Evidence map›Paper›PMID 40617705›Full record

ArticleAcademic radiology2025

Disease Classification of Pulmonary Xenon Ventilation MRI Using Artificial Intelligence.

Alexander M Matheson, Abdullah S Bdaiwi, Matthew M Willmering, Erik B Hysinger, Francis X McCormack, Laura L Walkup, Zackary I Cleveland, Jason C Woods

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Academic radiology, 2025. 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
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.

  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

8 authors.

Alexander M MathesonCenter for Pulmonary Imaging Research, Division of Pulmonary Medicine, Cincinnati, Ohio (A.M.M., A.S.B., M.M.W., E.B.H., L.L.W., Z.I.C., J.C.W.).
Abdullah S BdaiwiCenter for Pulmonary Imaging Research, Division of Pulmonary Medicine, Cincinnati, Ohio (A.M.M., A.S.B., M.M.W., E.B.H., L.L.W., Z.I.C., J.C.W.).
Matthew M WillmeringCenter for Pulmonary Imaging Research, Division of Pulmonary Medicine, Cincinnati, Ohio (A.M.M., A.S.B., M.M.W., E.B.H., L.L.W., Z.I.C., J.C.W.); Department of Pediatrics, Cincinnati, Ohio (M.M.W., E.B.H., L.L.W., Z.I.C., J.C.W.).
Erik B HysingerCenter for Pulmonary Imaging Research, Division of Pulmonary Medicine, Cincinnati, Ohio (A.M.M., A.S.B., M.M.W., E.B.H., L.L.W., Z.I.C., J.C.W.); Department of Pediatrics, Cincinnati, Ohio (M.M.W., E.B.H., L.L.W., Z.I.C., J.C.W.); Cincinnati Bronchopulmonary Dysplasia Center, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio (E.B.H., J.C.W.).
Francis X McCormackDepartment of Internal Medicine, Division of Pulmonary, Critical Care, and Sleep Medicine, University of Cincinnati College of Medicine, Cincinnati, Ohio (F.X.M.).
Laura L WalkupCenter for Pulmonary Imaging Research, Division of Pulmonary Medicine, Cincinnati, Ohio (A.M.M., A.S.B., M.M.W., E.B.H., L.L.W., Z.I.C., J.C.W.); Department of Radiology, Cincinnati, Ohio (L.L.W., Z.I.C., J.C.W.); Department of Pediatrics, Cincinnati, Ohio (M.M.W., E.B.H., L.L.W., Z.I.C., J.C.W.); Department of Biomedical Engineering, Cincinnati, Ohio (L.L.W., Z.I.C.).
Zackary I ClevelandCenter for Pulmonary Imaging Research, Division of Pulmonary Medicine, Cincinnati, Ohio (A.M.M., A.S.B., M.M.W., E.B.H., L.L.W., Z.I.C., J.C.W.); Department of Radiology, Cincinnati, Ohio (L.L.W., Z.I.C., J.C.W.); Department of Pediatrics, Cincinnati, Ohio (M.M.W., E.B.H., L.L.W., Z.I.C., J.C.W.); Department of Biomedical Engineering, Cincinnati, Ohio (L.L.W., Z.I.C.).
Jason C WoodsCenter for Pulmonary Imaging Research, Division of Pulmonary Medicine, Cincinnati, Ohio (A.M.M., A.S.B., M.M.W., E.B.H., L.L.W., Z.I.C., J.C.W.); Department of Radiology, Cincinnati, Ohio (L.L.W., Z.I.C., J.C.W.); Department of Pediatrics, Cincinnati, Ohio (M.M.W., E.B.H., L.L.W., Z.I.C., J.C.W.); Cincinnati Bronchopulmonary Dysplasia Center, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio (E.B.H., J.C.W.); Department of Physics, University of Cincinnati, Cincinnati, Ohio (J.C.W.). Electronic address: Jason.Woods@cchmc.org.

Funding

UTE MRI to monitor CF lung disease and response to CFTR modulation in young childrenR01HL131012 · NHLBI · CINCINNATI CHILDRENS HOSP MED CTR · PI Raouf S. Amin, JASON C WOODS · 2016 to 2026
$6.8M
NHLBI NIH HHS R01 HL131012
6 · The paper itself

Abstract

RATIONALE AND

objectivesHyperpolarized MATERIALS AND

methodsXenon MRI data in six pulmonary conditions (control, asthma, bronchiolitis obliterans syndrome, bronchopulmonary dysplasia, cystic fibrosis, LAM) were used to train convolutional neural networks. Network performance was assessed with top-1 and top-2 accuracy, recall, precision, and one-versus-all area under the curve (AUC). Gradient class-activation-mapping (Grad-CAM) was used to visualize what parts of the images were important for classification.

resultsTraining/testing data were collected from 262 participants. The top performing network (VGG-16) had top-1 accuracy=56%, top-2 accuracy=78%, recall=.30, precision=.70, and AUC=.85. The network performed better on larger classes (top-1 accuracy: control=62% [n=57], CF=67% [n=85], LAM=69% [n=61]) and outperformed human observers (human top-1 accuracy=40%, network top-1 accuracy=61% on a single training fold).

conclusionWe developed an artificial intelligence tool that could classify disease from xenon ventilation images alone that outperformed human observers. This suggests that xenon images have additional, disease-specific information that could be useful for cases that are clinically challenging or for disease phenotyping.

Indexed as

Artificial IntelligenceImage Interpretation, Computer-AssistedLung DiseasesMagnetic Resonance ImagingPulmonary VentilationXenonAdultFemaleHumansMaleNeural Networks, ComputerReproducibility of ResultsXenon IsotopesXenonXenon IsotopesArtificial intelligenceClassificationConvolutional neural networksDeep learningPulmonary imaging

Identifiers

PMID40617705
PMCPMC13034160

What Socratic holds

Textmetadata
LicenceTDM
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 Socratic graph.