Evidence map›Paper›PMID 38465203›Full record

ArticleImaging neuroscience (Cambridge, Mass.)2024

Precise and Rapid Whole-Head Segmentation from Magnetic Resonance Images of Older Adults using Deep Learning.

Skylar E Stolte, Aprinda Indahlastari, Jason Chen, Alejandro Albizu, Ayden Dunn, Samantha Pedersen, Kyle B See, Adam J Woods, Ruogu Fang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Imaging neuroscience (Cambridge, Mass.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
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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

9 authors.

Skylar E StolteJ. Crayton Pruitt Family Department of Biomedical Engineering, Herbert Wertheim College of Engineering, University of Florida, Gainesville, USA.
Aprinda IndahlastariCenter for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, USA.
Jason ChenDepartment Of Computer & Information Science & Engineering, Herbert Wertheim College of Engineering, University of Florida, Gainesville, USA.
Alejandro AlbizuCenter for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, USA.
Ayden DunnDepartment of Clinical and Health Psychology, College of Public Health and Health Professions, University of Florida, Gainesville, USA.
Samantha PedersenDepartment of Clinical and Health Psychology, College of Public Health and Health Professions, University of Florida, Gainesville, USA.
Kyle B SeeJ. Crayton Pruitt Family Department of Biomedical Engineering, Herbert Wertheim College of Engineering, University of Florida, Gainesville, USA.
Adam J WoodsCenter for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, USA.
Ruogu FangJ. Crayton Pruitt Family Department of Biomedical Engineering, Herbert Wertheim College of Engineering, University of Florida, Gainesville, USA.

Funding

Augmenting Cognitive Training in Older Adults - The ACT GrantR01AG054077 · NIA · UNIVERSITY OF FLORIDA · PI COHEN, RONALD A, MARSISKE, MICHAEL · 2016 to 2020
$6.9M
Mechanisms, response heterogeneity and dosing from MRI-derived electric field models in tDCS augmented cognitive training: a secondary data analysis of the ACT studyRF1AG071469 · NIA · UNIVERSITY OF FLORIDA · PI FANG, RUOGU, WOODS, ADAM J. · 2021 to 2021
$2.2M
NIA NIH HHS R01 AG054077NIA NIH HHS RF1 AG071469
6 · The paper itself

Abstract

Whole-head segmentation from Magnetic Resonance Images (MRI) establishes the foundation for individualized computational models using finite element method (FEM). This foundation paves the path for computer-aided solutions in fields, particularly in non-invasive brain stimulation. Most current automatic head segmentation tools are developed using healthy young adults. Thus, they may neglect the older population that is more prone to age-related structural decline such as brain atrophy. In this work, we present a new deep learning method called GRACE, which stands for

Indexed as

Artificial IntelligenceDeep learningMRINon-invasive brain stimulationWhole-head segmentation

Identifiers

PMID38465203
PMCPMC10922731

What Socratic holds

Textmetadata
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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 Socratic graph.