Evidence map›Paper›PMID 40985603›Full record

ArticleHuman brain mapping2025

AI Model Integrating Imaging and Clinical Data for Predicting CSF Diversion in Neonatal Hydrocephalus: A Preliminary Study.

Yuwei Dai, Zhusi Zhong, Yan Qin, Yuli Wang, Guangdi Yu, Andrew Kobets, David W Swenson, Jerrold L Boxerman, Gang Li, Shenandoah Robinson and 4 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Human brain mapping, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
  4. 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.

Yuwei DaiDepartment of Neurology, Second Xiangya Hospital of Central South University, Changsha, Hunan, China.ORCID 0000-0003-4789-0242
Zhusi ZhongDepartment of Diagnostic Imaging, Brown University Health, Providence, Rhode Island, USA.
Yan QinDepartment of Radiology, Xiangya Hospital of Central South University, Changsha, Hunan, China.
Yuli WangDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA.
Guangdi YuRussell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University, Baltimore, Maryland, USA.
Andrew KobetsDepartment of Neurosurgery, Montefiore Medical Center and the Albert Einstein College of Medicine, New York, USA.
David W SwensonDepartment of Diagnostic Imaging, Brown University Health, Providence, Rhode Island, USA.
Jerrold L BoxermanDepartment of Diagnostic Imaging, Brown University Health, Providence, Rhode Island, USA.
Gang LiDepartment of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
Shenandoah RobinsonDepartment of Neurosurgery, Johns Hopkins University, Baltimore, Maryland, USA.
Harrison BaiRussell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University, Baltimore, Maryland, USA.
Li YangDepartment of Neurology, Second Xiangya Hospital of Central South University, Changsha, Hunan, China.
Weihua LiaoDepartment of Radiology, Xiangya Hospital of Central South University, Changsha, Hunan, China.ORCID 0000-0002-1926-3527
Zhicheng JiaoDepartment of Diagnostic Imaging, Brown University Health, Providence, Rhode Island, USA.

Funding

Translational Research Central ServicesP30CA006973 · NCI · JOHNS HOPKINS UNIVERSITY · PI ALAN KEITH MEEKER · 1985 to 2026
$208.6M
National Institutions of Health/National Cancer Institute Grant P30CA006973NCI NIH HHS P30 CA006973
6 · The paper itself

Abstract

Predictive tools for stratifying neonatal hydrocephalus into low- and high-risk groups for cerebrospinal fluid (CSF) diversion are currently lacking. We developed and validated an artificial intelligence (AI) model that integrates multimodal imaging and clinical data to predict CSF diversion needs. The development cohort included 116 neonates with suspicion of raised intracranial pressure (ICP) from a Chinese tertiary referral hospital (80 with intracranial pressure > 80 mm H

Indexed as

Artificial IntelligenceCerebrospinal Fluid ShuntsHydrocephalusMagnetic Resonance ImagingFemaleHumansInfant, NewbornMaleMeningomyeloceleartificial intelligencecerebrospinal fluid shuntingMRIneonatal hydrocephalusraised intracranial pressure

Identifiers

PMID40985603
PMCPMC12455681

What OpenQuestion holds

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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.