underepoch 390

Registered trials

None linked

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.