Evidence map›Paper›PMID 33778529›Full record

ArticleRadiology. Cardiothoracic imaging2019

Left Atrial Volume as a Biomarker of Atrial Fibrillation at Routine Chest CT: Deep Learning Approach.

Alex Bratt, Zachary Guenther, Lewis D Hahn, Michael Kadoch, Patrick L Adams, Ann N C Leung, Haiwei H Guo

Open access · bronzeAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Radiology. Cardiothoracic imaging, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
1.3field-weighted citation impact, top 17% of its field
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

10 citing papers in PubMed, 16 citations in OpenAlex.

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  8. Applications of multimodality imaging for left atrial catheter ablation.European heart journal. Cardiovascular Imaging · 2021
    Review
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors at 1 institution in 1 country.

Alex BrattDepartment of Radiology, Stanford University School of Medicine, 300 Pasteur Dr, Stanford, CA 94305 (A.B., Z.G., L.D.H., P.L.A., A.N.C.L., H.H.G.); and Department of Radiology, University of California at Davis, Sacramento, Calif (M.K.).ORCID 0000-0002-2445-2357
Zachary GuentherDepartment of Radiology, Stanford University School of Medicine, 300 Pasteur Dr, Stanford, CA 94305 (A.B., Z.G., L.D.H., P.L.A., A.N.C.L., H.H.G.); and Department of Radiology, University of California at Davis, Sacramento, Calif (M.K.).
Lewis D HahnDepartment of Radiology, Stanford University School of Medicine, 300 Pasteur Dr, Stanford, CA 94305 (A.B., Z.G., L.D.H., P.L.A., A.N.C.L., H.H.G.); and Department of Radiology, University of California at Davis, Sacramento, Calif (M.K.).ORCID 0000-0003-0468-9442
Michael KadochDepartment of Radiology, Stanford University School of Medicine, 300 Pasteur Dr, Stanford, CA 94305 (A.B., Z.G., L.D.H., P.L.A., A.N.C.L., H.H.G.); and Department of Radiology, University of California at Davis, Sacramento, Calif (M.K.).
Patrick L AdamsDepartment of Radiology, Stanford University School of Medicine, 300 Pasteur Dr, Stanford, CA 94305 (A.B., Z.G., L.D.H., P.L.A., A.N.C.L., H.H.G.); and Department of Radiology, University of California at Davis, Sacramento, Calif (M.K.).ORCID 0000-0001-6673-3475
Ann N C LeungDepartment of Radiology, Stanford University School of Medicine, 300 Pasteur Dr, Stanford, CA 94305 (A.B., Z.G., L.D.H., P.L.A., A.N.C.L., H.H.G.); and Department of Radiology, University of California at Davis, Sacramento, Calif (M.K.).ORCID 0000-0001-7807-2643
Haiwei H GuoDepartment of Radiology, Stanford University School of Medicine, 300 Pasteur Dr, Stanford, CA 94305 (A.B., Z.G., L.D.H., P.L.A., A.N.C.L., H.H.G.); and Department of Radiology, University of California at Davis, Sacramento, Calif (M.K.).ORCID 0000-0001-5511-1750
Stanford University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTo test the performance of a deep learning (DL) model in predicting atrial fibrillation (AF) at routine nongated chest CT. MATERIALS AND

methodsA retrospective derivation cohort (mean age, 64 years; 51% female) consisting of 500 consecutive patients who underwent routine chest CT served as the training set for a DL model that was used to measure left atrial volume. The model was then used to measure atrial size for a separate 500-patient validation cohort (mean age, 61 years; 46% female), in which the AF status was determined by performing a chart review. The performance of automated atrial size as a predictor of AF was evaluated by using a receiver operating characteristic analysis.

resultsThere was good agreement between manual and model-generated segmentation maps by all measures of overlap and surface distance (mean Dice = 0.87, intersection over union = 0.77, Hausdorff distance = 4.36 mm, average symmetric surface distance = 0.96 mm), and agreement was slightly but significantly greater than that between human observers (mean Dice = 0.85 [automated] vs 0.84 [manual];

conclusionLeft atrial volume is an independent predictor of the AF status as measured at routine nongated chest CT. Deep learning is a suitable tool for automated measurement.© RSNA, 2019See also the commentary by de Roos and Tao in this issue.

Identifiers

PMID33778529
PMCPMC7977801
OpenAlexW2996425568

What OpenQuestion holds

Textmetadata
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.