Evidence map›Paper›PMID 39398280›Full record

ArticleProceedings. IEEE International Symposium on Biomedical Imaging2024

UNSUPERVISED AIRWAY TREE CLUSTERING WITH DEEP LEARNING: THE MULTI-ETHNIC STUDY OF ATHEROSCLEROSIS (MESA) LUNG STUDY.

Sneha N Naik, Elsa D Angelini, R Graham Barr, Norrina Allen, Alain Bertoni, Eric A Hoffman, Ani Manichaikul, Jim Pankow, Wendy Post, Yifei Sun and 3 more

Abstract read
In one paragraph

Article in Proceedings. IEEE International Symposium on Biomedical Imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

13 authors.

Sneha N NaikColumbia University, USA.
Elsa D AngeliniColumbia University, USA.
R Graham BarrColumbia University, USA.
Norrina AllenNorthwestern University, USA.
Alain BertoniWake Forest University, USA.
Eric A HoffmanUniversity of Iowa, USA.
Ani ManichaikulUniversity of Virginia, USA.
Jim PankowUniversity of Minnesota, USA.
Wendy PostJohns Hopkins University, USA.
Yifei SunColumbia University, USA.
Karol WatsonUniversity of California, USA.
Benjamin M SmithMcGill University, Canada.
Andrew F LaineColumbia University, USA.

Funding

Institute for Clinical and Translational ResearchUL1TR001079 · NCATS · JOHNS HOPKINS UNIVERSITY · PI FORD, DANIEL ERNEST · 2013 to 2017
$60.1M
Pulmonary Toxicology Facility CoreP30ES005605 · NIEHS · UNIVERSITY OF IOWA · PI Jong Sung Kim · 1990 to 2026
$40.5M
Wake Forest Clinical and Translational Science AwardUL1TR001420 · NCATS · WAKE FOREST UNIVERSITY HEALTH SCIENCES · PI ARD, JAMY D, FOLEY, KRISTIE L · 2015 to 2023
$32.3M
Clinical and Translational Science AwardUL1TR000040 · NCATS · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI GINSBERG, HENRY N · 2012 to 2015
$26.2M
Pulmonary microvascular perfusion in the Multi-Ethnic Study of AtherosclerosisR01HL077612 · NHLBI · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI R Graham BARR · 2004 to 2026
$18.8M
Pulmonary Vascular Changes in Early Chronic Obstructive Pulmonary Disease (COPD)R01HL093081 · NHLBI · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI R Graham BARR · 2008 to 2026
$17.2M
Task Area A Core Study Operations.Task Area A shall encompass annual follow-up of cohort members, clinical endpoints ascertainment, study coordination activities, maintenance of the database and biosp75N92020D00001 · NHLBI · UNIVERSITY OF WASHINGTON · PI MCCLELLAND, ROBYN LEAGH · 2020 to 2025
$17.2M
Chronic obstructive pulmonary disease in non-smokersR01HL130506 · NHLBI · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI HERBSTMAN, JULIE BETH, SMITH, BENJAMIN M. · 2016 to 2025
$6.3M
Novel Quantitative Emphysema Subtypes in MESA and SPIROMICSR01HL121270 · NHLBI · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI BARR, R GRAHAM, LAINE, ANDREW FRANCIS · 2014 to 2022
$5.6M
Task Area A shall encompass annual follow-up of cohort members, clinical events investigations, study operations, and data analysis and manuscript writing. If implemented, Task A.1 will provide fundin75N92020D00005 · NHLBI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI WATSON, KAROL E · 2020 to 2025
$5.1M
TO EXERCISE OPTION PERIOD ONE (1) FOR TASK AREA A - MESA CORE OPERATIONS, FIELD CENTER.75N92020D00004 · NHLBI · NORTHWESTERN UNIVERSITY · PI SIEGEL, JONATHAN H · 2020 to 2025
$4.5M
Task Area A shall encompass annual follow-up of cohort members, clinical events investigations, study operations, and data analysis and manuscript writing. If implemented, Task A.1 will provide fundin75N92020D00006 · NHLBI · UNIVERSITY OF MINNESOTA · PI PANKOW, JAMES S · 2020 to 2025
$4.4M
NCATS NIH HHS UL1 TR000040NCATS NIH HHS UL1 TR001079NCATS NIH HHS UL1 TR001420NHLBI NIH HHS 75N92020D00001NHLBI NIH HHS 75N92020D00002NHLBI NIH HHS 75N92020D00003NHLBI NIH HHS 75N92020D00004NHLBI NIH HHS 75N92020D00005NHLBI NIH HHS 75N92020D00006NHLBI NIH HHS 75N92020D00007NHLBI NIH HHS HHSN268201500003CNHLBI NIH HHS HHSN268201500003INHLBI NIH HHS N01 HC095159NHLBI NIH HHS N01 HC095160NHLBI NIH HHS N01 HC095161NHLBI NIH HHS N01 HC095162NHLBI NIH HHS N01 HC095163NHLBI NIH HHS N01 HC095164NHLBI NIH HHS N01 HC095165NHLBI NIH HHS N01 HC095166NHLBI NIH HHS N01 HC095167NHLBI NIH HHS N01 HC095168NHLBI NIH HHS N01 HC095169NHLBI NIH HHS R01 HL077612NHLBI NIH HHS R01 HL093081NHLBI NIH HHS R01 HL121270NHLBI NIH HHS R01 HL130506NHLBI NIH HHS R01 HL155816NIEHS NIH HHS P30 ES005605
6 · The paper itself

Abstract

High-resolution full lung CT scans now enable the detailed segmentation of airway trees up to the 6th branching generation. The airway binary masks display very complex tree structures that may encode biological information relevant to disease risk and yet remain challenging to exploit via traditional methods such as meshing or skeletonization. Recent clinical studies suggest that some variations in shape patterns and caliber of the human airway tree are highly associated with adverse health outcomes, including all-cause mortality and incident COPD. However, quantitative characterization of variations observed on CT segmented airway tree remain incomplete, as does our understanding of the clinical and developmental implications of such. In this work, we present an unsupervised deep-learning pipeline for feature extraction and clustering of human airway trees, learned directly from projections of 3D airway segmentations. We identify four reproducible and clinically distinct airway sub-types in the MESA Lung CT cohort.

Indexed as

Airway structureCommunity DetectionDeep LearningLung CT

Identifiers

PMID39398280
PMCPMC11467912

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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.