Exploring the Efficacy of Base Data Augmentation Methods in Deep Learning-Based Radiograph Classification of Knee Joint Osteoarthritis Article Swipe
Fabi Prezja
,
Leevi Annala
,
Sampsa Kiiskinen
,
Timo Ojala
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/a17010008
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/a17010008
Diagnosing knee joint osteoarthritis (KOA), a major cause of disability worldwide, is challenging due to subtle radiographic indicators and the varied progression of the disease. Using deep learning for KOA diagnosis requires broad, comprehensive datasets. However, obtaining these datasets poses significant challenges due to patient privacy and data collection restrictions. Additive data augmentation, which enhances data variability, emerges as a promising solution. Yet, it’s unclear which augmentation techniques are most effective for KOA. Our study explored data augmentation methods, including adversarial techniques. We used strategies like horizontal cropping and region of interest (ROI) extraction, alongside adversarial methods such as noise injection and ROI removal. Interestingly, rotations improved performance, while methods like horizontal split were less effective. We discovered potential confounding regions using adversarial augmentation, shown in our models’ accurate classification of extreme KOA grades, even without the knee joint. This indicated a potential model bias towards irrelevant radiographic features. Removing the knee joint paradoxically increased accuracy in classifying early-stage KOA. Grad-CAM visualizations helped elucidate these effects. Our study contributed to the field by pinpointing augmentation techniques that either improve or impede model performance, in addition to recognizing potential confounding regions within radiographic images of knee osteoarthritis.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/a17010008
- https://www.mdpi.com/1999-4893/17/1/8/pdf?version=1703403653
- OA Status
- gold
- Cited By
- 10
- References
- 59
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4390176466
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4390176466Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/a17010008Digital Object Identifier
- Title
-
Exploring the Efficacy of Base Data Augmentation Methods in Deep Learning-Based Radiograph Classification of Knee Joint OsteoarthritisWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-12-24Full publication date if available
- Authors
-
Fabi Prezja, Leevi Annala, Sampsa Kiiskinen, Timo OjalaList of authors in order
- Landing page
-
https://doi.org/10.3390/a17010008Publisher landing page
- PDF URL
-
https://www.mdpi.com/1999-4893/17/1/8/pdf?version=1703403653Direct 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.mdpi.com/1999-4893/17/1/8/pdf?version=1703403653Direct OA link when available
- Concepts
-
Osteoarthritis, Artificial intelligence, Confounding, Computer science, Radiography, Knee Joint, Joint (building), Region of interest, Deep learning, Adversarial system, Medicine, Machine learning, Radiology, Surgery, Pathology, Architectural engineering, Engineering, Alternative medicineTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
10Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 6, 2024: 4Per-year citation counts (last 5 years)
- References (count)
-
59Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4390176466 |
|---|---|
| doi | https://doi.org/10.3390/a17010008 |
| ids.doi | https://doi.org/10.3390/a17010008 |
| ids.openalex | https://openalex.org/W4390176466 |
| fwci | 5.03735602 |
| type | article |
| title | Exploring the Efficacy of Base Data Augmentation Methods in Deep Learning-Based Radiograph Classification of Knee Joint Osteoarthritis |
| biblio.issue | 1 |
| biblio.volume | 17 |
| biblio.last_page | 8 |
| biblio.first_page | 8 |
| topics[0].id | https://openalex.org/T11293 |
| topics[0].field.id | https://openalex.org/fields/27 |
| topics[0].field.display_name | Medicine |
| topics[0].score | 0.977400004863739 |
| topics[0].domain.id | https://openalex.org/domains/4 |
| topics[0].domain.display_name | Health Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2746 |
| topics[0].subfield.display_name | Surgery |
| topics[0].display_name | Orthopedic Infections and Treatments |
| topics[1].id | https://openalex.org/T10105 |
| topics[1].field.id | https://openalex.org/fields/27 |
| topics[1].field.display_name | Medicine |
| topics[1].score | 0.9591000080108643 |
| topics[1].domain.id | https://openalex.org/domains/4 |
| topics[1].domain.display_name | Health Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2745 |
| topics[1].subfield.display_name | Rheumatology |
| topics[1].display_name | Osteoarthritis Treatment and Mechanisms |
| topics[2].id | https://openalex.org/T14510 |
| topics[2].field.id | https://openalex.org/fields/22 |
| topics[2].field.display_name | Engineering |
| topics[2].score | 0.9401999711990356 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2204 |
| topics[2].subfield.display_name | Biomedical Engineering |
| topics[2].display_name | Medical Imaging and Analysis |
| is_xpac | False |
| apc_list.value | 1400 |
| apc_list.currency | CHF |
| apc_list.value_usd | 1515 |
| apc_paid.value | 1400 |
| apc_paid.currency | CHF |
| apc_paid.value_usd | 1515 |
| concepts[0].id | https://openalex.org/C2776164576 |
| concepts[0].level | 3 |
| concepts[0].score | 0.7974887490272522 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q62736 |
| concepts[0].display_name | Osteoarthritis |
| concepts[1].id | https://openalex.org/C154945302 |
| concepts[1].level | 1 |
| concepts[1].score | 0.6288869380950928 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[1].display_name | Artificial intelligence |
| concepts[2].id | https://openalex.org/C77350462 |
| concepts[2].level | 2 |
| concepts[2].score | 0.6039514541625977 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q1125472 |
| concepts[2].display_name | Confounding |
| concepts[3].id | https://openalex.org/C41008148 |
| concepts[3].level | 0 |
| concepts[3].score | 0.5638989210128784 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[3].display_name | Computer science |
| concepts[4].id | https://openalex.org/C36454342 |
| concepts[4].level | 2 |
| concepts[4].score | 0.5360064506530762 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q245341 |
| concepts[4].display_name | Radiography |
| concepts[5].id | https://openalex.org/C2908736133 |
| concepts[5].level | 2 |
| concepts[5].score | 0.5207183957099915 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q37425 |
| concepts[5].display_name | Knee Joint |
| concepts[6].id | https://openalex.org/C18555067 |
| concepts[6].level | 2 |
| concepts[6].score | 0.5129110813140869 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q8375051 |
| concepts[6].display_name | Joint (building) |
| concepts[7].id | https://openalex.org/C19609008 |
| concepts[7].level | 2 |
| concepts[7].score | 0.47069668769836426 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q2138203 |
| concepts[7].display_name | Region of interest |
| concepts[8].id | https://openalex.org/C108583219 |
| concepts[8].level | 2 |
| concepts[8].score | 0.4682534635066986 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q197536 |
| concepts[8].display_name | Deep learning |
| concepts[9].id | https://openalex.org/C37736160 |
| concepts[9].level | 2 |
| concepts[9].score | 0.42213261127471924 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q1801315 |
| concepts[9].display_name | Adversarial system |
| concepts[10].id | https://openalex.org/C71924100 |
| concepts[10].level | 0 |
| concepts[10].score | 0.37273329496383667 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q11190 |
| concepts[10].display_name | Medicine |
| concepts[11].id | https://openalex.org/C119857082 |
| concepts[11].level | 1 |
| concepts[11].score | 0.3275139331817627 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[11].display_name | Machine learning |
| concepts[12].id | https://openalex.org/C126838900 |
| concepts[12].level | 1 |
| concepts[12].score | 0.1787639856338501 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q77604 |
| concepts[12].display_name | Radiology |
| concepts[13].id | https://openalex.org/C141071460 |
| concepts[13].level | 1 |
| concepts[13].score | 0.10596290230751038 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q40821 |
| concepts[13].display_name | Surgery |
| concepts[14].id | https://openalex.org/C142724271 |
| concepts[14].level | 1 |
| concepts[14].score | 0.07789653539657593 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q7208 |
| concepts[14].display_name | Pathology |
| concepts[15].id | https://openalex.org/C170154142 |
| concepts[15].level | 1 |
| concepts[15].score | 0.0 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q150737 |
| concepts[15].display_name | Architectural engineering |
| concepts[16].id | https://openalex.org/C127413603 |
| concepts[16].level | 0 |
| concepts[16].score | 0.0 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q11023 |
| concepts[16].display_name | Engineering |
| concepts[17].id | https://openalex.org/C204787440 |
| concepts[17].level | 2 |
| concepts[17].score | 0.0 |
| concepts[17].wikidata | https://www.wikidata.org/wiki/Q188504 |
| concepts[17].display_name | Alternative medicine |
| keywords[0].id | https://openalex.org/keywords/osteoarthritis |
| keywords[0].score | 0.7974887490272522 |
| keywords[0].display_name | Osteoarthritis |
| keywords[1].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[1].score | 0.6288869380950928 |
| keywords[1].display_name | Artificial intelligence |
| keywords[2].id | https://openalex.org/keywords/confounding |
| keywords[2].score | 0.6039514541625977 |
| keywords[2].display_name | Confounding |
| keywords[3].id | https://openalex.org/keywords/computer-science |
| keywords[3].score | 0.5638989210128784 |
| keywords[3].display_name | Computer science |
| keywords[4].id | https://openalex.org/keywords/radiography |
| keywords[4].score | 0.5360064506530762 |
| keywords[4].display_name | Radiography |
| keywords[5].id | https://openalex.org/keywords/knee-joint |
| keywords[5].score | 0.5207183957099915 |
| keywords[5].display_name | Knee Joint |
| keywords[6].id | https://openalex.org/keywords/joint |
| keywords[6].score | 0.5129110813140869 |
| keywords[6].display_name | Joint (building) |
| keywords[7].id | https://openalex.org/keywords/region-of-interest |
| keywords[7].score | 0.47069668769836426 |
| keywords[7].display_name | Region of interest |
| keywords[8].id | https://openalex.org/keywords/deep-learning |
| keywords[8].score | 0.4682534635066986 |
| keywords[8].display_name | Deep learning |
| keywords[9].id | https://openalex.org/keywords/adversarial-system |
| keywords[9].score | 0.42213261127471924 |
| keywords[9].display_name | Adversarial system |
| keywords[10].id | https://openalex.org/keywords/medicine |
| keywords[10].score | 0.37273329496383667 |
| keywords[10].display_name | Medicine |
| keywords[11].id | https://openalex.org/keywords/machine-learning |
| keywords[11].score | 0.3275139331817627 |
| keywords[11].display_name | Machine learning |
| keywords[12].id | https://openalex.org/keywords/radiology |
| keywords[12].score | 0.1787639856338501 |
| keywords[12].display_name | Radiology |
| keywords[13].id | https://openalex.org/keywords/surgery |
| keywords[13].score | 0.10596290230751038 |
| keywords[13].display_name | Surgery |
| keywords[14].id | https://openalex.org/keywords/pathology |
| keywords[14].score | 0.07789653539657593 |
| keywords[14].display_name | Pathology |
| language | en |
| locations[0].id | doi:10.3390/a17010008 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S190629608 |
| locations[0].source.issn | 1999-4893 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 1999-4893 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | True |
| locations[0].source.display_name | Algorithms |
| locations[0].source.host_organization | https://openalex.org/P4310310987 |
| locations[0].source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310310987 |
| locations[0].source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| locations[0].license | cc-by |
| locations[0].pdf_url | https://www.mdpi.com/1999-4893/17/1/8/pdf?version=1703403653 |
| 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 | Algorithms |
| locations[0].landing_page_url | https://doi.org/10.3390/a17010008 |
| locations[1].id | pmh:oai:jyx.jyu.fi:123456789/92694 |
| locations[1].is_oa | True |
| locations[1].source.id | https://openalex.org/S4306400563 |
| 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 | Jyväskylä University Digital Archive (University of Jyväskylä) |
| locations[1].source.host_organization | https://openalex.org/I94722563 |
| locations[1].source.host_organization_name | University of Jyväskylä |
| locations[1].source.host_organization_lineage | https://openalex.org/I94722563 |
| locations[1].license | cc-by |
| locations[1].pdf_url | |
| locations[1].version | acceptedVersion |
| locations[1].raw_type | A1 |
| locations[1].license_id | https://openalex.org/licenses/cc-by |
| locations[1].is_accepted | True |
| locations[1].is_published | False |
| locations[1].raw_source_name | |
| locations[1].landing_page_url | http://urn.fi/URN:NBN:fi:jyu-202401111195 |
| locations[2].id | pmh:oai:doaj.org/article:41259ec513b443f1bea81299762a3af2 |
| locations[2].is_oa | False |
| locations[2].source.id | https://openalex.org/S4306401280 |
| 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 | DOAJ (DOAJ: Directory of Open Access Journals) |
| locations[2].source.host_organization | |
| locations[2].source.host_organization_name | |
| locations[2].license | |
| locations[2].pdf_url | |
| locations[2].version | submittedVersion |
| locations[2].raw_type | article |
| locations[2].license_id | |
| locations[2].is_accepted | False |
| locations[2].is_published | False |
| locations[2].raw_source_name | Algorithms, Vol 17, Iss 1, p 8 (2023) |
| locations[2].landing_page_url | https://doaj.org/article/41259ec513b443f1bea81299762a3af2 |
| indexed_in | crossref, doaj |
| authorships[0].author.id | https://openalex.org/A5045286306 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-5295-7192 |
| authorships[0].author.display_name | Fabi Prezja |
| authorships[0].countries | FI |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I94722563 |
| authorships[0].affiliations[0].raw_affiliation_string | Faculty of Information Technology, University of Jyväskylä, 40014 Jyväskylä, Finland |
| authorships[0].institutions[0].id | https://openalex.org/I94722563 |
| authorships[0].institutions[0].ror | https://ror.org/05n3dz165 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I94722563 |
| authorships[0].institutions[0].country_code | FI |
| authorships[0].institutions[0].display_name | University of Jyväskylä |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Fabi Prezja |
| authorships[0].is_corresponding | True |
| authorships[0].raw_affiliation_strings | Faculty of Information Technology, University of Jyväskylä, 40014 Jyväskylä, Finland |
| authorships[1].author.id | https://openalex.org/A5031244613 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-8297-8859 |
| authorships[1].author.display_name | Leevi Annala |
| authorships[1].countries | FI |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I133731052 |
| authorships[1].affiliations[0].raw_affiliation_string | Faculty of Agriculture and Forestry, Department of Food and Nutrition, University of Helsinki, 00014 Helsinki, Finland |
| authorships[1].affiliations[1].institution_ids | https://openalex.org/I133731052 |
| authorships[1].affiliations[1].raw_affiliation_string | Faculty of Science, Department of Computer Science, University of Helsinki, 00014 Helsinki, Finland |
| authorships[1].institutions[0].id | https://openalex.org/I133731052 |
| authorships[1].institutions[0].ror | https://ror.org/040af2s02 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I133731052 |
| authorships[1].institutions[0].country_code | FI |
| authorships[1].institutions[0].display_name | University of Helsinki |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Leevi Annala |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Faculty of Agriculture and Forestry, Department of Food and Nutrition, University of Helsinki, 00014 Helsinki, Finland, Faculty of Science, Department of Computer Science, University of Helsinki, 00014 Helsinki, Finland |
| authorships[2].author.id | https://openalex.org/A5006040355 |
| authorships[2].author.orcid | https://orcid.org/0000-0001-6307-4750 |
| authorships[2].author.display_name | Sampsa Kiiskinen |
| authorships[2].countries | FI |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I94722563 |
| authorships[2].affiliations[0].raw_affiliation_string | Faculty of Information Technology, University of Jyväskylä, 40014 Jyväskylä, Finland |
| authorships[2].institutions[0].id | https://openalex.org/I94722563 |
| authorships[2].institutions[0].ror | https://ror.org/05n3dz165 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I94722563 |
| authorships[2].institutions[0].country_code | FI |
| authorships[2].institutions[0].display_name | University of Jyväskylä |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Sampsa Kiiskinen |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Faculty of Information Technology, University of Jyväskylä, 40014 Jyväskylä, Finland |
| authorships[3].author.id | https://openalex.org/A5043089012 |
| authorships[3].author.orcid | https://orcid.org/0009-0007-9811-6237 |
| authorships[3].author.display_name | Timo Ojala |
| authorships[3].countries | FI |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I94722563 |
| authorships[3].affiliations[0].raw_affiliation_string | Faculty of Information Technology, University of Jyväskylä, 40014 Jyväskylä, Finland |
| authorships[3].institutions[0].id | https://openalex.org/I94722563 |
| authorships[3].institutions[0].ror | https://ror.org/05n3dz165 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I94722563 |
| authorships[3].institutions[0].country_code | FI |
| authorships[3].institutions[0].display_name | University of Jyväskylä |
| authorships[3].author_position | last |
| authorships[3].raw_author_name | Timo Ojala |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | Faculty of Information Technology, University of Jyväskylä, 40014 Jyväskylä, Finland |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://www.mdpi.com/1999-4893/17/1/8/pdf?version=1703403653 |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Exploring the Efficacy of Base Data Augmentation Methods in Deep Learning-Based Radiograph Classification of Knee Joint Osteoarthritis |
| has_fulltext | True |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T11293 |
| primary_topic.field.id | https://openalex.org/fields/27 |
| primary_topic.field.display_name | Medicine |
| primary_topic.score | 0.977400004863739 |
| primary_topic.domain.id | https://openalex.org/domains/4 |
| primary_topic.domain.display_name | Health Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2746 |
| primary_topic.subfield.display_name | Surgery |
| primary_topic.display_name | Orthopedic Infections and Treatments |
| related_works | https://openalex.org/W2502115930, https://openalex.org/W4246396837, https://openalex.org/W2482350142, https://openalex.org/W3176240006, https://openalex.org/W3126451824, https://openalex.org/W1561927205, https://openalex.org/W2360561200, https://openalex.org/W3177426155, https://openalex.org/W2377082163, https://openalex.org/W3028956932 |
| cited_by_count | 10 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 6 |
| counts_by_year[1].year | 2024 |
| counts_by_year[1].cited_by_count | 4 |
| locations_count | 3 |
| best_oa_location.id | doi:10.3390/a17010008 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S190629608 |
| best_oa_location.source.issn | 1999-4893 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 1999-4893 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | True |
| best_oa_location.source.display_name | Algorithms |
| best_oa_location.source.host_organization | https://openalex.org/P4310310987 |
| best_oa_location.source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310310987 |
| best_oa_location.source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | https://www.mdpi.com/1999-4893/17/1/8/pdf?version=1703403653 |
| 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 | Algorithms |
| best_oa_location.landing_page_url | https://doi.org/10.3390/a17010008 |
| primary_location.id | doi:10.3390/a17010008 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S190629608 |
| primary_location.source.issn | 1999-4893 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 1999-4893 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | Algorithms |
| primary_location.source.host_organization | https://openalex.org/P4310310987 |
| primary_location.source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310310987 |
| primary_location.source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| primary_location.license | cc-by |
| primary_location.pdf_url | https://www.mdpi.com/1999-4893/17/1/8/pdf?version=1703403653 |
| 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 | Algorithms |
| primary_location.landing_page_url | https://doi.org/10.3390/a17010008 |
| publication_date | 2023-12-24 |
| publication_year | 2023 |
| referenced_works | https://openalex.org/W2905483812, https://openalex.org/W2789894922, https://openalex.org/W2919115771, https://openalex.org/W2914568698, https://openalex.org/W2978882452, https://openalex.org/W2923027365, https://openalex.org/W2581082771, https://openalex.org/W2716665989, https://openalex.org/W2886848602, https://openalex.org/W4386980335, https://openalex.org/W4288041395, https://openalex.org/W3014974815, https://openalex.org/W3123982987, https://openalex.org/W2964261464, https://openalex.org/W2765253680, https://openalex.org/W2794022343, https://openalex.org/W3213056609, https://openalex.org/W2884065486, https://openalex.org/W3012140936, https://openalex.org/W3115920135, https://openalex.org/W6799532714, https://openalex.org/W4308486497, https://openalex.org/W3213209526, https://openalex.org/W2031144025, https://openalex.org/W6638233663, https://openalex.org/W4211067786, https://openalex.org/W1988644603, https://openalex.org/W2998512002, https://openalex.org/W2963202012, https://openalex.org/W3017873021, https://openalex.org/W4379229508, https://openalex.org/W2337827194, https://openalex.org/W6743073161, https://openalex.org/W4327909703, https://openalex.org/W6748400442, https://openalex.org/W2992037462, https://openalex.org/W4224991957, https://openalex.org/W3107645027, https://openalex.org/W2890430415, https://openalex.org/W2924551358, https://openalex.org/W1967057044, https://openalex.org/W6793164127, https://openalex.org/W2962858109, https://openalex.org/W2951269226, https://openalex.org/W6632100814, https://openalex.org/W2946948417, https://openalex.org/W2752782242, https://openalex.org/W6894113159, https://openalex.org/W2108598243, https://openalex.org/W1994488211, https://openalex.org/W2811374795, https://openalex.org/W2765793020, https://openalex.org/W4304083970, https://openalex.org/W2785809060, https://openalex.org/W1538131130, https://openalex.org/W3210222962, https://openalex.org/W3112701542, https://openalex.org/W1779199741, https://openalex.org/W2744999500 |
| referenced_works_count | 59 |
| abstract_inverted_index.a | 5, 59, 141 |
| abstract_inverted_index.We | 82, 116 |
| abstract_inverted_index.as | 58, 98 |
| abstract_inverted_index.by | 172 |
| abstract_inverted_index.in | 125, 156, 183 |
| abstract_inverted_index.is | 11 |
| abstract_inverted_index.of | 8, 22, 90, 130, 193 |
| abstract_inverted_index.or | 179 |
| abstract_inverted_index.to | 14, 43, 169, 185 |
| abstract_inverted_index.KOA | 29, 132 |
| abstract_inverted_index.Our | 73, 166 |
| abstract_inverted_index.ROI | 102 |
| abstract_inverted_index.and | 18, 46, 88, 101 |
| abstract_inverted_index.are | 68 |
| abstract_inverted_index.due | 13, 42 |
| abstract_inverted_index.for | 28, 71 |
| abstract_inverted_index.our | 126 |
| abstract_inverted_index.the | 19, 23, 136, 150, 170 |
| abstract_inverted_index.KOA. | 72, 159 |
| abstract_inverted_index.This | 139 |
| abstract_inverted_index.Yet, | 62 |
| abstract_inverted_index.bias | 144 |
| abstract_inverted_index.data | 47, 51, 55, 76 |
| abstract_inverted_index.deep | 26 |
| abstract_inverted_index.even | 134 |
| abstract_inverted_index.knee | 1, 137, 151, 194 |
| abstract_inverted_index.less | 114 |
| abstract_inverted_index.like | 85, 110 |
| abstract_inverted_index.most | 69 |
| abstract_inverted_index.such | 97 |
| abstract_inverted_index.that | 176 |
| abstract_inverted_index.used | 83 |
| abstract_inverted_index.were | 113 |
| abstract_inverted_index.(ROI) | 92 |
| abstract_inverted_index.Using | 25 |
| abstract_inverted_index.cause | 7 |
| abstract_inverted_index.field | 171 |
| abstract_inverted_index.joint | 2, 152 |
| abstract_inverted_index.major | 6 |
| abstract_inverted_index.model | 143, 181 |
| abstract_inverted_index.noise | 99 |
| abstract_inverted_index.poses | 39 |
| abstract_inverted_index.shown | 124 |
| abstract_inverted_index.split | 112 |
| abstract_inverted_index.study | 74, 167 |
| abstract_inverted_index.these | 37, 164 |
| abstract_inverted_index.using | 121 |
| abstract_inverted_index.which | 53, 65 |
| abstract_inverted_index.while | 108 |
| abstract_inverted_index.(KOA), | 4 |
| abstract_inverted_index.broad, | 32 |
| abstract_inverted_index.either | 177 |
| abstract_inverted_index.helped | 162 |
| abstract_inverted_index.images | 192 |
| abstract_inverted_index.impede | 180 |
| abstract_inverted_index.it’s | 63 |
| abstract_inverted_index.joint. | 138 |
| abstract_inverted_index.region | 89 |
| abstract_inverted_index.subtle | 15 |
| abstract_inverted_index.varied | 20 |
| abstract_inverted_index.within | 190 |
| abstract_inverted_index.emerges | 57 |
| abstract_inverted_index.extreme | 131 |
| abstract_inverted_index.grades, | 133 |
| abstract_inverted_index.improve | 178 |
| abstract_inverted_index.methods | 96, 109 |
| abstract_inverted_index.patient | 44 |
| abstract_inverted_index.privacy | 45 |
| abstract_inverted_index.regions | 120, 189 |
| abstract_inverted_index.towards | 145 |
| abstract_inverted_index.unclear | 64 |
| abstract_inverted_index.without | 135 |
| abstract_inverted_index.Additive | 50 |
| abstract_inverted_index.Grad-CAM | 160 |
| abstract_inverted_index.However, | 35 |
| abstract_inverted_index.Removing | 149 |
| abstract_inverted_index.accuracy | 155 |
| abstract_inverted_index.accurate | 128 |
| abstract_inverted_index.addition | 184 |
| abstract_inverted_index.cropping | 87 |
| abstract_inverted_index.datasets | 38 |
| abstract_inverted_index.disease. | 24 |
| abstract_inverted_index.effects. | 165 |
| abstract_inverted_index.enhances | 54 |
| abstract_inverted_index.explored | 75 |
| abstract_inverted_index.improved | 106 |
| abstract_inverted_index.interest | 91 |
| abstract_inverted_index.learning | 27 |
| abstract_inverted_index.methods, | 78 |
| abstract_inverted_index.removal. | 103 |
| abstract_inverted_index.requires | 31 |
| abstract_inverted_index.alongside | 94 |
| abstract_inverted_index.datasets. | 34 |
| abstract_inverted_index.diagnosis | 30 |
| abstract_inverted_index.effective | 70 |
| abstract_inverted_index.elucidate | 163 |
| abstract_inverted_index.features. | 148 |
| abstract_inverted_index.including | 79 |
| abstract_inverted_index.increased | 154 |
| abstract_inverted_index.indicated | 140 |
| abstract_inverted_index.injection | 100 |
| abstract_inverted_index.models’ | 127 |
| abstract_inverted_index.obtaining | 36 |
| abstract_inverted_index.potential | 118, 142, 187 |
| abstract_inverted_index.promising | 60 |
| abstract_inverted_index.rotations | 105 |
| abstract_inverted_index.solution. | 61 |
| abstract_inverted_index.Diagnosing | 0 |
| abstract_inverted_index.challenges | 41 |
| abstract_inverted_index.collection | 48 |
| abstract_inverted_index.disability | 9 |
| abstract_inverted_index.discovered | 117 |
| abstract_inverted_index.effective. | 115 |
| abstract_inverted_index.horizontal | 86, 111 |
| abstract_inverted_index.indicators | 17 |
| abstract_inverted_index.irrelevant | 146 |
| abstract_inverted_index.strategies | 84 |
| abstract_inverted_index.techniques | 67, 175 |
| abstract_inverted_index.worldwide, | 10 |
| abstract_inverted_index.adversarial | 80, 95, 122 |
| abstract_inverted_index.challenging | 12 |
| abstract_inverted_index.classifying | 157 |
| abstract_inverted_index.confounding | 119, 188 |
| abstract_inverted_index.contributed | 168 |
| abstract_inverted_index.early-stage | 158 |
| abstract_inverted_index.extraction, | 93 |
| abstract_inverted_index.pinpointing | 173 |
| abstract_inverted_index.progression | 21 |
| abstract_inverted_index.recognizing | 186 |
| abstract_inverted_index.significant | 40 |
| abstract_inverted_index.techniques. | 81 |
| abstract_inverted_index.augmentation | 66, 77, 174 |
| abstract_inverted_index.performance, | 107, 182 |
| abstract_inverted_index.radiographic | 16, 147, 191 |
| abstract_inverted_index.variability, | 56 |
| abstract_inverted_index.augmentation, | 52, 123 |
| abstract_inverted_index.comprehensive | 33 |
| abstract_inverted_index.paradoxically | 153 |
| abstract_inverted_index.restrictions. | 49 |
| abstract_inverted_index.Interestingly, | 104 |
| abstract_inverted_index.classification | 129 |
| abstract_inverted_index.osteoarthritis | 3 |
| abstract_inverted_index.visualizations | 161 |
| abstract_inverted_index.osteoarthritis. | 195 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 97 |
| corresponding_author_ids | https://openalex.org/A5045286306 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| corresponding_institution_ids | https://openalex.org/I94722563 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.4099999964237213 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile.value | 0.93779929 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |