Deep learning-based automated spine fracture type identification with Clinically validated GAN generated CT images Article Swipe
D N Sindhura
,
Radhika M. Pai
,
Shyamasunder N Bhat
,
M. M. Manohara Pai
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1080/23311916.2023.2295645
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1080/23311916.2023.2295645
Automatic type identification of sub-axial spine fractures is of prime importance for orthopaedicians to reduce image interpretation time and increase patient care time. But identifying fracture types is challenging due to imbalanced datasets. In this work, CT scan images of fractured spine has been collected from a Tertiary Care hospital and extended Deep Convolutional Generative Adversarial Network (DCGAN) architecture is developed for generating spine fracture images that overcomes the imbalanced dataset problem. These enhanced dataset are clinically evaluated with Two Visual Turing Tests (VTTs): the first test to “identify real and generated images” and second test to determine “type of fractures in the generated images.” The first VTT demonstrates that generated images of fractures are realistic and that even spine surgeons have difficulty in distinguishing them from real. The second VTT demonstrates that fracture lines are clearly visible in the generated images. The VTT results are analyzed using Fleiss Kappa statistical techniques to determine the inter-observer reliability of spine surgeons’ clinical evaluation. The results showed high interobserver agreement for “type identification” in the generated images. The clinically evaluated generated images are provided to the proposed ensemble based type identification model, which outperformed other models in type identification.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1080/23311916.2023.2295645
- https://www.tandfonline.com/doi/pdf/10.1080/23311916.2023.2295645?needAccess=true
- OA Status
- gold
- Cited By
- 4
- References
- 34
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4390953897
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4390953897Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1080/23311916.2023.2295645Digital Object Identifier
- Title
-
Deep learning-based automated spine fracture type identification with Clinically validated GAN generated CT imagesWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-01-16Full publication date if available
- Authors
-
D N Sindhura, Radhika M. Pai, Shyamasunder N Bhat, M. M. Manohara PaiList of authors in order
- Landing page
-
https://doi.org/10.1080/23311916.2023.2295645Publisher landing page
- PDF URL
-
https://www.tandfonline.com/doi/pdf/10.1080/23311916.2023.2295645?needAccess=trueDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.tandfonline.com/doi/pdf/10.1080/23311916.2023.2295645?needAccess=trueDirect OA link when available
- Concepts
-
Artificial intelligence, Identification (biology), Generative adversarial network, Computer science, Pattern recognition (psychology), Deep learning, Convolutional neural network, Computer vision, Reliability (semiconductor), Biology, Quantum mechanics, Physics, Botany, Power (physics)Top concepts (fields/topics) attached by OpenAlex
- Cited by
-
4Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2, 2024: 2Per-year citation counts (last 5 years)
- References (count)
-
34Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4390953897 |
|---|---|
| doi | https://doi.org/10.1080/23311916.2023.2295645 |
| ids.doi | https://doi.org/10.1080/23311916.2023.2295645 |
| ids.openalex | https://openalex.org/W4390953897 |
| fwci | 1.47010385 |
| type | article |
| title | Deep learning-based automated spine fracture type identification with Clinically validated GAN generated CT images |
| biblio.issue | 1 |
| biblio.volume | 11 |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T14510 |
| topics[0].field.id | https://openalex.org/fields/22 |
| topics[0].field.display_name | Engineering |
| topics[0].score | 0.9994999766349792 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2204 |
| topics[0].subfield.display_name | Biomedical Engineering |
| topics[0].display_name | Medical Imaging and Analysis |
| topics[1].id | https://openalex.org/T11775 |
| topics[1].field.id | https://openalex.org/fields/27 |
| topics[1].field.display_name | Medicine |
| topics[1].score | 0.9940000176429749 |
| topics[1].domain.id | https://openalex.org/domains/4 |
| topics[1].domain.display_name | Health Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2741 |
| topics[1].subfield.display_name | Radiology, Nuclear Medicine and Imaging |
| topics[1].display_name | COVID-19 diagnosis using AI |
| topics[2].id | https://openalex.org/T11894 |
| topics[2].field.id | https://openalex.org/fields/27 |
| topics[2].field.display_name | Medicine |
| topics[2].score | 0.973800003528595 |
| 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 | Radiology practices and education |
| is_xpac | False |
| apc_list.value | 950 |
| apc_list.currency | GBP |
| apc_list.value_usd | 1165 |
| apc_paid.value | 950 |
| apc_paid.currency | GBP |
| apc_paid.value_usd | 1165 |
| concepts[0].id | https://openalex.org/C154945302 |
| concepts[0].level | 1 |
| concepts[0].score | 0.7502487301826477 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[0].display_name | Artificial intelligence |
| concepts[1].id | https://openalex.org/C116834253 |
| concepts[1].level | 2 |
| concepts[1].score | 0.6502213478088379 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q2039217 |
| concepts[1].display_name | Identification (biology) |
| concepts[2].id | https://openalex.org/C2988773926 |
| concepts[2].level | 3 |
| concepts[2].score | 0.5287282466888428 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q25104379 |
| concepts[2].display_name | Generative adversarial network |
| concepts[3].id | https://openalex.org/C41008148 |
| concepts[3].level | 0 |
| concepts[3].score | 0.5254454016685486 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[3].display_name | Computer science |
| concepts[4].id | https://openalex.org/C153180895 |
| concepts[4].level | 2 |
| concepts[4].score | 0.5178971290588379 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q7148389 |
| concepts[4].display_name | Pattern recognition (psychology) |
| concepts[5].id | https://openalex.org/C108583219 |
| concepts[5].level | 2 |
| concepts[5].score | 0.49991726875305176 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q197536 |
| concepts[5].display_name | Deep learning |
| concepts[6].id | https://openalex.org/C81363708 |
| concepts[6].level | 2 |
| concepts[6].score | 0.4538586735725403 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q17084460 |
| concepts[6].display_name | Convolutional neural network |
| concepts[7].id | https://openalex.org/C31972630 |
| concepts[7].level | 1 |
| concepts[7].score | 0.4174361228942871 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q844240 |
| concepts[7].display_name | Computer vision |
| concepts[8].id | https://openalex.org/C43214815 |
| concepts[8].level | 3 |
| concepts[8].score | 0.41044795513153076 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q7310987 |
| concepts[8].display_name | Reliability (semiconductor) |
| concepts[9].id | https://openalex.org/C86803240 |
| concepts[9].level | 0 |
| concepts[9].score | 0.0 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q420 |
| concepts[9].display_name | Biology |
| concepts[10].id | https://openalex.org/C62520636 |
| concepts[10].level | 1 |
| concepts[10].score | 0.0 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q944 |
| concepts[10].display_name | Quantum mechanics |
| concepts[11].id | https://openalex.org/C121332964 |
| concepts[11].level | 0 |
| concepts[11].score | 0.0 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q413 |
| concepts[11].display_name | Physics |
| concepts[12].id | https://openalex.org/C59822182 |
| concepts[12].level | 1 |
| concepts[12].score | 0.0 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q441 |
| concepts[12].display_name | Botany |
| concepts[13].id | https://openalex.org/C163258240 |
| concepts[13].level | 2 |
| concepts[13].score | 0.0 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q25342 |
| concepts[13].display_name | Power (physics) |
| keywords[0].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[0].score | 0.7502487301826477 |
| keywords[0].display_name | Artificial intelligence |
| keywords[1].id | https://openalex.org/keywords/identification |
| keywords[1].score | 0.6502213478088379 |
| keywords[1].display_name | Identification (biology) |
| keywords[2].id | https://openalex.org/keywords/generative-adversarial-network |
| keywords[2].score | 0.5287282466888428 |
| keywords[2].display_name | Generative adversarial network |
| keywords[3].id | https://openalex.org/keywords/computer-science |
| keywords[3].score | 0.5254454016685486 |
| keywords[3].display_name | Computer science |
| keywords[4].id | https://openalex.org/keywords/pattern-recognition |
| keywords[4].score | 0.5178971290588379 |
| keywords[4].display_name | Pattern recognition (psychology) |
| keywords[5].id | https://openalex.org/keywords/deep-learning |
| keywords[5].score | 0.49991726875305176 |
| keywords[5].display_name | Deep learning |
| keywords[6].id | https://openalex.org/keywords/convolutional-neural-network |
| keywords[6].score | 0.4538586735725403 |
| keywords[6].display_name | Convolutional neural network |
| keywords[7].id | https://openalex.org/keywords/computer-vision |
| keywords[7].score | 0.4174361228942871 |
| keywords[7].display_name | Computer vision |
| keywords[8].id | https://openalex.org/keywords/reliability |
| keywords[8].score | 0.41044795513153076 |
| keywords[8].display_name | Reliability (semiconductor) |
| language | en |
| locations[0].id | doi:10.1080/23311916.2023.2295645 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S2764513175 |
| locations[0].source.issn | 2331-1916 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 2331-1916 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | True |
| locations[0].source.display_name | Cogent Engineering |
| locations[0].source.host_organization | https://openalex.org/P4310311767 |
| locations[0].source.host_organization_name | Cogent OA |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310311767 |
| locations[0].source.host_organization_lineage_names | Cogent OA |
| locations[0].license | cc-by |
| locations[0].pdf_url | https://www.tandfonline.com/doi/pdf/10.1080/23311916.2023.2295645?needAccess=true |
| 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 | Cogent Engineering |
| locations[0].landing_page_url | https://doi.org/10.1080/23311916.2023.2295645 |
| locations[1].id | pmh:oai:doaj.org/article:9b76f7b3c76f49f1a0dc8f4640142596 |
| locations[1].is_oa | False |
| locations[1].source.id | https://openalex.org/S4306401280 |
| 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 | DOAJ (DOAJ: Directory of Open Access Journals) |
| locations[1].source.host_organization | |
| locations[1].source.host_organization_name | |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | submittedVersion |
| locations[1].raw_type | article |
| locations[1].license_id | |
| locations[1].is_accepted | False |
| locations[1].is_published | False |
| locations[1].raw_source_name | Cogent Engineering, Vol 11, Iss 1 (2024) |
| locations[1].landing_page_url | https://doaj.org/article/9b76f7b3c76f49f1a0dc8f4640142596 |
| indexed_in | crossref, doaj |
| authorships[0].author.id | https://openalex.org/A5071646904 |
| authorships[0].author.orcid | https://orcid.org/0000-0001-9358-9165 |
| authorships[0].author.display_name | D N Sindhura |
| authorships[0].countries | IN |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I164861460 |
| authorships[0].affiliations[0].raw_affiliation_string | Department of Information and Communication Technology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India |
| authorships[0].institutions[0].id | https://openalex.org/I164861460 |
| authorships[0].institutions[0].ror | https://ror.org/02xzytt36 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I164861460 |
| authorships[0].institutions[0].country_code | IN |
| authorships[0].institutions[0].display_name | Manipal Academy of Higher Education |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Sindhura D. N. |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Department of Information and Communication Technology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India |
| authorships[1].author.id | https://openalex.org/A5000742553 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-0916-0495 |
| authorships[1].author.display_name | Radhika M. Pai |
| authorships[1].countries | IN |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I164861460 |
| authorships[1].affiliations[0].raw_affiliation_string | Department of Data Science and Computer Applications, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India |
| authorships[1].institutions[0].id | https://openalex.org/I164861460 |
| authorships[1].institutions[0].ror | https://ror.org/02xzytt36 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I164861460 |
| authorships[1].institutions[0].country_code | IN |
| authorships[1].institutions[0].display_name | Manipal Academy of Higher Education |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Radhika M. Pai |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Department of Data Science and Computer Applications, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India |
| authorships[2].author.id | https://openalex.org/A5022089203 |
| authorships[2].author.orcid | https://orcid.org/0000-0001-9545-4838 |
| authorships[2].author.display_name | Shyamasunder N Bhat |
| authorships[2].countries | IN |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I164861460, https://openalex.org/I76414455 |
| authorships[2].affiliations[0].raw_affiliation_string | Department of Orthopaeadics Kasturba Medical College, Manipal, Manipal Academy of Higher Education, Manipal, India |
| authorships[2].institutions[0].id | https://openalex.org/I76414455 |
| authorships[2].institutions[0].ror | https://ror.org/05hg48t65 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I164861460, https://openalex.org/I76414455 |
| authorships[2].institutions[0].country_code | IN |
| authorships[2].institutions[0].display_name | Kasturba Medical College, Manipal |
| authorships[2].institutions[1].id | https://openalex.org/I164861460 |
| authorships[2].institutions[1].ror | https://ror.org/02xzytt36 |
| authorships[2].institutions[1].type | education |
| authorships[2].institutions[1].lineage | https://openalex.org/I164861460 |
| authorships[2].institutions[1].country_code | IN |
| authorships[2].institutions[1].display_name | Manipal Academy of Higher Education |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Shyamasunder N. Bhat |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Department of Orthopaeadics Kasturba Medical College, Manipal, Manipal Academy of Higher Education, Manipal, India |
| authorships[3].author.id | https://openalex.org/A5056139480 |
| authorships[3].author.orcid | https://orcid.org/0000-0003-2164-2945 |
| authorships[3].author.display_name | M. M. Manohara Pai |
| authorships[3].countries | IN |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I164861460 |
| authorships[3].affiliations[0].raw_affiliation_string | Department of Information and Communication Technology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India |
| authorships[3].institutions[0].id | https://openalex.org/I164861460 |
| authorships[3].institutions[0].ror | https://ror.org/02xzytt36 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I164861460 |
| authorships[3].institutions[0].country_code | IN |
| authorships[3].institutions[0].display_name | Manipal Academy of Higher Education |
| authorships[3].author_position | last |
| authorships[3].raw_author_name | Manohara Pai M. M. |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | Department of Information and Communication Technology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India |
| has_content.pdf | True |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://www.tandfonline.com/doi/pdf/10.1080/23311916.2023.2295645?needAccess=true |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Deep learning-based automated spine fracture type identification with Clinically validated GAN generated CT images |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T06:51:31.235846 |
| primary_topic.id | https://openalex.org/T14510 |
| primary_topic.field.id | https://openalex.org/fields/22 |
| primary_topic.field.display_name | Engineering |
| primary_topic.score | 0.9994999766349792 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2204 |
| primary_topic.subfield.display_name | Biomedical Engineering |
| primary_topic.display_name | Medical Imaging and Analysis |
| related_works | https://openalex.org/W4293226380, https://openalex.org/W4375867731, https://openalex.org/W2611989081, https://openalex.org/W4226493464, https://openalex.org/W4312417841, https://openalex.org/W3193565141, https://openalex.org/W3133861977, https://openalex.org/W3167935049, https://openalex.org/W3029198973, https://openalex.org/W3216455563 |
| cited_by_count | 4 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 2 |
| counts_by_year[1].year | 2024 |
| counts_by_year[1].cited_by_count | 2 |
| locations_count | 2 |
| best_oa_location.id | doi:10.1080/23311916.2023.2295645 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S2764513175 |
| best_oa_location.source.issn | 2331-1916 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 2331-1916 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | True |
| best_oa_location.source.display_name | Cogent Engineering |
| best_oa_location.source.host_organization | https://openalex.org/P4310311767 |
| best_oa_location.source.host_organization_name | Cogent OA |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310311767 |
| best_oa_location.source.host_organization_lineage_names | Cogent OA |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | https://www.tandfonline.com/doi/pdf/10.1080/23311916.2023.2295645?needAccess=true |
| 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 | Cogent Engineering |
| best_oa_location.landing_page_url | https://doi.org/10.1080/23311916.2023.2295645 |
| primary_location.id | doi:10.1080/23311916.2023.2295645 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S2764513175 |
| primary_location.source.issn | 2331-1916 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 2331-1916 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | Cogent Engineering |
| primary_location.source.host_organization | https://openalex.org/P4310311767 |
| primary_location.source.host_organization_name | Cogent OA |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310311767 |
| primary_location.source.host_organization_lineage_names | Cogent OA |
| primary_location.license | cc-by |
| primary_location.pdf_url | https://www.tandfonline.com/doi/pdf/10.1080/23311916.2023.2295645?needAccess=true |
| 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 | Cogent Engineering |
| primary_location.landing_page_url | https://doi.org/10.1080/23311916.2023.2295645 |
| publication_date | 2024-01-16 |
| publication_year | 2024 |
| referenced_works | https://openalex.org/W4281572218, https://openalex.org/W4205147541, https://openalex.org/W3013601031, https://openalex.org/W2935090763, https://openalex.org/W2990410559, https://openalex.org/W2964261464, https://openalex.org/W2053154970, https://openalex.org/W2944407548, https://openalex.org/W2963942586, https://openalex.org/W2794022343, https://openalex.org/W2988141759, https://openalex.org/W3096831136, https://openalex.org/W4200582787, https://openalex.org/W3110223342, https://openalex.org/W2806118840, https://openalex.org/W3215490101, https://openalex.org/W4365813716, https://openalex.org/W3025011581, https://openalex.org/W3084438349, https://openalex.org/W2982127434, https://openalex.org/W2886403459, https://openalex.org/W3027139020, https://openalex.org/W2940692219, https://openalex.org/W3163936961, https://openalex.org/W3182127358, https://openalex.org/W2901030517, https://openalex.org/W2323632593, https://openalex.org/W2811095288, https://openalex.org/W3113056502, https://openalex.org/W2023448396, https://openalex.org/W2890139949, https://openalex.org/W4200293404, https://openalex.org/W2896328393, https://openalex.org/W3015378265 |
| referenced_works_count | 34 |
| abstract_inverted_index.a | 46 |
| abstract_inverted_index.CT | 36 |
| abstract_inverted_index.In | 33 |
| abstract_inverted_index.in | 101, 123, 138, 171, 194 |
| abstract_inverted_index.is | 7, 27, 59 |
| abstract_inverted_index.of | 3, 8, 39, 99, 112, 157 |
| abstract_inverted_index.to | 13, 30, 87, 96, 152, 182 |
| abstract_inverted_index.But | 23 |
| abstract_inverted_index.The | 105, 128, 142, 162, 175 |
| abstract_inverted_index.Two | 79 |
| abstract_inverted_index.VTT | 107, 130, 143 |
| abstract_inverted_index.and | 18, 50, 90, 93, 116 |
| abstract_inverted_index.are | 75, 114, 135, 145, 180 |
| abstract_inverted_index.due | 29 |
| abstract_inverted_index.for | 11, 61, 168 |
| abstract_inverted_index.has | 42 |
| abstract_inverted_index.the | 68, 84, 102, 139, 154, 172, 183 |
| abstract_inverted_index.Care | 48 |
| abstract_inverted_index.Deep | 52 |
| abstract_inverted_index.been | 43 |
| abstract_inverted_index.care | 21 |
| abstract_inverted_index.even | 118 |
| abstract_inverted_index.from | 45, 126 |
| abstract_inverted_index.have | 121 |
| abstract_inverted_index.high | 165 |
| abstract_inverted_index.real | 89 |
| abstract_inverted_index.scan | 37 |
| abstract_inverted_index.test | 86, 95 |
| abstract_inverted_index.that | 66, 109, 117, 132 |
| abstract_inverted_index.them | 125 |
| abstract_inverted_index.this | 34 |
| abstract_inverted_index.time | 17 |
| abstract_inverted_index.type | 1, 187, 195 |
| abstract_inverted_index.with | 78 |
| abstract_inverted_index.Kappa | 149 |
| abstract_inverted_index.Tests | 82 |
| abstract_inverted_index.These | 72 |
| abstract_inverted_index.based | 186 |
| abstract_inverted_index.first | 85, 106 |
| abstract_inverted_index.image | 15 |
| abstract_inverted_index.lines | 134 |
| abstract_inverted_index.other | 192 |
| abstract_inverted_index.prime | 9 |
| abstract_inverted_index.real. | 127 |
| abstract_inverted_index.spine | 5, 41, 63, 119, 158 |
| abstract_inverted_index.time. | 22 |
| abstract_inverted_index.types | 26 |
| abstract_inverted_index.using | 147 |
| abstract_inverted_index.which | 190 |
| abstract_inverted_index.work, | 35 |
| abstract_inverted_index.Fleiss | 148 |
| abstract_inverted_index.Turing | 81 |
| abstract_inverted_index.Visual | 80 |
| abstract_inverted_index.images | 38, 65, 111, 179 |
| abstract_inverted_index.model, | 189 |
| abstract_inverted_index.models | 193 |
| abstract_inverted_index.reduce | 14 |
| abstract_inverted_index.second | 94, 129 |
| abstract_inverted_index.showed | 164 |
| abstract_inverted_index.(DCGAN) | 57 |
| abstract_inverted_index.(VTTs): | 83 |
| abstract_inverted_index.Network | 56 |
| abstract_inverted_index.clearly | 136 |
| abstract_inverted_index.dataset | 70, 74 |
| abstract_inverted_index.images. | 141, 174 |
| abstract_inverted_index.patient | 20 |
| abstract_inverted_index.results | 144, 163 |
| abstract_inverted_index.visible | 137 |
| abstract_inverted_index.“type | 98, 169 |
| abstract_inverted_index.Tertiary | 47 |
| abstract_inverted_index.analyzed | 146 |
| abstract_inverted_index.clinical | 160 |
| abstract_inverted_index.enhanced | 73 |
| abstract_inverted_index.ensemble | 185 |
| abstract_inverted_index.extended | 51 |
| abstract_inverted_index.fracture | 25, 64, 133 |
| abstract_inverted_index.hospital | 49 |
| abstract_inverted_index.increase | 19 |
| abstract_inverted_index.problem. | 71 |
| abstract_inverted_index.proposed | 184 |
| abstract_inverted_index.provided | 181 |
| abstract_inverted_index.surgeons | 120 |
| abstract_inverted_index.agreement | 167 |
| abstract_inverted_index.collected | 44 |
| abstract_inverted_index.datasets. | 32 |
| abstract_inverted_index.determine | 97, 153 |
| abstract_inverted_index.developed | 60 |
| abstract_inverted_index.evaluated | 77, 177 |
| abstract_inverted_index.fractured | 40 |
| abstract_inverted_index.fractures | 6, 100, 113 |
| abstract_inverted_index.generated | 91, 103, 110, 140, 173, 178 |
| abstract_inverted_index.images” | 92 |
| abstract_inverted_index.overcomes | 67 |
| abstract_inverted_index.realistic | 115 |
| abstract_inverted_index.sub-axial | 4 |
| abstract_inverted_index.Generative | 54 |
| abstract_inverted_index.clinically | 76, 176 |
| abstract_inverted_index.difficulty | 122 |
| abstract_inverted_index.generating | 62 |
| abstract_inverted_index.images.” | 104 |
| abstract_inverted_index.imbalanced | 31, 69 |
| abstract_inverted_index.importance | 10 |
| abstract_inverted_index.techniques | 151 |
| abstract_inverted_index.Adversarial | 55 |
| abstract_inverted_index.challenging | 28 |
| abstract_inverted_index.evaluation. | 161 |
| abstract_inverted_index.identifying | 24 |
| abstract_inverted_index.reliability | 156 |
| abstract_inverted_index.statistical | 150 |
| abstract_inverted_index.surgeons’ | 159 |
| abstract_inverted_index.“identify | 88 |
| abstract_inverted_index.architecture | 58 |
| abstract_inverted_index.demonstrates | 108, 131 |
| abstract_inverted_index.outperformed | 191 |
| abstract_inverted_index.Convolutional | 53 |
| abstract_inverted_index.interobserver | 166 |
| abstract_inverted_index.distinguishing | 124 |
| abstract_inverted_index.identification | 2, 188 |
| abstract_inverted_index.inter-observer | 155 |
| abstract_inverted_index.interpretation | 16 |
| abstract_inverted_index.identification. | 196 |
| abstract_inverted_index.orthopaedicians | 12 |
| abstract_inverted_index.AbstractAutomatic | 0 |
| abstract_inverted_index.identification” | 170 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 94 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile.value | 0.71494461 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |