Light Convolutional Neural Network to Detect Chronic Obstructive Pulmonary Disease (COPDxNet): A Multicenter Model Development and External Validation Study Article Swipe
AKM Shahariar Azad Rabby
,
Muhammad F. A. Chaudhary
,
Pratim Saha
,
Venkata Sthanam
,
Arie Nakhmani
,
Chengcui Zhang
,
R. Graham Barr
,
Jessica Bon
,
Christopher B. Cooper
,
Jeffrey L. Curtis
,
Eric A. Hoffman
,
Robert Paine
,
A.S. Kizhakke Puliyakote
,
Joyce Schroeder
,
Jessica C. Sieren
,
Benjamin M. Smith
,
Prescott G. Woodruff
,
Joseph M. Reinhardt
,
Surya P. Bhatt
,
Sandeep Bodduluri
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1101/2025.07.30.25332459
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1101/2025.07.30.25332459
Background Approximately 70% of adults with chronic obstructive pulmonary disease (COPD) remain undiagnosed. Opportunistic screening using chest computed tomography (CT) scans, commonly acquired in clinical practice, may be used to improve COPD detection through simple, clinically applicable deep-learning models. We developed a lightweight, convolutional neural network (COPDxNet) that utilizes minimally processed chest CT scans to detect COPD. Methods We analyzed 13,043 inspiratory chest CT scans from the COPDGene participants, (9,675 standard-dose and 3,368 low-dose scans), which we randomly split into training (70%) and test (30%) sets at the participant level to no individual contributed to both sets. COPD was defined by postbronchodilator FEV /FVC < 0.70. We constructed a simple, four-block convolutional model that was trained on pooled data and validated on the held-out standard- and low-dose test sets. External validation was performed using standard-dose CT scans from 2,890 SPIROMICS participants and low-dose CT scans from 7,893 participants in the National Lung Screening Trial (NLST). We evaluated performance using the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, Brier scores, and calibration curves. Findings On COPDGene standard-dose CT scans, COPDxNet achieved an AUC of 0.92 (95% CI: 0.91 to 0.93), sensitivity of 80.2%, and specificity of 89.4%. On low-dose scans, AUC was 0.88 (95% CI: 0.86 to 0.90). When the COPDxNet model was applied to external validation datasets, it showed an AUC of 0.92 (95% CI: 0.91 to 0.93) in SPIROMICS and 0.82 (95% CI: 0.81 to 0.83) on NLST. The model was well-calibrated, with Brier scores of 0.11 for standard- dose and 0.13 for low-dose CT scans in COPDGene, 0.12 in SPIROMICS, and 0.17 in NLST. Interpretation COPDxNet demonstrates high discriminative accuracy and generalizability for detecting COPD on standard- and low-dose chest CT scans, supporting its potential for clinical and screening applications across diverse populations.
Related Topics
Concepts
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2025.07.30.25332459
- https://www.medrxiv.org/content/medrxiv/early/2025/08/01/2025.07.30.25332459.full.pdf
- OA Status
- green
- Cited By
- 1
- References
- 40
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4412835675
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4412835675Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1101/2025.07.30.25332459Digital Object Identifier
- Title
-
Light Convolutional Neural Network to Detect Chronic Obstructive Pulmonary Disease (COPDxNet): A Multicenter Model Development and External Validation StudyWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-08-01Full publication date if available
- Authors
-
AKM Shahariar Azad Rabby, Muhammad F. A. Chaudhary, Pratim Saha, Venkata Sthanam, Arie Nakhmani, Chengcui Zhang, R. Graham Barr, Jessica Bon, Christopher B. Cooper, Jeffrey L. Curtis, Eric A. Hoffman, Robert Paine, A.S. Kizhakke Puliyakote, Joyce Schroeder, Jessica C. Sieren, Benjamin M. Smith, Prescott G. Woodruff, Joseph M. Reinhardt, Surya P. Bhatt, Sandeep BodduluriList of authors in order
- Landing page
-
https://doi.org/10.1101/2025.07.30.25332459Publisher landing page
- PDF URL
-
https://www.medrxiv.org/content/medrxiv/early/2025/08/01/2025.07.30.25332459.full.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.medrxiv.org/content/medrxiv/early/2025/08/01/2025.07.30.25332459.full.pdfDirect OA link when available
- Concepts
-
Medicine, COPD, Receiver operating characteristic, Gold standard (test), Nuclear medicine, Radiology, Area under the curve, Convolutional neural network, Pulmonary disease, Internal medicine, Artificial intelligence, Computer scienceTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1Per-year citation counts (last 5 years)
- References (count)
-
40Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4412835675 |
|---|---|
| doi | https://doi.org/10.1101/2025.07.30.25332459 |
| ids.doi | https://doi.org/10.1101/2025.07.30.25332459 |
| ids.pmid | https://pubmed.ncbi.nlm.nih.gov/40766163 |
| ids.openalex | https://openalex.org/W4412835675 |
| fwci | 4.88980793 |
| type | preprint |
| title | Light Convolutional Neural Network to Detect Chronic Obstructive Pulmonary Disease (COPDxNet): A Multicenter Model Development and External Validation Study |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T10143 |
| topics[0].field.id | https://openalex.org/fields/27 |
| topics[0].field.display_name | Medicine |
| topics[0].score | 0.9991000294685364 |
| topics[0].domain.id | https://openalex.org/domains/4 |
| topics[0].domain.display_name | Health Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2740 |
| topics[0].subfield.display_name | Pulmonary and Respiratory Medicine |
| topics[0].display_name | Chronic Obstructive Pulmonary Disease (COPD) Research |
| topics[1].id | https://openalex.org/T12120 |
| topics[1].field.id | https://openalex.org/fields/23 |
| topics[1].field.display_name | Environmental Science |
| topics[1].score | 0.9848999977111816 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2305 |
| topics[1].subfield.display_name | Environmental Engineering |
| topics[1].display_name | Air Quality Monitoring and Forecasting |
| topics[2].id | https://openalex.org/T12419 |
| topics[2].field.id | https://openalex.org/fields/27 |
| topics[2].field.display_name | Medicine |
| topics[2].score | 0.9757999777793884 |
| topics[2].domain.id | https://openalex.org/domains/4 |
| topics[2].domain.display_name | Health Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2740 |
| topics[2].subfield.display_name | Pulmonary and Respiratory Medicine |
| topics[2].display_name | Phonocardiography and Auscultation Techniques |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C71924100 |
| concepts[0].level | 0 |
| concepts[0].score | 0.8125814199447632 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q11190 |
| concepts[0].display_name | Medicine |
| concepts[1].id | https://openalex.org/C2776780178 |
| concepts[1].level | 2 |
| concepts[1].score | 0.8108236789703369 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q199804 |
| concepts[1].display_name | COPD |
| concepts[2].id | https://openalex.org/C58471807 |
| concepts[2].level | 2 |
| concepts[2].score | 0.7568234801292419 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q327120 |
| concepts[2].display_name | Receiver operating characteristic |
| concepts[3].id | https://openalex.org/C40993552 |
| concepts[3].level | 2 |
| concepts[3].score | 0.5607770085334778 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q514654 |
| concepts[3].display_name | Gold standard (test) |
| concepts[4].id | https://openalex.org/C2989005 |
| concepts[4].level | 1 |
| concepts[4].score | 0.4933597147464752 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q214963 |
| concepts[4].display_name | Nuclear medicine |
| concepts[5].id | https://openalex.org/C126838900 |
| concepts[5].level | 1 |
| concepts[5].score | 0.48589199781417847 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q77604 |
| concepts[5].display_name | Radiology |
| concepts[6].id | https://openalex.org/C76318530 |
| concepts[6].level | 2 |
| concepts[6].score | 0.46547195315361023 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q16833590 |
| concepts[6].display_name | Area under the curve |
| concepts[7].id | https://openalex.org/C81363708 |
| concepts[7].level | 2 |
| concepts[7].score | 0.4653393030166626 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q17084460 |
| concepts[7].display_name | Convolutional neural network |
| concepts[8].id | https://openalex.org/C2992779976 |
| concepts[8].level | 2 |
| concepts[8].score | 0.45897215604782104 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q3286546 |
| concepts[8].display_name | Pulmonary disease |
| concepts[9].id | https://openalex.org/C126322002 |
| concepts[9].level | 1 |
| concepts[9].score | 0.2806999683380127 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q11180 |
| concepts[9].display_name | Internal medicine |
| concepts[10].id | https://openalex.org/C154945302 |
| concepts[10].level | 1 |
| concepts[10].score | 0.1949433982372284 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[10].display_name | Artificial intelligence |
| concepts[11].id | https://openalex.org/C41008148 |
| concepts[11].level | 0 |
| concepts[11].score | 0.09818953275680542 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[11].display_name | Computer science |
| keywords[0].id | https://openalex.org/keywords/medicine |
| keywords[0].score | 0.8125814199447632 |
| keywords[0].display_name | Medicine |
| keywords[1].id | https://openalex.org/keywords/copd |
| keywords[1].score | 0.8108236789703369 |
| keywords[1].display_name | COPD |
| keywords[2].id | https://openalex.org/keywords/receiver-operating-characteristic |
| keywords[2].score | 0.7568234801292419 |
| keywords[2].display_name | Receiver operating characteristic |
| keywords[3].id | https://openalex.org/keywords/gold-standard |
| keywords[3].score | 0.5607770085334778 |
| keywords[3].display_name | Gold standard (test) |
| keywords[4].id | https://openalex.org/keywords/nuclear-medicine |
| keywords[4].score | 0.4933597147464752 |
| keywords[4].display_name | Nuclear medicine |
| keywords[5].id | https://openalex.org/keywords/radiology |
| keywords[5].score | 0.48589199781417847 |
| keywords[5].display_name | Radiology |
| keywords[6].id | https://openalex.org/keywords/area-under-the-curve |
| keywords[6].score | 0.46547195315361023 |
| keywords[6].display_name | Area under the curve |
| keywords[7].id | https://openalex.org/keywords/convolutional-neural-network |
| keywords[7].score | 0.4653393030166626 |
| keywords[7].display_name | Convolutional neural network |
| keywords[8].id | https://openalex.org/keywords/pulmonary-disease |
| keywords[8].score | 0.45897215604782104 |
| keywords[8].display_name | Pulmonary disease |
| keywords[9].id | https://openalex.org/keywords/internal-medicine |
| keywords[9].score | 0.2806999683380127 |
| keywords[9].display_name | Internal medicine |
| keywords[10].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[10].score | 0.1949433982372284 |
| keywords[10].display_name | Artificial intelligence |
| keywords[11].id | https://openalex.org/keywords/computer-science |
| keywords[11].score | 0.09818953275680542 |
| keywords[11].display_name | Computer science |
| language | en |
| locations[0].id | doi:10.1101/2025.07.30.25332459 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4306402567 |
| locations[0].source.issn | |
| locations[0].source.type | repository |
| locations[0].source.is_oa | False |
| locations[0].source.issn_l | |
| locations[0].source.is_core | False |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | bioRxiv (Cold Spring Harbor Laboratory) |
| locations[0].source.host_organization | https://openalex.org/I2750212522 |
| locations[0].source.host_organization_name | Cold Spring Harbor Laboratory |
| locations[0].source.host_organization_lineage | https://openalex.org/I2750212522 |
| locations[0].license | cc-by-nc-nd |
| locations[0].pdf_url | https://www.medrxiv.org/content/medrxiv/early/2025/08/01/2025.07.30.25332459.full.pdf |
| locations[0].version | acceptedVersion |
| locations[0].raw_type | posted-content |
| locations[0].license_id | https://openalex.org/licenses/cc-by-nc-nd |
| locations[0].is_accepted | True |
| locations[0].is_published | False |
| locations[0].raw_source_name | |
| locations[0].landing_page_url | https://doi.org/10.1101/2025.07.30.25332459 |
| locations[1].id | pmid:40766163 |
| locations[1].is_oa | False |
| locations[1].source.id | https://openalex.org/S4306525036 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | False |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | PubMed |
| locations[1].source.host_organization | https://openalex.org/I1299303238 |
| locations[1].source.host_organization_name | National Institutes of Health |
| locations[1].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | publishedVersion |
| locations[1].raw_type | |
| locations[1].license_id | |
| locations[1].is_accepted | True |
| locations[1].is_published | True |
| locations[1].raw_source_name | medRxiv : the preprint server for health sciences |
| locations[1].landing_page_url | https://pubmed.ncbi.nlm.nih.gov/40766163 |
| locations[2].id | pmh:oai:pubmedcentral.nih.gov:12324643 |
| locations[2].is_oa | True |
| locations[2].source.id | https://openalex.org/S2764455111 |
| locations[2].source.issn | |
| locations[2].source.type | repository |
| locations[2].source.is_oa | False |
| locations[2].source.issn_l | |
| locations[2].source.is_core | False |
| locations[2].source.is_in_doaj | False |
| locations[2].source.display_name | PubMed Central |
| locations[2].source.host_organization | https://openalex.org/I1299303238 |
| locations[2].source.host_organization_name | National Institutes of Health |
| locations[2].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[2].license | |
| locations[2].pdf_url | |
| locations[2].version | submittedVersion |
| locations[2].raw_type | Text |
| locations[2].license_id | |
| locations[2].is_accepted | False |
| locations[2].is_published | False |
| locations[2].raw_source_name | medRxiv |
| locations[2].landing_page_url | https://www.ncbi.nlm.nih.gov/pmc/articles/12324643 |
| indexed_in | crossref, pubmed |
| authorships[0].author.id | https://openalex.org/A5061996966 |
| authorships[0].author.orcid | https://orcid.org/0000-0003-3994-3105 |
| authorships[0].author.display_name | AKM Shahariar Azad Rabby |
| authorships[0].countries | US |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I32389192 |
| authorships[0].affiliations[0].raw_affiliation_string | Department of Computer Science, The University of Alabama at Birmingham, AL, 3594 |
| authorships[0].institutions[0].id | https://openalex.org/I32389192 |
| authorships[0].institutions[0].ror | https://ror.org/008s83205 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I32389192 |
| authorships[0].institutions[0].country_code | US |
| authorships[0].institutions[0].display_name | University of Alabama at Birmingham |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | AKM Shahariar Azad Rabby |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Department of Computer Science, The University of Alabama at Birmingham, AL, 3594 |
| authorships[1].author.id | https://openalex.org/A5042238069 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-0807-4560 |
| authorships[1].author.display_name | Muhammad F. A. Chaudhary |
| authorships[1].countries | US |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I32389192 |
| authorships[1].affiliations[0].raw_affiliation_string | Division of Pulmonary, Allergy and Critical Care Medicine, The University of Alabama at Birmingham, Birmingham, AL, 5294 |
| authorships[1].institutions[0].id | https://openalex.org/I32389192 |
| authorships[1].institutions[0].ror | https://ror.org/008s83205 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I32389192 |
| authorships[1].institutions[0].country_code | US |
| authorships[1].institutions[0].display_name | University of Alabama at Birmingham |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Muhammad Faizyab Ali Chaudhary |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Division of Pulmonary, Allergy and Critical Care Medicine, The University of Alabama at Birmingham, Birmingham, AL, 5294 |
| authorships[2].author.id | https://openalex.org/A5043291928 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-8282-6206 |
| authorships[2].author.display_name | Pratim Saha |
| authorships[2].countries | US |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I32389192 |
| authorships[2].affiliations[0].raw_affiliation_string | Department of Computer Science, The University of Alabama at Birmingham, AL, 3594 |
| authorships[2].institutions[0].id | https://openalex.org/I32389192 |
| authorships[2].institutions[0].ror | https://ror.org/008s83205 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I32389192 |
| authorships[2].institutions[0].country_code | US |
| authorships[2].institutions[0].display_name | University of Alabama at Birmingham |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Pratim Saha |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Department of Computer Science, The University of Alabama at Birmingham, AL, 3594 |
| authorships[3].author.id | https://openalex.org/A5002348322 |
| authorships[3].author.orcid | https://orcid.org/0009-0002-3541-0897 |
| authorships[3].author.display_name | Venkata Sthanam |
| authorships[3].countries | US |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I32389192 |
| authorships[3].affiliations[0].raw_affiliation_string | Center for Lung Analytics and Imaging Research (CLAIR), The University of Alabama at Birmingham, Birmingham, AL, 35294 |
| authorships[3].institutions[0].id | https://openalex.org/I32389192 |
| authorships[3].institutions[0].ror | https://ror.org/008s83205 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I32389192 |
| authorships[3].institutions[0].country_code | US |
| authorships[3].institutions[0].display_name | University of Alabama at Birmingham |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Venkata Sthanam |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | Center for Lung Analytics and Imaging Research (CLAIR), The University of Alabama at Birmingham, Birmingham, AL, 35294 |
| authorships[4].author.id | https://openalex.org/A5057368675 |
| authorships[4].author.orcid | https://orcid.org/0000-0002-0987-4834 |
| authorships[4].author.display_name | Arie Nakhmani |
| authorships[4].countries | US |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I32389192 |
| authorships[4].affiliations[0].raw_affiliation_string | Center for Lung Analytics and Imaging Research (CLAIR), The University of Alabama at Birmingham, Birmingham, AL, 35294 |
| authorships[4].institutions[0].id | https://openalex.org/I32389192 |
| authorships[4].institutions[0].ror | https://ror.org/008s83205 |
| authorships[4].institutions[0].type | education |
| authorships[4].institutions[0].lineage | https://openalex.org/I32389192 |
| authorships[4].institutions[0].country_code | US |
| authorships[4].institutions[0].display_name | University of Alabama at Birmingham |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Arie Nakhmani |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | Center for Lung Analytics and Imaging Research (CLAIR), The University of Alabama at Birmingham, Birmingham, AL, 35294 |
| authorships[5].author.id | https://openalex.org/A5061568662 |
| authorships[5].author.orcid | https://orcid.org/0000-0002-5868-6450 |
| authorships[5].author.display_name | Chengcui Zhang |
| authorships[5].countries | US |
| authorships[5].affiliations[0].institution_ids | https://openalex.org/I32389192 |
| authorships[5].affiliations[0].raw_affiliation_string | Department of Computer Science, The University of Alabama at Birmingham, AL, 3594 |
| authorships[5].institutions[0].id | https://openalex.org/I32389192 |
| authorships[5].institutions[0].ror | https://ror.org/008s83205 |
| authorships[5].institutions[0].type | education |
| authorships[5].institutions[0].lineage | https://openalex.org/I32389192 |
| authorships[5].institutions[0].country_code | US |
| authorships[5].institutions[0].display_name | University of Alabama at Birmingham |
| authorships[5].author_position | middle |
| authorships[5].raw_author_name | Chengcui Zhang |
| authorships[5].is_corresponding | False |
| authorships[5].raw_affiliation_strings | Department of Computer Science, The University of Alabama at Birmingham, AL, 3594 |
| authorships[6].author.id | https://openalex.org/A5103134292 |
| authorships[6].author.orcid | https://orcid.org/0000-0002-0743-2303 |
| authorships[6].author.display_name | R. Graham Barr |
| authorships[6].countries | US |
| authorships[6].affiliations[0].institution_ids | https://openalex.org/I78577930 |
| authorships[6].affiliations[0].raw_affiliation_string | Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, 10032 |
| authorships[6].institutions[0].id | https://openalex.org/I78577930 |
| authorships[6].institutions[0].ror | https://ror.org/00hj8s172 |
| authorships[6].institutions[0].type | education |
| authorships[6].institutions[0].lineage | https://openalex.org/I78577930 |
| authorships[6].institutions[0].country_code | US |
| authorships[6].institutions[0].display_name | Columbia University |
| authorships[6].author_position | middle |
| authorships[6].raw_author_name | R. Graham Barr |
| authorships[6].is_corresponding | False |
| authorships[6].raw_affiliation_strings | Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, 10032 |
| authorships[7].author.id | https://openalex.org/A5082385395 |
| authorships[7].author.orcid | https://orcid.org/0000-0002-4336-3835 |
| authorships[7].author.display_name | Jessica Bon |
| authorships[7].countries | US |
| authorships[7].affiliations[0].institution_ids | https://openalex.org/I47251452 |
| authorships[7].affiliations[0].raw_affiliation_string | Division of Pulmonary, Critical Care, Allergy and Immunology, School of Medicine, Wake Forest University, Winston-Salem, NC, 27109 |
| authorships[7].institutions[0].id | https://openalex.org/I47251452 |
| authorships[7].institutions[0].ror | https://ror.org/0207ad724 |
| authorships[7].institutions[0].type | education |
| authorships[7].institutions[0].lineage | https://openalex.org/I47251452 |
| authorships[7].institutions[0].country_code | US |
| authorships[7].institutions[0].display_name | Wake Forest University |
| authorships[7].author_position | middle |
| authorships[7].raw_author_name | Jessica Bon |
| authorships[7].is_corresponding | False |
| authorships[7].raw_affiliation_strings | Division of Pulmonary, Critical Care, Allergy and Immunology, School of Medicine, Wake Forest University, Winston-Salem, NC, 27109 |
| authorships[8].author.id | https://openalex.org/A5086501700 |
| authorships[8].author.orcid | https://orcid.org/0000-0002-6314-0903 |
| authorships[8].author.display_name | Christopher B. Cooper |
| authorships[8].countries | US |
| authorships[8].affiliations[0].institution_ids | https://openalex.org/I161318765 |
| authorships[8].affiliations[0].raw_affiliation_string | Department of Physiology, David Geffen School of Medicine at UCLA, Los Angeles, CA, 90095 |
| authorships[8].institutions[0].id | https://openalex.org/I161318765 |
| authorships[8].institutions[0].ror | https://ror.org/046rm7j60 |
| authorships[8].institutions[0].type | education |
| authorships[8].institutions[0].lineage | https://openalex.org/I161318765 |
| authorships[8].institutions[0].country_code | US |
| authorships[8].institutions[0].display_name | University of California, Los Angeles |
| authorships[8].author_position | middle |
| authorships[8].raw_author_name | Christopher B. Cooper |
| authorships[8].is_corresponding | False |
| authorships[8].raw_affiliation_strings | Department of Physiology, David Geffen School of Medicine at UCLA, Los Angeles, CA, 90095 |
| authorships[9].author.id | https://openalex.org/A5052110481 |
| authorships[9].author.orcid | https://orcid.org/0000-0001-5191-4847 |
| authorships[9].author.display_name | Jeffrey L. Curtis |
| authorships[9].countries | US |
| authorships[9].affiliations[0].institution_ids | https://openalex.org/I4210114445 |
| authorships[9].affiliations[0].raw_affiliation_string | Division of Pulmonary and Critical Care Medicine, University of Michigan Health System, Ann Arbor, MI, 4109 |
| authorships[9].institutions[0].id | https://openalex.org/I4210114445 |
| authorships[9].institutions[0].ror | https://ror.org/01zcpa714 |
| authorships[9].institutions[0].type | healthcare |
| authorships[9].institutions[0].lineage | https://openalex.org/I4210114445 |
| authorships[9].institutions[0].country_code | US |
| authorships[9].institutions[0].display_name | Michigan Medicine |
| authorships[9].author_position | middle |
| authorships[9].raw_author_name | Jeffrey L. Curtis |
| authorships[9].is_corresponding | False |
| authorships[9].raw_affiliation_strings | Division of Pulmonary and Critical Care Medicine, University of Michigan Health System, Ann Arbor, MI, 4109 |
| authorships[10].author.id | https://openalex.org/A5011527598 |
| authorships[10].author.orcid | https://orcid.org/0000-0001-8456-9437 |
| authorships[10].author.display_name | Eric A. Hoffman |
| authorships[10].countries | US |
| authorships[10].affiliations[0].institution_ids | https://openalex.org/I126307644 |
| authorships[10].affiliations[0].raw_affiliation_string | Department of Radiology, University of Iowa Roy J. and Lucille A. Carver College of Medicine, Iowa City, IA, 52242 |
| authorships[10].institutions[0].id | https://openalex.org/I126307644 |
| authorships[10].institutions[0].ror | https://ror.org/036jqmy94 |
| authorships[10].institutions[0].type | education |
| authorships[10].institutions[0].lineage | https://openalex.org/I126307644 |
| authorships[10].institutions[0].country_code | US |
| authorships[10].institutions[0].display_name | University of Iowa |
| authorships[10].author_position | middle |
| authorships[10].raw_author_name | Eric A. Hoffman |
| authorships[10].is_corresponding | False |
| authorships[10].raw_affiliation_strings | Department of Radiology, University of Iowa Roy J. and Lucille A. Carver College of Medicine, Iowa City, IA, 52242 |
| authorships[11].author.id | https://openalex.org/A5087218579 |
| authorships[11].author.orcid | https://orcid.org/0000-0002-4511-4437 |
| authorships[11].author.display_name | Robert Paine |
| authorships[11].countries | US |
| authorships[11].affiliations[0].institution_ids | https://openalex.org/I223532165 |
| authorships[11].affiliations[0].raw_affiliation_string | Division of Respiratory, Critical Care and Occupational Pulmonary Medicine, University of Utah, Salt Lake City, UT, 84112 |
| authorships[11].institutions[0].id | https://openalex.org/I223532165 |
| authorships[11].institutions[0].ror | https://ror.org/03r0ha626 |
| authorships[11].institutions[0].type | education |
| authorships[11].institutions[0].lineage | https://openalex.org/I223532165 |
| authorships[11].institutions[0].country_code | US |
| authorships[11].institutions[0].display_name | University of Utah |
| authorships[11].author_position | middle |
| authorships[11].raw_author_name | Robert Paine |
| authorships[11].is_corresponding | False |
| authorships[11].raw_affiliation_strings | Division of Respiratory, Critical Care and Occupational Pulmonary Medicine, University of Utah, Salt Lake City, UT, 84112 |
| authorships[12].author.id | https://openalex.org/A5070803053 |
| authorships[12].author.orcid | https://orcid.org/0000-0003-4541-0786 |
| authorships[12].author.display_name | A.S. Kizhakke Puliyakote |
| authorships[12].countries | US |
| authorships[12].affiliations[0].institution_ids | https://openalex.org/I126307644 |
| authorships[12].affiliations[0].raw_affiliation_string | Department of Radiology, University of Iowa Roy J. and Lucille A. Carver College of Medicine, Iowa City, IA, 52242 |
| authorships[12].institutions[0].id | https://openalex.org/I126307644 |
| authorships[12].institutions[0].ror | https://ror.org/036jqmy94 |
| authorships[12].institutions[0].type | education |
| authorships[12].institutions[0].lineage | https://openalex.org/I126307644 |
| authorships[12].institutions[0].country_code | US |
| authorships[12].institutions[0].display_name | University of Iowa |
| authorships[12].author_position | middle |
| authorships[12].raw_author_name | Abhilash Kizhakke Puliyakote |
| authorships[12].is_corresponding | False |
| authorships[12].raw_affiliation_strings | Department of Radiology, University of Iowa Roy J. and Lucille A. Carver College of Medicine, Iowa City, IA, 52242 |
| authorships[13].author.id | https://openalex.org/A5010212355 |
| authorships[13].author.orcid | https://orcid.org/0000-0002-7451-4886 |
| authorships[13].author.display_name | Joyce Schroeder |
| authorships[13].countries | US |
| authorships[13].affiliations[0].institution_ids | https://openalex.org/I4210125099 |
| authorships[13].affiliations[0].raw_affiliation_string | Department of Radiology, Mayo Clinic, Rochester, MN, 55905 |
| authorships[13].institutions[0].id | https://openalex.org/I4210125099 |
| authorships[13].institutions[0].ror | https://ror.org/03jp40720 |
| authorships[13].institutions[0].type | healthcare |
| authorships[13].institutions[0].lineage | https://openalex.org/I1330342723, https://openalex.org/I4210125099 |
| authorships[13].institutions[0].country_code | US |
| authorships[13].institutions[0].display_name | Mayo Clinic in Arizona |
| authorships[13].author_position | middle |
| authorships[13].raw_author_name | Joyce D. Schroeder |
| authorships[13].is_corresponding | False |
| authorships[13].raw_affiliation_strings | Department of Radiology, Mayo Clinic, Rochester, MN, 55905 |
| authorships[14].author.id | https://openalex.org/A5026306257 |
| authorships[14].author.orcid | https://orcid.org/0000-0002-1109-7886 |
| authorships[14].author.display_name | Jessica C. Sieren |
| authorships[14].countries | US |
| authorships[14].affiliations[0].institution_ids | https://openalex.org/I126307644 |
| authorships[14].affiliations[0].raw_affiliation_string | Department of Radiology, University of Iowa Roy J. and Lucille A. Carver College of Medicine, Iowa City, IA, 52242 |
| authorships[14].institutions[0].id | https://openalex.org/I126307644 |
| authorships[14].institutions[0].ror | https://ror.org/036jqmy94 |
| authorships[14].institutions[0].type | education |
| authorships[14].institutions[0].lineage | https://openalex.org/I126307644 |
| authorships[14].institutions[0].country_code | US |
| authorships[14].institutions[0].display_name | University of Iowa |
| authorships[14].author_position | middle |
| authorships[14].raw_author_name | Jessica C. Sieren |
| authorships[14].is_corresponding | False |
| authorships[14].raw_affiliation_strings | Department of Radiology, University of Iowa Roy J. and Lucille A. Carver College of Medicine, Iowa City, IA, 52242 |
| authorships[15].author.id | https://openalex.org/A5081621677 |
| authorships[15].author.orcid | https://orcid.org/0000-0003-3299-3628 |
| authorships[15].author.display_name | Benjamin M. Smith |
| authorships[15].countries | CA |
| authorships[15].affiliations[0].institution_ids | https://openalex.org/I2801766832 |
| authorships[15].affiliations[0].raw_affiliation_string | McGill University Health Centre, McGill University, Montreal, QC, Canada |
| authorships[15].institutions[0].id | https://openalex.org/I2801766832 |
| authorships[15].institutions[0].ror | https://ror.org/04cpxjv19 |
| authorships[15].institutions[0].type | healthcare |
| authorships[15].institutions[0].lineage | https://openalex.org/I2801766832 |
| authorships[15].institutions[0].country_code | CA |
| authorships[15].institutions[0].display_name | McGill University Health Centre |
| authorships[15].author_position | middle |
| authorships[15].raw_author_name | Benjamin Smith |
| authorships[15].is_corresponding | False |
| authorships[15].raw_affiliation_strings | McGill University Health Centre, McGill University, Montreal, QC, Canada |
| authorships[16].author.id | https://openalex.org/A5068244386 |
| authorships[16].author.orcid | https://orcid.org/0000-0003-3176-0603 |
| authorships[16].author.display_name | Prescott G. Woodruff |
| authorships[16].countries | US |
| authorships[16].affiliations[0].institution_ids | https://openalex.org/I180670191, https://openalex.org/I2803209242 |
| authorships[16].affiliations[0].raw_affiliation_string | Division of Pulmonary, Critical Care, Allergy and Sleep Medicine, Department of Medicine, University of California, San Francisco, CA, 94115 |
| authorships[16].institutions[0].id | https://openalex.org/I2803209242 |
| authorships[16].institutions[0].ror | https://ror.org/00pjdza24 |
| authorships[16].institutions[0].type | education |
| authorships[16].institutions[0].lineage | https://openalex.org/I2803209242 |
| authorships[16].institutions[0].country_code | US |
| authorships[16].institutions[0].display_name | University of California System |
| authorships[16].institutions[1].id | https://openalex.org/I180670191 |
| authorships[16].institutions[1].ror | https://ror.org/043mz5j54 |
| authorships[16].institutions[1].type | education |
| authorships[16].institutions[1].lineage | https://openalex.org/I180670191 |
| authorships[16].institutions[1].country_code | US |
| authorships[16].institutions[1].display_name | University of California, San Francisco |
| authorships[16].author_position | middle |
| authorships[16].raw_author_name | Prescott G. Woodruff |
| authorships[16].is_corresponding | False |
| authorships[16].raw_affiliation_strings | Division of Pulmonary, Critical Care, Allergy and Sleep Medicine, Department of Medicine, University of California, San Francisco, CA, 94115 |
| authorships[17].author.id | https://openalex.org/A5066017625 |
| authorships[17].author.orcid | https://orcid.org/0000-0003-3526-3591 |
| authorships[17].author.display_name | Joseph M. Reinhardt |
| authorships[17].countries | US |
| authorships[17].affiliations[0].institution_ids | https://openalex.org/I126307644 |
| authorships[17].affiliations[0].raw_affiliation_string | The Roy J. Carver Department of Biomedical Engineering, The University of Iowa, Iowa City, IA, 52246 |
| authorships[17].institutions[0].id | https://openalex.org/I126307644 |
| authorships[17].institutions[0].ror | https://ror.org/036jqmy94 |
| authorships[17].institutions[0].type | education |
| authorships[17].institutions[0].lineage | https://openalex.org/I126307644 |
| authorships[17].institutions[0].country_code | US |
| authorships[17].institutions[0].display_name | University of Iowa |
| authorships[17].author_position | middle |
| authorships[17].raw_author_name | Joseph M. Reinhardt |
| authorships[17].is_corresponding | False |
| authorships[17].raw_affiliation_strings | The Roy J. Carver Department of Biomedical Engineering, The University of Iowa, Iowa City, IA, 52246 |
| authorships[18].author.id | https://openalex.org/A5046399622 |
| authorships[18].author.orcid | https://orcid.org/0000-0002-8418-4497 |
| authorships[18].author.display_name | Surya P. Bhatt |
| authorships[18].countries | US |
| authorships[18].affiliations[0].institution_ids | https://openalex.org/I32389192 |
| authorships[18].affiliations[0].raw_affiliation_string | Division of Pulmonary, Allergy and Critical Care Medicine, The University of Alabama at Birmingham, Birmingham, AL, 5294 |
| authorships[18].institutions[0].id | https://openalex.org/I32389192 |
| authorships[18].institutions[0].ror | https://ror.org/008s83205 |
| authorships[18].institutions[0].type | education |
| authorships[18].institutions[0].lineage | https://openalex.org/I32389192 |
| authorships[18].institutions[0].country_code | US |
| authorships[18].institutions[0].display_name | University of Alabama at Birmingham |
| authorships[18].author_position | middle |
| authorships[18].raw_author_name | Surya P. Bhatt |
| authorships[18].is_corresponding | False |
| authorships[18].raw_affiliation_strings | Division of Pulmonary, Allergy and Critical Care Medicine, The University of Alabama at Birmingham, Birmingham, AL, 5294 |
| authorships[19].author.id | https://openalex.org/A5037794512 |
| authorships[19].author.orcid | https://orcid.org/0000-0001-8201-5262 |
| authorships[19].author.display_name | Sandeep Bodduluri |
| authorships[19].countries | US |
| authorships[19].affiliations[0].institution_ids | https://openalex.org/I32389192 |
| authorships[19].affiliations[0].raw_affiliation_string | Division of Pulmonary, Allergy and Critical Care Medicine, The University of Alabama at Birmingham, Birmingham, AL, 5294 |
| authorships[19].institutions[0].id | https://openalex.org/I32389192 |
| authorships[19].institutions[0].ror | https://ror.org/008s83205 |
| authorships[19].institutions[0].type | education |
| authorships[19].institutions[0].lineage | https://openalex.org/I32389192 |
| authorships[19].institutions[0].country_code | US |
| authorships[19].institutions[0].display_name | University of Alabama at Birmingham |
| authorships[19].author_position | last |
| authorships[19].raw_author_name | Sandeep Bodduluri |
| authorships[19].is_corresponding | False |
| authorships[19].raw_affiliation_strings | Division of Pulmonary, Allergy and Critical Care Medicine, The University of Alabama at Birmingham, Birmingham, AL, 5294 |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://www.medrxiv.org/content/medrxiv/early/2025/08/01/2025.07.30.25332459.full.pdf |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Light Convolutional Neural Network to Detect Chronic Obstructive Pulmonary Disease (COPDxNet): A Multicenter Model Development and External Validation Study |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10143 |
| primary_topic.field.id | https://openalex.org/fields/27 |
| primary_topic.field.display_name | Medicine |
| primary_topic.score | 0.9991000294685364 |
| primary_topic.domain.id | https://openalex.org/domains/4 |
| primary_topic.domain.display_name | Health Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2740 |
| primary_topic.subfield.display_name | Pulmonary and Respiratory Medicine |
| primary_topic.display_name | Chronic Obstructive Pulmonary Disease (COPD) Research |
| related_works | https://openalex.org/W2326585857, https://openalex.org/W142525846, https://openalex.org/W2312602415, https://openalex.org/W2312864656, https://openalex.org/W2472126364, https://openalex.org/W2320098543, https://openalex.org/W2585009645, https://openalex.org/W2333131755, https://openalex.org/W4401482945, https://openalex.org/W2372297656 |
| cited_by_count | 1 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 1 |
| locations_count | 3 |
| best_oa_location.id | doi:10.1101/2025.07.30.25332459 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4306402567 |
| best_oa_location.source.issn | |
| best_oa_location.source.type | repository |
| best_oa_location.source.is_oa | False |
| best_oa_location.source.issn_l | |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | bioRxiv (Cold Spring Harbor Laboratory) |
| best_oa_location.source.host_organization | https://openalex.org/I2750212522 |
| best_oa_location.source.host_organization_name | Cold Spring Harbor Laboratory |
| best_oa_location.source.host_organization_lineage | https://openalex.org/I2750212522 |
| best_oa_location.license | cc-by-nc-nd |
| best_oa_location.pdf_url | https://www.medrxiv.org/content/medrxiv/early/2025/08/01/2025.07.30.25332459.full.pdf |
| best_oa_location.version | acceptedVersion |
| best_oa_location.raw_type | posted-content |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by-nc-nd |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | https://doi.org/10.1101/2025.07.30.25332459 |
| primary_location.id | doi:10.1101/2025.07.30.25332459 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4306402567 |
| primary_location.source.issn | |
| primary_location.source.type | repository |
| primary_location.source.is_oa | False |
| primary_location.source.issn_l | |
| primary_location.source.is_core | False |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | bioRxiv (Cold Spring Harbor Laboratory) |
| primary_location.source.host_organization | https://openalex.org/I2750212522 |
| primary_location.source.host_organization_name | Cold Spring Harbor Laboratory |
| primary_location.source.host_organization_lineage | https://openalex.org/I2750212522 |
| primary_location.license | cc-by-nc-nd |
| primary_location.pdf_url | https://www.medrxiv.org/content/medrxiv/early/2025/08/01/2025.07.30.25332459.full.pdf |
| primary_location.version | acceptedVersion |
| primary_location.raw_type | posted-content |
| primary_location.license_id | https://openalex.org/licenses/cc-by-nc-nd |
| primary_location.is_accepted | True |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | https://doi.org/10.1101/2025.07.30.25332459 |
| publication_date | 2025-08-01 |
| publication_year | 2025 |
| referenced_works | https://openalex.org/W3096402359, https://openalex.org/W4220761888, https://openalex.org/W3033438356, https://openalex.org/W2898925453, https://openalex.org/W4389068173, https://openalex.org/W3090626718, https://openalex.org/W4205701164, https://openalex.org/W2898753907, https://openalex.org/W4293571173, https://openalex.org/W1964248798, https://openalex.org/W2971828568, https://openalex.org/W2151212842, https://openalex.org/W2170260757, https://openalex.org/W2754103191, https://openalex.org/W3017504127, https://openalex.org/W2122590281, https://openalex.org/W4381245382, https://openalex.org/W2082907106, https://openalex.org/W2106671991, https://openalex.org/W4394857110, https://openalex.org/W2896441775, https://openalex.org/W2120397327, https://openalex.org/W2524508118, https://openalex.org/W1213336605, https://openalex.org/W2150780222, https://openalex.org/W2626967530, https://openalex.org/W3118630706, https://openalex.org/W3008375417, https://openalex.org/W3118503369, https://openalex.org/W3134447409, https://openalex.org/W3133859228, https://openalex.org/W2733550378, https://openalex.org/W3153372042, https://openalex.org/W3155567600, https://openalex.org/W2136039969, https://openalex.org/W4393899383, https://openalex.org/W1951292309, https://openalex.org/W2010279428, https://openalex.org/W4225307047, https://openalex.org/W4220817505 |
| referenced_works_count | 40 |
| abstract_inverted_index.a | 42, 109 |
| abstract_inverted_index.CT | 53, 64, 136, 144, 180, 259, 286 |
| abstract_inverted_index.On | 177, 200 |
| abstract_inverted_index.We | 40, 59, 107, 156 |
| abstract_inverted_index.an | 184, 223 |
| abstract_inverted_index.at | 87 |
| abstract_inverted_index.be | 28 |
| abstract_inverted_index.by | 101 |
| abstract_inverted_index.in | 24, 149, 232, 261, 264, 268 |
| abstract_inverted_index.it | 221 |
| abstract_inverted_index.no | 92 |
| abstract_inverted_index.of | 4, 186, 194, 198, 225, 250 |
| abstract_inverted_index.on | 117, 122, 241, 281 |
| abstract_inverted_index.to | 30, 55, 91, 95, 191, 209, 217, 230, 239 |
| abstract_inverted_index.we | 77 |
| abstract_inverted_index.70% | 3 |
| abstract_inverted_index.AUC | 185, 203, 224 |
| abstract_inverted_index.CI: | 189, 207, 228, 237 |
| abstract_inverted_index.FEV | 103 |
| abstract_inverted_index.The | 243 |
| abstract_inverted_index.and | 72, 83, 120, 126, 142, 173, 196, 234, 255, 266, 276, 283, 293 |
| abstract_inverted_index.for | 252, 257, 278, 291 |
| abstract_inverted_index.its | 289 |
| abstract_inverted_index.may | 27 |
| abstract_inverted_index.the | 67, 88, 123, 150, 160, 163, 212 |
| abstract_inverted_index.was | 99, 115, 132, 204, 215, 245 |
| abstract_inverted_index.< | 105 |
| abstract_inverted_index.(95% | 188, 206, 227, 236 |
| abstract_inverted_index.(CT) | 20 |
| abstract_inverted_index./FVC | 104 |
| abstract_inverted_index.0.11 | 251 |
| abstract_inverted_index.0.12 | 263 |
| abstract_inverted_index.0.13 | 256 |
| abstract_inverted_index.0.17 | 267 |
| abstract_inverted_index.0.81 | 238 |
| abstract_inverted_index.0.82 | 235 |
| abstract_inverted_index.0.86 | 208 |
| abstract_inverted_index.0.88 | 205 |
| abstract_inverted_index.0.91 | 190, 229 |
| abstract_inverted_index.0.92 | 187, 226 |
| abstract_inverted_index.COPD | 32, 98, 280 |
| abstract_inverted_index.Lung | 152 |
| abstract_inverted_index.When | 211 |
| abstract_inverted_index.area | 161 |
| abstract_inverted_index.both | 96 |
| abstract_inverted_index.data | 119 |
| abstract_inverted_index.dose | 254 |
| abstract_inverted_index.from | 66, 138, 146 |
| abstract_inverted_index.high | 273 |
| abstract_inverted_index.into | 80 |
| abstract_inverted_index.sets | 86 |
| abstract_inverted_index.test | 84, 128 |
| abstract_inverted_index.that | 48, 114 |
| abstract_inverted_index.used | 29 |
| abstract_inverted_index.with | 6, 247 |
| abstract_inverted_index.(30%) | 85 |
| abstract_inverted_index.(70%) | 82 |
| abstract_inverted_index.0.70. | 106 |
| abstract_inverted_index.0.83) | 240 |
| abstract_inverted_index.0.93) | 231 |
| abstract_inverted_index.2,890 | 139 |
| abstract_inverted_index.3,368 | 73 |
| abstract_inverted_index.7,893 | 147 |
| abstract_inverted_index.Brier | 171, 248 |
| abstract_inverted_index.COPD. | 57 |
| abstract_inverted_index.NLST. | 242, 269 |
| abstract_inverted_index.Trial | 154 |
| abstract_inverted_index.chest | 17, 52, 63, 285 |
| abstract_inverted_index.curve | 167 |
| abstract_inverted_index.level | 90 |
| abstract_inverted_index.model | 113, 214, 244 |
| abstract_inverted_index.scans | 54, 65, 137, 145, 260 |
| abstract_inverted_index.sets. | 97, 129 |
| abstract_inverted_index.split | 79 |
| abstract_inverted_index.under | 162 |
| abstract_inverted_index.using | 16, 134, 159 |
| abstract_inverted_index.which | 76 |
| abstract_inverted_index.(9,675 | 70 |
| abstract_inverted_index.(AUC), | 168 |
| abstract_inverted_index.(COPD) | 11 |
| abstract_inverted_index.0.90). | 210 |
| abstract_inverted_index.0.93), | 192 |
| abstract_inverted_index.13,043 | 61 |
| abstract_inverted_index.80.2%, | 195 |
| abstract_inverted_index.89.4%. | 199 |
| abstract_inverted_index.across | 296 |
| abstract_inverted_index.adults | 5 |
| abstract_inverted_index.detect | 56 |
| abstract_inverted_index.neural | 45 |
| abstract_inverted_index.pooled | 118 |
| abstract_inverted_index.remain | 12 |
| abstract_inverted_index.scans, | 21, 181, 202, 287 |
| abstract_inverted_index.scores | 249 |
| abstract_inverted_index.showed | 222 |
| abstract_inverted_index.(NLST). | 155 |
| abstract_inverted_index.Methods | 58 |
| abstract_inverted_index.applied | 216 |
| abstract_inverted_index.chronic | 7 |
| abstract_inverted_index.curves. | 175 |
| abstract_inverted_index.defined | 100 |
| abstract_inverted_index.disease | 10 |
| abstract_inverted_index.diverse | 297 |
| abstract_inverted_index.improve | 31 |
| abstract_inverted_index.models. | 39 |
| abstract_inverted_index.network | 46 |
| abstract_inverted_index.scans), | 75 |
| abstract_inverted_index.scores, | 172 |
| abstract_inverted_index.simple, | 35, 110 |
| abstract_inverted_index.through | 34 |
| abstract_inverted_index.trained | 116 |
| abstract_inverted_index.ABSTRACT | 0 |
| abstract_inverted_index.COPDGene | 68, 178 |
| abstract_inverted_index.COPDxNet | 182, 213, 271 |
| abstract_inverted_index.External | 130 |
| abstract_inverted_index.Findings | 176 |
| abstract_inverted_index.National | 151 |
| abstract_inverted_index.accuracy | 275 |
| abstract_inverted_index.achieved | 183 |
| abstract_inverted_index.acquired | 23 |
| abstract_inverted_index.analyzed | 60 |
| abstract_inverted_index.clinical | 25, 292 |
| abstract_inverted_index.commonly | 22 |
| abstract_inverted_index.computed | 18 |
| abstract_inverted_index.external | 218 |
| abstract_inverted_index.held-out | 124 |
| abstract_inverted_index.low-dose | 74, 127, 143, 201, 258, 284 |
| abstract_inverted_index.randomly | 78 |
| abstract_inverted_index.receiver | 164 |
| abstract_inverted_index.training | 81 |
| abstract_inverted_index.utilizes | 49 |
| abstract_inverted_index.COPDGene, | 262 |
| abstract_inverted_index.SPIROMICS | 140, 233 |
| abstract_inverted_index.Screening | 153 |
| abstract_inverted_index.datasets, | 220 |
| abstract_inverted_index.detecting | 279 |
| abstract_inverted_index.detection | 33 |
| abstract_inverted_index.developed | 41 |
| abstract_inverted_index.evaluated | 157 |
| abstract_inverted_index.minimally | 50 |
| abstract_inverted_index.operating | 165 |
| abstract_inverted_index.performed | 133 |
| abstract_inverted_index.potential | 290 |
| abstract_inverted_index.practice, | 26 |
| abstract_inverted_index.processed | 51 |
| abstract_inverted_index.pulmonary | 9 |
| abstract_inverted_index.screening | 15, 294 |
| abstract_inverted_index.standard- | 125, 253, 282 |
| abstract_inverted_index.validated | 121 |
| abstract_inverted_index.(COPDxNet) | 47 |
| abstract_inverted_index.Background | 1 |
| abstract_inverted_index.SPIROMICS, | 265 |
| abstract_inverted_index.applicable | 37 |
| abstract_inverted_index.clinically | 36 |
| abstract_inverted_index.four-block | 111 |
| abstract_inverted_index.individual | 93 |
| abstract_inverted_index.supporting | 288 |
| abstract_inverted_index.tomography | 19 |
| abstract_inverted_index.validation | 131, 219 |
| abstract_inverted_index.calibration | 174 |
| abstract_inverted_index.constructed | 108 |
| abstract_inverted_index.contributed | 94 |
| abstract_inverted_index.inspiratory | 62 |
| abstract_inverted_index.obstructive | 8 |
| abstract_inverted_index.participant | 89 |
| abstract_inverted_index.performance | 158 |
| abstract_inverted_index.sensitivity | 193 |
| abstract_inverted_index.specificity | 197 |
| abstract_inverted_index.applications | 295 |
| abstract_inverted_index.demonstrates | 272 |
| abstract_inverted_index.lightweight, | 43 |
| abstract_inverted_index.participants | 141, 148 |
| abstract_inverted_index.populations. | 298 |
| abstract_inverted_index.sensitivity, | 169 |
| abstract_inverted_index.specificity, | 170 |
| abstract_inverted_index.undiagnosed. | 13 |
| abstract_inverted_index.Approximately | 2 |
| abstract_inverted_index.Opportunistic | 14 |
| abstract_inverted_index.convolutional | 44, 112 |
| abstract_inverted_index.deep-learning | 38 |
| abstract_inverted_index.participants, | 69 |
| abstract_inverted_index.standard-dose | 71, 135, 179 |
| abstract_inverted_index.Interpretation | 270 |
| abstract_inverted_index.characteristic | 166 |
| abstract_inverted_index.discriminative | 274 |
| abstract_inverted_index.generalizability | 277 |
| abstract_inverted_index.well-calibrated, | 246 |
| abstract_inverted_index.postbronchodilator | 102 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 20 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.5299999713897705 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile.value | 0.90461633 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |