Evidence map›Paper›PMID 33893070›Full record

ArticleThe Lancet. Digital health2021

Deep learning to detect acute respiratory distress syndrome on chest radiographs: a retrospective study with external validation.

Michael W Sjoding, Daniel Taylor, Jonathan Motyka, Elizabeth Lee, Ivan Co, Dru Claar, Jakob I McSparron, Sardar Ansari, Meeta Prasad Kerlin, John P Reilly and 9 more

Open access · goldAbstract readEvaluation Study
PubMed Full text at PMC Publisher
In one paragraph

Article in The Lancet. Digital health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
43citing papers in PubMed, 1 pooled it
8.2field-weighted citation impact, top 2% 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

43 citing papers in PubMed, 1 synthesis or guideline pooled it, 75 citations in OpenAlex.

  1. Pooled it
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  13. Multimodal Deep Learning for ARDS Detection.medRxiv : the preprint server for health sciences · 2025
    Article
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  20. Imaging the Lung in ARDS: A Primer.Respiratory care · 2024
    Review
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

19 authors at 5 institutions in 1 country.

Michael W SjodingDivision of Pulmonary and Critical Care Medicine, Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI, USA; Center for Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA; Michigan Center for Integrative Research in Critical Care; Ann Arbor, MI, USA; Institute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor, MI, USA. Electronic address: msjoding@umich.edu.
Daniel TaylorMichigan Center for Integrative Research in Critical Care; Ann Arbor, MI, USA.
Jonathan MotykaMichigan Center for Integrative Research in Critical Care; Ann Arbor, MI, USA.
Elizabeth LeeDepartment of Radiology, University of Michigan Medical School, Ann Arbor, MI, USA.
Ivan CoDivision of Pulmonary and Critical Care Medicine, Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI, USA; Department of Emergency Medicine, University of Michigan Medical School, Ann Arbor, MI, USA; Michigan Center for Integrative Research in Critical Care; Ann Arbor, MI, USA.
Dru ClaarDivision of Pulmonary and Critical Care Medicine, Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI, USA.
Jakob I McSparronDivision of Pulmonary and Critical Care Medicine, Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI, USA; Michigan Center for Integrative Research in Critical Care; Ann Arbor, MI, USA.
Sardar AnsariDepartment of Emergency Medicine, University of Michigan Medical School, Ann Arbor, MI, USA; Michigan Center for Integrative Research in Critical Care; Ann Arbor, MI, USA.
Meeta Prasad KerlinDivision of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
John P ReillyDivision of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA; Center for Translational Lung Biology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
Michael G S ShashatyDivision of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
Brian J AndersonDivision of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
Tiffanie K JonesDivision of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA; Center for Translational Lung Biology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
Harrison M DrebinDivision of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA; Center for Translational Lung Biology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
Caroline A G IttnerDivision of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA; Center for Translational Lung Biology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
Nuala J MeyerDivision of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA; Center for Translational Lung Biology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
Theodore J IwashynaDivision of Pulmonary and Critical Care Medicine, Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI, USA; VA Center for Clinic Management Research, Ann Arbor, MI, USA; Institute for Social Research, Ann Arbor, MI, USA.
Kevin R WardCenter for Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA; Department of Emergency Medicine, University of Michigan Medical School, Ann Arbor, MI, USA; Michigan Center for Integrative Research in Critical Care; Ann Arbor, MI, USA.
Christopher E GilliesDepartment of Emergency Medicine, University of Michigan Medical School, Ann Arbor, MI, USA; Michigan Center for Integrative Research in Critical Care; Ann Arbor, MI, USA; Michigan Institute for Data Science, University of Michigan, Ann Arbor, MI, USA.
University of Pennsylvania · USUniversity of Michigan–Ann Arbor · USAnn Arbor Center for Independent Living · USMichigan Medicine · USVA Center for Clinical Management Research · US

Funding

Targeting Angiopoietin-2 in ARDSR01HL137006 · NHLBI · UNIVERSITY OF PENNSYLVANIA · PI MEYER, NUALA JENNINGS · 2017 to 2021
$2.5M
Reconsidering the IL-1 axis in sepsis-associated ARDSR01HL137915 · NHLBI · UNIVERSITY OF PENNSYLVANIA · PI MEYER, NUALA JENNINGS · 2017 to 2020
$2.1M
Neurocognitive Impairment in Acute Respiratory Failure and Shock: Understanding the Role of Neuronal ExcitotoxicityK23HL140482 · NHLBI · UNIVERSITY OF PENNSYLVANIA · PI ANDERSON, BRIAN J. · 2018 to 2022
$959k
SCH: Leveraging Clinical Time Series to Learn Optimal Treatment of Acute Dyspnea R01LM013325 · NLM · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI SJODING, MICHAEL WILLIAM, WIENS, JENNA · 2019 to 2022
$935k
Data-Driven Identification of the Acute Respiratory Distress SyndromeK01HL136687 · NHLBI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI SJODING, MICHAEL WILLIAM · 2017 to 2021
$862k
The Role of ABO Glycosyltransferases in the Acute Respiratory Distress SyndromeK23HL125723 · NHLBI · UNIVERSITY OF PENNSYLVANIA · PI REILLY, JOHN PATRICK · 2015 to 2019
$840k
Mentoring Patient-Oriented Research to Promote ARDS Precision MedicineK24HL155804 · NHLBI · UNIVERSITY OF PENNSYLVANIA · PI MEYER, NUALA JENNINGS · 2021 to 2021
$124k
NHLBI NIH HHS K01 HL136687NHLBI NIH HHS K23 HL125723NHLBI NIH HHS K23 HL140482NHLBI NIH HHS K24 HL155804NHLBI NIH HHS L30 HL138779NHLBI NIH HHS R01 HL137006NHLBI NIH HHS R01 HL137915NLM NIH HHS R01 LM013325
6 · The paper itself

Abstract

backgroundAcute respiratory distress syndrome (ARDS) is a common, but under-recognised, critical illness syndrome associated with high mortality. An important factor in its under-recognition is the variability in chest radiograph interpretation for ARDS. We sought to train a deep convolutional neural network (CNN) to detect ARDS findings on chest radiographs.

methodsCNNs were pretrained on 595 506 radiographs from two centres to identify common chest findings (eg, opacity and effusion), and then trained on 8072 radiographs annotated for ARDS by multiple physicians using various transfer learning approaches. The best performing CNN was tested on chest radiographs in an internal and external cohort, including a subset reviewed by six physicians, including a chest radiologist and physicians trained in intensive care medicine. Chest radiograph data were acquired from four US hospitals.

findingsIn an internal test set of 1560 chest radiographs from 455 patients with acute hypoxaemic respiratory failure, a CNN could detect ARDS with an area under the receiver operator characteristics curve (AUROC) of 0·92 (95% CI 0·89-0·94). In the subgroup of 413 images reviewed by at least six physicians, its AUROC was 0·93 (95% CI 0·88-0·96), sensitivity 83·0% (95% CI 74·0-91·1), and specificity 88·3% (95% CI 83·1-92·8). Among images with zero of six ARDS annotations (n=155), the median CNN probability was 11%, with six (4%) assigned a probability above 50%. Among images with six of six ARDS annotations (n=27), the median CNN probability was 91%, with two (7%) assigned a probability below 50%. In an external cohort of 958 chest radiographs from 431 patients with sepsis, the AUROC was 0·88 (95% CI 0·85-0·91). When radiographs annotated as equivocal were excluded, the AUROC was 0·93 (0·92-0·95).

interpretationA CNN can be trained to achieve expert physician-level performance in ARDS detection on chest radiographs. Further research is needed to evaluate the use of these algorithms to support real-time identification of ARDS patients to ensure fidelity with evidence-based care or to support ongoing ARDS research.

fundingNational Institutes of Health, Department of Defense, and Department of Veterans Affairs.

Indexed as

Deep LearningNeural Networks, ComputerRadiography, ThoracicAgedAlgorithmsArea Under CurveDatasets as TopicFemaleHospitalsHumansLungMaleMiddle AgedPleural CavityPleural DiseasesRadiographic Image Interpretation, Computer-Assisted

Identifiers

PMID33893070
PMCPMC8182690
OpenAlexW3155436984

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.