Efficacy of an Automated Pulmonary Embolism (PE) Detection Algorithm on Routine Contrast-Enhanced Chest CT Imaging for Non-PE Studies Article Swipe
Hayden Troutt
,
Kenneth N. Huynh
,
Aditya Joshi
,
Ling Jiang
,
Scott Refugio
,
Scott D. Cramer
,
J. A. Lopez
,
Katherine Wei
,
Amir Imanzadeh
,
Daniel Chow
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1007/s10278-025-01552-0
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1007/s10278-025-01552-0
The urgency to accelerate PE management and minimize patient risk has driven the development of artificial intelligence (AI) algorithms designed to provide a swift and accurate diagnosis in dedicated chest imaging (computed tomography pulmonary angiogram; CTPA) for suspected PE; however, the accuracy of AI algorithms in the detection of incidental PE in non-dedicated CT imaging studies remains unclear and untested. This study explores the potential for a commercial AI algorithm to identify incidental PE in non-dedicated contrast-enhanced CT chest imaging studies. The Viz PE algorithm was deployed to identify the presence of PE on 130 dedicated and 63 non-dedicated contrast-enhanced CT chest exams. The predictions for non-dedicated contrast-enhanced chest CT imaging studies were 90.48% accurate, with a sensitivity of 0.14 and specificity of 1.00. Our findings reflect that the Viz PE algorithm demonstrated an overall accuracy of 90.16%, with a specificity of 96% and a sensitivity of 41%. Although the high specificity is promising for ruling in PE, the low sensitivity highlights a limitation, as it indicates the algorithm may miss a substantial number of true-positive incidental PEs. This study demonstrates that commercial AI detection tools hold promise as integral support for detecting PE, particularly when there is a strong clinical indication for their use; however, current limitations in sensitivity, especially for incidental cases, underscore the need for ongoing radiologist oversight.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1007/s10278-025-01552-0
- https://link.springer.com/content/pdf/10.1007/s10278-025-01552-0.pdf
- OA Status
- hybrid
- Cited By
- 1
- References
- 25
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4411643431
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4411643431Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1007/s10278-025-01552-0Digital Object Identifier
- Title
-
Efficacy of an Automated Pulmonary Embolism (PE) Detection Algorithm on Routine Contrast-Enhanced Chest CT Imaging for Non-PE StudiesWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-06-25Full publication date if available
- Authors
-
Hayden Troutt, Kenneth N. Huynh, Aditya Joshi, Ling Jiang, Scott Refugio, Scott D. Cramer, J. A. Lopez, Katherine Wei, Amir Imanzadeh, Daniel ChowList of authors in order
- Landing page
-
https://doi.org/10.1007/s10278-025-01552-0Publisher landing page
- PDF URL
-
https://link.springer.com/content/pdf/10.1007/s10278-025-01552-0.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
-
https://link.springer.com/content/pdf/10.1007/s10278-025-01552-0.pdfDirect OA link when available
- Concepts
-
Medicine, Pulmonary embolism, Radiology, Contrast (vision), Algorithm, Computed tomography, Diagnostic accuracy, Artificial intelligence, Computer science, Internal medicineTop 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)
-
25Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4411643431 |
|---|---|
| doi | https://doi.org/10.1007/s10278-025-01552-0 |
| ids.doi | https://doi.org/10.1007/s10278-025-01552-0 |
| ids.pmid | https://pubmed.ncbi.nlm.nih.gov/40563035 |
| ids.openalex | https://openalex.org/W4411643431 |
| fwci | 6.1422926 |
| type | article |
| title | Efficacy of an Automated Pulmonary Embolism (PE) Detection Algorithm on Routine Contrast-Enhanced Chest CT Imaging for Non-PE Studies |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T10285 |
| topics[0].field.id | https://openalex.org/fields/27 |
| topics[0].field.display_name | Medicine |
| topics[0].score | 0.9998000264167786 |
| topics[0].domain.id | https://openalex.org/domains/4 |
| topics[0].domain.display_name | Health Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2724 |
| topics[0].subfield.display_name | Internal Medicine |
| topics[0].display_name | Venous Thromboembolism Diagnosis and Management |
| topics[1].id | https://openalex.org/T10227 |
| topics[1].field.id | https://openalex.org/fields/27 |
| topics[1].field.display_name | Medicine |
| topics[1].score | 0.9894999861717224 |
| topics[1].domain.id | https://openalex.org/domains/4 |
| topics[1].domain.display_name | Health Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2713 |
| topics[1].subfield.display_name | Epidemiology |
| topics[1].display_name | Acute Ischemic Stroke Management |
| topics[2].id | https://openalex.org/T10844 |
| topics[2].field.id | https://openalex.org/fields/27 |
| topics[2].field.display_name | Medicine |
| topics[2].score | 0.9787999987602234 |
| topics[2].domain.id | https://openalex.org/domains/4 |
| topics[2].domain.display_name | Health Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2741 |
| topics[2].subfield.display_name | Radiology, Nuclear Medicine and Imaging |
| topics[2].display_name | Radiation Dose and Imaging |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C71924100 |
| concepts[0].level | 0 |
| concepts[0].score | 0.6827319860458374 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q11190 |
| concepts[0].display_name | Medicine |
| concepts[1].id | https://openalex.org/C2776265017 |
| concepts[1].level | 2 |
| concepts[1].score | 0.621281087398529 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q220570 |
| concepts[1].display_name | Pulmonary embolism |
| concepts[2].id | https://openalex.org/C126838900 |
| concepts[2].level | 1 |
| concepts[2].score | 0.5983358025550842 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q77604 |
| concepts[2].display_name | Radiology |
| concepts[3].id | https://openalex.org/C2776502983 |
| concepts[3].level | 2 |
| concepts[3].score | 0.5405096411705017 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q690182 |
| concepts[3].display_name | Contrast (vision) |
| concepts[4].id | https://openalex.org/C11413529 |
| concepts[4].level | 1 |
| concepts[4].score | 0.526648759841919 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q8366 |
| concepts[4].display_name | Algorithm |
| concepts[5].id | https://openalex.org/C544519230 |
| concepts[5].level | 2 |
| concepts[5].score | 0.44289931654930115 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q32566 |
| concepts[5].display_name | Computed tomography |
| concepts[6].id | https://openalex.org/C3020132585 |
| concepts[6].level | 2 |
| concepts[6].score | 0.43483132123947144 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q2671652 |
| concepts[6].display_name | Diagnostic accuracy |
| concepts[7].id | https://openalex.org/C154945302 |
| concepts[7].level | 1 |
| concepts[7].score | 0.3238504230976105 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[7].display_name | Artificial intelligence |
| concepts[8].id | https://openalex.org/C41008148 |
| concepts[8].level | 0 |
| concepts[8].score | 0.22697332501411438 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[8].display_name | Computer science |
| concepts[9].id | https://openalex.org/C126322002 |
| concepts[9].level | 1 |
| concepts[9].score | 0.10902470350265503 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q11180 |
| concepts[9].display_name | Internal medicine |
| keywords[0].id | https://openalex.org/keywords/medicine |
| keywords[0].score | 0.6827319860458374 |
| keywords[0].display_name | Medicine |
| keywords[1].id | https://openalex.org/keywords/pulmonary-embolism |
| keywords[1].score | 0.621281087398529 |
| keywords[1].display_name | Pulmonary embolism |
| keywords[2].id | https://openalex.org/keywords/radiology |
| keywords[2].score | 0.5983358025550842 |
| keywords[2].display_name | Radiology |
| keywords[3].id | https://openalex.org/keywords/contrast |
| keywords[3].score | 0.5405096411705017 |
| keywords[3].display_name | Contrast (vision) |
| keywords[4].id | https://openalex.org/keywords/algorithm |
| keywords[4].score | 0.526648759841919 |
| keywords[4].display_name | Algorithm |
| keywords[5].id | https://openalex.org/keywords/computed-tomography |
| keywords[5].score | 0.44289931654930115 |
| keywords[5].display_name | Computed tomography |
| keywords[6].id | https://openalex.org/keywords/diagnostic-accuracy |
| keywords[6].score | 0.43483132123947144 |
| keywords[6].display_name | Diagnostic accuracy |
| keywords[7].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[7].score | 0.3238504230976105 |
| keywords[7].display_name | Artificial intelligence |
| keywords[8].id | https://openalex.org/keywords/computer-science |
| keywords[8].score | 0.22697332501411438 |
| keywords[8].display_name | Computer science |
| keywords[9].id | https://openalex.org/keywords/internal-medicine |
| keywords[9].score | 0.10902470350265503 |
| keywords[9].display_name | Internal medicine |
| language | en |
| locations[0].id | doi:10.1007/s10278-025-01552-0 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4393920007 |
| locations[0].source.issn | 2948-2925, 2948-2933 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | False |
| locations[0].source.issn_l | 2948-2925 |
| locations[0].source.is_core | False |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | Journal of Imaging Informatics in Medicine |
| locations[0].source.host_organization | |
| locations[0].source.host_organization_name | |
| locations[0].license | cc-by |
| locations[0].pdf_url | https://link.springer.com/content/pdf/10.1007/s10278-025-01552-0.pdf |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Journal of Imaging Informatics in Medicine |
| locations[0].landing_page_url | https://doi.org/10.1007/s10278-025-01552-0 |
| locations[1].id | pmid:40563035 |
| 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 | Journal of imaging informatics in medicine |
| locations[1].landing_page_url | https://pubmed.ncbi.nlm.nih.gov/40563035 |
| indexed_in | crossref, pubmed |
| authorships[0].author.id | https://openalex.org/A5046161516 |
| authorships[0].author.orcid | https://orcid.org/0000-0003-1079-007X |
| authorships[0].author.display_name | Hayden Troutt |
| authorships[0].countries | US |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I4210137750 |
| authorships[0].affiliations[0].raw_affiliation_string | UCI Health, Irvine, CA, USA. |
| authorships[0].institutions[0].id | https://openalex.org/I4210137750 |
| authorships[0].institutions[0].ror | https://ror.org/03fgher32 |
| authorships[0].institutions[0].type | healthcare |
| authorships[0].institutions[0].lineage | https://openalex.org/I4210137750 |
| authorships[0].institutions[0].country_code | US |
| authorships[0].institutions[0].display_name | UC Irvine Health |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Hayden R Troutt |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | UCI Health, Irvine, CA, USA. |
| authorships[1].author.id | https://openalex.org/A5077006500 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-3179-3424 |
| authorships[1].author.display_name | Kenneth N. Huynh |
| authorships[1].countries | US |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I4210137750 |
| authorships[1].affiliations[0].raw_affiliation_string | UCI Health, Irvine, CA, USA. |
| authorships[1].institutions[0].id | https://openalex.org/I4210137750 |
| authorships[1].institutions[0].ror | https://ror.org/03fgher32 |
| authorships[1].institutions[0].type | healthcare |
| authorships[1].institutions[0].lineage | https://openalex.org/I4210137750 |
| authorships[1].institutions[0].country_code | US |
| authorships[1].institutions[0].display_name | UC Irvine Health |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Kenneth N Huynh |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | UCI Health, Irvine, CA, USA. |
| authorships[2].author.id | https://openalex.org/A5059529586 |
| authorships[2].author.orcid | https://orcid.org/0000-0003-2200-9703 |
| authorships[2].author.display_name | Aditya Joshi |
| authorships[2].countries | US |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I4210137750 |
| authorships[2].affiliations[0].raw_affiliation_string | UCI Health, Irvine, CA, USA. |
| authorships[2].institutions[0].id | https://openalex.org/I4210137750 |
| authorships[2].institutions[0].ror | https://ror.org/03fgher32 |
| authorships[2].institutions[0].type | healthcare |
| authorships[2].institutions[0].lineage | https://openalex.org/I4210137750 |
| authorships[2].institutions[0].country_code | US |
| authorships[2].institutions[0].display_name | UC Irvine Health |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Aditya Joshi |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | UCI Health, Irvine, CA, USA. |
| authorships[3].author.id | https://openalex.org/A5049414098 |
| authorships[3].author.orcid | https://orcid.org/0000-0003-1464-5881 |
| authorships[3].author.display_name | Ling Jiang |
| authorships[3].countries | US |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I4210137750 |
| authorships[3].affiliations[0].raw_affiliation_string | UCI Health, Irvine, CA, USA. |
| authorships[3].institutions[0].id | https://openalex.org/I4210137750 |
| authorships[3].institutions[0].ror | https://ror.org/03fgher32 |
| authorships[3].institutions[0].type | healthcare |
| authorships[3].institutions[0].lineage | https://openalex.org/I4210137750 |
| authorships[3].institutions[0].country_code | US |
| authorships[3].institutions[0].display_name | UC Irvine Health |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Justin Ling |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | UCI Health, Irvine, CA, USA. |
| authorships[4].author.id | https://openalex.org/A5093842152 |
| authorships[4].author.orcid | https://orcid.org/0000-0002-9148-042X |
| authorships[4].author.display_name | Scott Refugio |
| authorships[4].countries | US |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I4210137750 |
| authorships[4].affiliations[0].raw_affiliation_string | UCI Health, Irvine, CA, USA. [email protected]. |
| authorships[4].institutions[0].id | https://openalex.org/I4210137750 |
| authorships[4].institutions[0].ror | https://ror.org/03fgher32 |
| authorships[4].institutions[0].type | healthcare |
| authorships[4].institutions[0].lineage | https://openalex.org/I4210137750 |
| authorships[4].institutions[0].country_code | US |
| authorships[4].institutions[0].display_name | UC Irvine Health |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Scott Refugio |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | UCI Health, Irvine, CA, USA. [email protected]. |
| authorships[5].author.id | https://openalex.org/A5110188082 |
| authorships[5].author.orcid | |
| authorships[5].author.display_name | Scott D. Cramer |
| authorships[5].countries | US |
| authorships[5].affiliations[0].institution_ids | https://openalex.org/I4210137750 |
| authorships[5].affiliations[0].raw_affiliation_string | UCI Health, Irvine, CA, USA. |
| authorships[5].institutions[0].id | https://openalex.org/I4210137750 |
| authorships[5].institutions[0].ror | https://ror.org/03fgher32 |
| authorships[5].institutions[0].type | healthcare |
| authorships[5].institutions[0].lineage | https://openalex.org/I4210137750 |
| authorships[5].institutions[0].country_code | US |
| authorships[5].institutions[0].display_name | UC Irvine Health |
| authorships[5].author_position | middle |
| authorships[5].raw_author_name | Scott Cramer |
| authorships[5].is_corresponding | False |
| authorships[5].raw_affiliation_strings | UCI Health, Irvine, CA, USA. |
| authorships[6].author.id | https://openalex.org/A5109991381 |
| authorships[6].author.orcid | https://orcid.org/0000-0002-2764-192X |
| authorships[6].author.display_name | J. A. Lopez |
| authorships[6].countries | US |
| authorships[6].affiliations[0].institution_ids | https://openalex.org/I4210137750 |
| authorships[6].affiliations[0].raw_affiliation_string | UCI Health, Irvine, CA, USA. |
| authorships[6].institutions[0].id | https://openalex.org/I4210137750 |
| authorships[6].institutions[0].ror | https://ror.org/03fgher32 |
| authorships[6].institutions[0].type | healthcare |
| authorships[6].institutions[0].lineage | https://openalex.org/I4210137750 |
| authorships[6].institutions[0].country_code | US |
| authorships[6].institutions[0].display_name | UC Irvine Health |
| authorships[6].author_position | middle |
| authorships[6].raw_author_name | Jasmine Lopez |
| authorships[6].is_corresponding | False |
| authorships[6].raw_affiliation_strings | UCI Health, Irvine, CA, USA. |
| authorships[7].author.id | https://openalex.org/A5102613396 |
| authorships[7].author.orcid | |
| authorships[7].author.display_name | Katherine Wei |
| authorships[7].countries | US |
| authorships[7].affiliations[0].institution_ids | https://openalex.org/I4210137750 |
| authorships[7].affiliations[0].raw_affiliation_string | UCI Health, Irvine, CA, USA. |
| authorships[7].institutions[0].id | https://openalex.org/I4210137750 |
| authorships[7].institutions[0].ror | https://ror.org/03fgher32 |
| authorships[7].institutions[0].type | healthcare |
| authorships[7].institutions[0].lineage | https://openalex.org/I4210137750 |
| authorships[7].institutions[0].country_code | US |
| authorships[7].institutions[0].display_name | UC Irvine Health |
| authorships[7].author_position | middle |
| authorships[7].raw_author_name | Katherine Wei |
| authorships[7].is_corresponding | False |
| authorships[7].raw_affiliation_strings | UCI Health, Irvine, CA, USA. |
| authorships[8].author.id | https://openalex.org/A5062224621 |
| authorships[8].author.orcid | |
| authorships[8].author.display_name | Amir Imanzadeh |
| authorships[8].countries | US |
| authorships[8].affiliations[0].institution_ids | https://openalex.org/I4210137750 |
| authorships[8].affiliations[0].raw_affiliation_string | UCI Health, Irvine, CA, USA. |
| authorships[8].institutions[0].id | https://openalex.org/I4210137750 |
| authorships[8].institutions[0].ror | https://ror.org/03fgher32 |
| authorships[8].institutions[0].type | healthcare |
| authorships[8].institutions[0].lineage | https://openalex.org/I4210137750 |
| authorships[8].institutions[0].country_code | US |
| authorships[8].institutions[0].display_name | UC Irvine Health |
| authorships[8].author_position | middle |
| authorships[8].raw_author_name | Amir Imanzadeh |
| authorships[8].is_corresponding | False |
| authorships[8].raw_affiliation_strings | UCI Health, Irvine, CA, USA. |
| authorships[9].author.id | https://openalex.org/A5001367440 |
| authorships[9].author.orcid | https://orcid.org/0000-0002-2359-7394 |
| authorships[9].author.display_name | Daniel Chow |
| authorships[9].countries | US |
| authorships[9].affiliations[0].institution_ids | https://openalex.org/I4210137750 |
| authorships[9].affiliations[0].raw_affiliation_string | UCI Health, Irvine, CA, USA. |
| authorships[9].institutions[0].id | https://openalex.org/I4210137750 |
| authorships[9].institutions[0].ror | https://ror.org/03fgher32 |
| authorships[9].institutions[0].type | healthcare |
| authorships[9].institutions[0].lineage | https://openalex.org/I4210137750 |
| authorships[9].institutions[0].country_code | US |
| authorships[9].institutions[0].display_name | UC Irvine Health |
| authorships[9].author_position | last |
| authorships[9].raw_author_name | Daniel S Chow |
| authorships[9].is_corresponding | False |
| authorships[9].raw_affiliation_strings | UCI Health, Irvine, CA, USA. |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://link.springer.com/content/pdf/10.1007/s10278-025-01552-0.pdf |
| open_access.oa_status | hybrid |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Efficacy of an Automated Pulmonary Embolism (PE) Detection Algorithm on Routine Contrast-Enhanced Chest CT Imaging for Non-PE Studies |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10285 |
| primary_topic.field.id | https://openalex.org/fields/27 |
| primary_topic.field.display_name | Medicine |
| primary_topic.score | 0.9998000264167786 |
| primary_topic.domain.id | https://openalex.org/domains/4 |
| primary_topic.domain.display_name | Health Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2724 |
| primary_topic.subfield.display_name | Internal Medicine |
| primary_topic.display_name | Venous Thromboembolism Diagnosis and Management |
| related_works | https://openalex.org/W2075740387, https://openalex.org/W2069592018, https://openalex.org/W2358990940, https://openalex.org/W2022759977, https://openalex.org/W2093931120, https://openalex.org/W4300123192, https://openalex.org/W2329812990, https://openalex.org/W2403684205, https://openalex.org/W2167167265, https://openalex.org/W4285163831 |
| cited_by_count | 1 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 1 |
| locations_count | 2 |
| best_oa_location.id | doi:10.1007/s10278-025-01552-0 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4393920007 |
| best_oa_location.source.issn | 2948-2925, 2948-2933 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | False |
| best_oa_location.source.issn_l | 2948-2925 |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | Journal of Imaging Informatics in Medicine |
| best_oa_location.source.host_organization | |
| best_oa_location.source.host_organization_name | |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | https://link.springer.com/content/pdf/10.1007/s10278-025-01552-0.pdf |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | Journal of Imaging Informatics in Medicine |
| best_oa_location.landing_page_url | https://doi.org/10.1007/s10278-025-01552-0 |
| primary_location.id | doi:10.1007/s10278-025-01552-0 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4393920007 |
| primary_location.source.issn | 2948-2925, 2948-2933 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | False |
| primary_location.source.issn_l | 2948-2925 |
| primary_location.source.is_core | False |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | Journal of Imaging Informatics in Medicine |
| primary_location.source.host_organization | |
| primary_location.source.host_organization_name | |
| primary_location.license | cc-by |
| primary_location.pdf_url | https://link.springer.com/content/pdf/10.1007/s10278-025-01552-0.pdf |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Journal of Imaging Informatics in Medicine |
| primary_location.landing_page_url | https://doi.org/10.1007/s10278-025-01552-0 |
| publication_date | 2025-06-25 |
| publication_year | 2025 |
| referenced_works | https://openalex.org/W4362577737, https://openalex.org/W2081682917, https://openalex.org/W4389482917, https://openalex.org/W2897158139, https://openalex.org/W2919999151, https://openalex.org/W4293103527, https://openalex.org/W4385993015, https://openalex.org/W4220661517, https://openalex.org/W4292247201, https://openalex.org/W3133109968, https://openalex.org/W3164896153, https://openalex.org/W4220777473, https://openalex.org/W4390140057, https://openalex.org/W4285391660, https://openalex.org/W4390501583, https://openalex.org/W4321460093, https://openalex.org/W4403650812, https://openalex.org/W3144842570, https://openalex.org/W2891492187, https://openalex.org/W3112990870, https://openalex.org/W3039113409, https://openalex.org/W4401822123, https://openalex.org/W3204277253, https://openalex.org/W4244665703, https://openalex.org/W4387296834 |
| referenced_works_count | 25 |
| abstract_inverted_index.a | 23, 67, 117, 140, 145, 163, 172, 199 |
| abstract_inverted_index.63 | 98 |
| abstract_inverted_index.AI | 44, 69, 184 |
| abstract_inverted_index.CT | 54, 78, 101, 110 |
| abstract_inverted_index.PE | 5, 51, 74, 84, 93, 131 |
| abstract_inverted_index.an | 134 |
| abstract_inverted_index.as | 165, 189 |
| abstract_inverted_index.in | 28, 46, 52, 75, 157, 209 |
| abstract_inverted_index.is | 153, 198 |
| abstract_inverted_index.it | 166 |
| abstract_inverted_index.of | 15, 43, 49, 92, 119, 123, 137, 142, 147, 175 |
| abstract_inverted_index.on | 94 |
| abstract_inverted_index.to | 3, 21, 71, 88 |
| abstract_inverted_index.130 | 95 |
| abstract_inverted_index.96% | 143 |
| abstract_inverted_index.Our | 125 |
| abstract_inverted_index.PE, | 158, 194 |
| abstract_inverted_index.PE; | 39 |
| abstract_inverted_index.The | 1, 82, 104 |
| abstract_inverted_index.Viz | 83, 130 |
| abstract_inverted_index.and | 7, 25, 59, 97, 121, 144 |
| abstract_inverted_index.for | 37, 66, 106, 155, 192, 203, 212, 218 |
| abstract_inverted_index.has | 11 |
| abstract_inverted_index.low | 160 |
| abstract_inverted_index.may | 170 |
| abstract_inverted_index.the | 13, 41, 47, 64, 90, 129, 150, 159, 168, 216 |
| abstract_inverted_index.was | 86 |
| abstract_inverted_index.(AI) | 18 |
| abstract_inverted_index.0.14 | 120 |
| abstract_inverted_index.41%. | 148 |
| abstract_inverted_index.PEs. | 178 |
| abstract_inverted_index.This | 61, 179 |
| abstract_inverted_index.high | 151 |
| abstract_inverted_index.hold | 187 |
| abstract_inverted_index.miss | 171 |
| abstract_inverted_index.need | 217 |
| abstract_inverted_index.risk | 10 |
| abstract_inverted_index.that | 128, 182 |
| abstract_inverted_index.use; | 205 |
| abstract_inverted_index.were | 113 |
| abstract_inverted_index.when | 196 |
| abstract_inverted_index.with | 116, 139 |
| abstract_inverted_index.1.00. | 124 |
| abstract_inverted_index.CTPA) | 36 |
| abstract_inverted_index.chest | 30, 79, 102, 109 |
| abstract_inverted_index.study | 62, 180 |
| abstract_inverted_index.swift | 24 |
| abstract_inverted_index.their | 204 |
| abstract_inverted_index.there | 197 |
| abstract_inverted_index.tools | 186 |
| abstract_inverted_index.90.48% | 114 |
| abstract_inverted_index.cases, | 214 |
| abstract_inverted_index.driven | 12 |
| abstract_inverted_index.exams. | 103 |
| abstract_inverted_index.number | 174 |
| abstract_inverted_index.ruling | 156 |
| abstract_inverted_index.strong | 200 |
| abstract_inverted_index.90.16%, | 138 |
| abstract_inverted_index.current | 207 |
| abstract_inverted_index.imaging | 31, 55, 80, 111 |
| abstract_inverted_index.ongoing | 219 |
| abstract_inverted_index.overall | 135 |
| abstract_inverted_index.patient | 9 |
| abstract_inverted_index.promise | 188 |
| abstract_inverted_index.provide | 22 |
| abstract_inverted_index.reflect | 127 |
| abstract_inverted_index.remains | 57 |
| abstract_inverted_index.studies | 56, 112 |
| abstract_inverted_index.support | 191 |
| abstract_inverted_index.unclear | 58 |
| abstract_inverted_index.urgency | 2 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.Although | 149 |
| abstract_inverted_index.accuracy | 42, 136 |
| abstract_inverted_index.accurate | 26 |
| abstract_inverted_index.clinical | 201 |
| abstract_inverted_index.deployed | 87 |
| abstract_inverted_index.designed | 20 |
| abstract_inverted_index.explores | 63 |
| abstract_inverted_index.findings | 126 |
| abstract_inverted_index.however, | 40, 206 |
| abstract_inverted_index.identify | 72, 89 |
| abstract_inverted_index.integral | 190 |
| abstract_inverted_index.minimize | 8 |
| abstract_inverted_index.presence | 91 |
| abstract_inverted_index.studies. | 81 |
| abstract_inverted_index.(computed | 32 |
| abstract_inverted_index.accurate, | 115 |
| abstract_inverted_index.algorithm | 70, 85, 132, 169 |
| abstract_inverted_index.dedicated | 29, 96 |
| abstract_inverted_index.detecting | 193 |
| abstract_inverted_index.detection | 48, 185 |
| abstract_inverted_index.diagnosis | 27 |
| abstract_inverted_index.indicates | 167 |
| abstract_inverted_index.potential | 65 |
| abstract_inverted_index.promising | 154 |
| abstract_inverted_index.pulmonary | 34 |
| abstract_inverted_index.suspected | 38 |
| abstract_inverted_index.untested. | 60 |
| abstract_inverted_index.accelerate | 4 |
| abstract_inverted_index.algorithms | 19, 45 |
| abstract_inverted_index.angiogram; | 35 |
| abstract_inverted_index.artificial | 16 |
| abstract_inverted_index.commercial | 68, 183 |
| abstract_inverted_index.especially | 211 |
| abstract_inverted_index.highlights | 162 |
| abstract_inverted_index.incidental | 50, 73, 177, 213 |
| abstract_inverted_index.indication | 202 |
| abstract_inverted_index.management | 6 |
| abstract_inverted_index.oversight. | 221 |
| abstract_inverted_index.tomography | 33 |
| abstract_inverted_index.underscore | 215 |
| abstract_inverted_index.development | 14 |
| abstract_inverted_index.limitation, | 164 |
| abstract_inverted_index.limitations | 208 |
| abstract_inverted_index.predictions | 105 |
| abstract_inverted_index.radiologist | 220 |
| abstract_inverted_index.sensitivity | 118, 146, 161 |
| abstract_inverted_index.specificity | 122, 141, 152 |
| abstract_inverted_index.substantial | 173 |
| abstract_inverted_index.demonstrated | 133 |
| abstract_inverted_index.demonstrates | 181 |
| abstract_inverted_index.intelligence | 17 |
| abstract_inverted_index.particularly | 195 |
| abstract_inverted_index.sensitivity, | 210 |
| abstract_inverted_index.non-dedicated | 53, 76, 99, 107 |
| abstract_inverted_index.true-positive | 176 |
| abstract_inverted_index.contrast-enhanced | 77, 100, 108 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 10 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.6200000047683716 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile.value | 0.91600086 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |