Automated Recognition Of Dentists’ Poor Postures Using Artificial Intelligence Article Swipe
Jie Xu
,
Hong Yu
,
Weini Xin
,
Jie Ji
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1016/j.identj.2025.104212
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1016/j.identj.2025.104212
Aim or purpose: This study aimed to create and test an artificial intelligence (AI)-based system for the automated evaluation of dentists' clinical sitting postures. Materials and methods: The dentists' sitting postures were compiled into a dataset. A training-validation set comprised 80% of the dataset, while a test set made up 20%. Segmenting video frames, extracting keypoints, calculating geometric angles, and training and validating classification models were the main algorithmic components. Performance measurements utilized included accuracy, F1-score, recall, confusion matrix, and Kappa coefficient. The model's performance was assessed using a 5-fold cross-validation. Results: The model exhibited outstanding performance on the test set, with an accuracy of 98.2%, an F1-score of 0.974, a sensitivity of 0.983, a specificity of 0.995, and an Area Under the Receiver Operating Characteristic Curve (AUC-ROC) of 0.999. It accurately identified typical poor postures, such as forward head tilt, lateral tilt, and shoulder asymmetry. It surpassed the conventional threshold-based classification method (P < 0.01). Conclusions: This research has created and tested an AI-based automated clinical posture evaluation system. The technology facilitates quantitative assessment of dentists' bad sitting postures, and has the potential to offer a dependable instrument for early prevention and management of occupational musculoskeletal problems.
Related Topics
Concepts
No concepts available.
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.identj.2025.104212
- OA Status
- gold
- OpenAlex ID
- https://openalex.org/W4415832515
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4415832515Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1016/j.identj.2025.104212Digital Object Identifier
- Title
-
Automated Recognition Of Dentists’ Poor Postures Using Artificial IntelligenceWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-10-01Full publication date if available
- Authors
-
Jie Xu, Hong Yu, Weini Xin, Jie JiList of authors in order
- Landing page
-
https://doi.org/10.1016/j.identj.2025.104212Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1016/j.identj.2025.104212Direct OA link when available
- Cited by
-
0Total citation count in OpenAlex
Full payload
| id | https://openalex.org/W4415832515 |
|---|---|
| doi | https://doi.org/10.1016/j.identj.2025.104212 |
| ids.doi | https://doi.org/10.1016/j.identj.2025.104212 |
| ids.openalex | https://openalex.org/W4415832515 |
| fwci | |
| type | article |
| title | Automated Recognition Of Dentists’ Poor Postures Using Artificial Intelligence |
| biblio.issue | |
| biblio.volume | 75 |
| biblio.last_page | 104212 |
| biblio.first_page | 104212 |
| is_xpac | False |
| apc_list.value | 2550 |
| apc_list.currency | GBP |
| apc_list.value_usd | 3127 |
| apc_paid.value | 2550 |
| apc_paid.currency | GBP |
| apc_paid.value_usd | 3127 |
| language | en |
| locations[0].id | doi:10.1016/j.identj.2025.104212 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S143937555 |
| locations[0].source.issn | 0020-6539, 1875-595X |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 0020-6539 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | True |
| locations[0].source.display_name | International Dental Journal |
| locations[0].source.host_organization | https://openalex.org/P4310320990 |
| locations[0].source.host_organization_name | Elsevier BV |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310320990 |
| locations[0].source.host_organization_lineage_names | Elsevier BV |
| locations[0].license | cc-by-nc-nd |
| locations[0].pdf_url | |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by-nc-nd |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | International Dental Journal |
| locations[0].landing_page_url | https://doi.org/10.1016/j.identj.2025.104212 |
| locations[1].id | pmh:oai:doaj.org/article:87648f7926a142139e6fdbef620ccb0e |
| 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 | International Dental Journal, Vol 75, Iss , Pp 104212- (2025) |
| locations[1].landing_page_url | https://doaj.org/article/87648f7926a142139e6fdbef620ccb0e |
| indexed_in | crossref, doaj |
| authorships[0].author.id | https://openalex.org/A5085531312 |
| authorships[0].author.orcid | https://orcid.org/0000-0001-5291-5198 |
| authorships[0].author.display_name | Jie Xu |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Xu Jie |
| authorships[0].is_corresponding | False |
| authorships[1].author.id | https://openalex.org/A5003746498 |
| authorships[1].author.orcid | https://orcid.org/0000-0003-0667-8413 |
| authorships[1].author.display_name | Hong Yu |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Yu Hong |
| authorships[1].is_corresponding | False |
| authorships[2].author.id | https://openalex.org/A5028357224 |
| authorships[2].author.orcid | |
| authorships[2].author.display_name | Weini Xin |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Weini Xin |
| authorships[2].is_corresponding | False |
| authorships[3].author.id | https://openalex.org/A5100332383 |
| authorships[3].author.orcid | https://orcid.org/0000-0002-8864-2352 |
| authorships[3].author.display_name | Jie Ji |
| authorships[3].author_position | last |
| authorships[3].raw_author_name | Jie Ji |
| authorships[3].is_corresponding | False |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://doi.org/10.1016/j.identj.2025.104212 |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-11-04T00:00:00 |
| display_name | Automated Recognition Of Dentists’ Poor Postures Using Artificial Intelligence |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic | |
| cited_by_count | 0 |
| locations_count | 2 |
| best_oa_location.id | doi:10.1016/j.identj.2025.104212 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S143937555 |
| best_oa_location.source.issn | 0020-6539, 1875-595X |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 0020-6539 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | True |
| best_oa_location.source.display_name | International Dental Journal |
| best_oa_location.source.host_organization | https://openalex.org/P4310320990 |
| best_oa_location.source.host_organization_name | Elsevier BV |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310320990 |
| best_oa_location.source.host_organization_lineage_names | Elsevier BV |
| best_oa_location.license | cc-by-nc-nd |
| best_oa_location.pdf_url | |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by-nc-nd |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | International Dental Journal |
| best_oa_location.landing_page_url | https://doi.org/10.1016/j.identj.2025.104212 |
| primary_location.id | doi:10.1016/j.identj.2025.104212 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S143937555 |
| primary_location.source.issn | 0020-6539, 1875-595X |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 0020-6539 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | International Dental Journal |
| primary_location.source.host_organization | https://openalex.org/P4310320990 |
| primary_location.source.host_organization_name | Elsevier BV |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310320990 |
| primary_location.source.host_organization_lineage_names | Elsevier BV |
| primary_location.license | cc-by-nc-nd |
| primary_location.pdf_url | |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by-nc-nd |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | International Dental Journal |
| primary_location.landing_page_url | https://doi.org/10.1016/j.identj.2025.104212 |
| publication_date | 2025-10-01 |
| publication_year | 2025 |
| referenced_works_count | 0 |
| abstract_inverted_index.< | 154 |
| abstract_inverted_index.A | 36 |
| abstract_inverted_index.a | 34, 45, 88, 110, 114, 186 |
| abstract_inverted_index.(P | 153 |
| abstract_inverted_index.It | 130, 146 |
| abstract_inverted_index.an | 10, 102, 106, 119, 163 |
| abstract_inverted_index.as | 137 |
| abstract_inverted_index.of | 19, 41, 104, 108, 112, 116, 128, 175, 194 |
| abstract_inverted_index.on | 97 |
| abstract_inverted_index.or | 1 |
| abstract_inverted_index.to | 6, 184 |
| abstract_inverted_index.up | 49 |
| abstract_inverted_index.80% | 40 |
| abstract_inverted_index.Aim | 0 |
| abstract_inverted_index.The | 27, 82, 92, 170 |
| abstract_inverted_index.and | 8, 25, 59, 61, 79, 118, 143, 161, 180, 192 |
| abstract_inverted_index.bad | 177 |
| abstract_inverted_index.for | 15, 189 |
| abstract_inverted_index.has | 159, 181 |
| abstract_inverted_index.set | 38, 47 |
| abstract_inverted_index.the | 16, 42, 66, 98, 122, 148, 182 |
| abstract_inverted_index.was | 85 |
| abstract_inverted_index.20%. | 50 |
| abstract_inverted_index.Area | 120 |
| abstract_inverted_index.This | 3, 157 |
| abstract_inverted_index.head | 139 |
| abstract_inverted_index.into | 33 |
| abstract_inverted_index.made | 48 |
| abstract_inverted_index.main | 67 |
| abstract_inverted_index.poor | 134 |
| abstract_inverted_index.set, | 100 |
| abstract_inverted_index.such | 136 |
| abstract_inverted_index.test | 9, 46, 99 |
| abstract_inverted_index.were | 31, 65 |
| abstract_inverted_index.with | 101 |
| abstract_inverted_index.Curve | 126 |
| abstract_inverted_index.Kappa | 80 |
| abstract_inverted_index.Under | 121 |
| abstract_inverted_index.aimed | 5 |
| abstract_inverted_index.early | 190 |
| abstract_inverted_index.model | 93 |
| abstract_inverted_index.offer | 185 |
| abstract_inverted_index.study | 4 |
| abstract_inverted_index.tilt, | 140, 142 |
| abstract_inverted_index.using | 87 |
| abstract_inverted_index.video | 52 |
| abstract_inverted_index.while | 44 |
| abstract_inverted_index.0.01). | 155 |
| abstract_inverted_index.0.974, | 109 |
| abstract_inverted_index.0.983, | 113 |
| abstract_inverted_index.0.995, | 117 |
| abstract_inverted_index.0.999. | 129 |
| abstract_inverted_index.5-fold | 89 |
| abstract_inverted_index.98.2%, | 105 |
| abstract_inverted_index.create | 7 |
| abstract_inverted_index.method | 152 |
| abstract_inverted_index.models | 64 |
| abstract_inverted_index.system | 14 |
| abstract_inverted_index.tested | 162 |
| abstract_inverted_index.angles, | 58 |
| abstract_inverted_index.created | 160 |
| abstract_inverted_index.forward | 138 |
| abstract_inverted_index.frames, | 53 |
| abstract_inverted_index.lateral | 141 |
| abstract_inverted_index.matrix, | 78 |
| abstract_inverted_index.model's | 83 |
| abstract_inverted_index.posture | 167 |
| abstract_inverted_index.recall, | 76 |
| abstract_inverted_index.sitting | 22, 29, 178 |
| abstract_inverted_index.system. | 169 |
| abstract_inverted_index.typical | 133 |
| abstract_inverted_index.AI-based | 164 |
| abstract_inverted_index.F1-score | 107 |
| abstract_inverted_index.Receiver | 123 |
| abstract_inverted_index.Results: | 91 |
| abstract_inverted_index.accuracy | 103 |
| abstract_inverted_index.assessed | 86 |
| abstract_inverted_index.clinical | 21, 166 |
| abstract_inverted_index.compiled | 32 |
| abstract_inverted_index.dataset, | 43 |
| abstract_inverted_index.dataset. | 35 |
| abstract_inverted_index.included | 73 |
| abstract_inverted_index.methods: | 26 |
| abstract_inverted_index.postures | 30 |
| abstract_inverted_index.purpose: | 2 |
| abstract_inverted_index.research | 158 |
| abstract_inverted_index.shoulder | 144 |
| abstract_inverted_index.training | 60 |
| abstract_inverted_index.utilized | 72 |
| abstract_inverted_index.(AUC-ROC) | 127 |
| abstract_inverted_index.F1-score, | 75 |
| abstract_inverted_index.Materials | 24 |
| abstract_inverted_index.Operating | 124 |
| abstract_inverted_index.accuracy, | 74 |
| abstract_inverted_index.automated | 17, 165 |
| abstract_inverted_index.comprised | 39 |
| abstract_inverted_index.confusion | 77 |
| abstract_inverted_index.dentists' | 20, 28, 176 |
| abstract_inverted_index.exhibited | 94 |
| abstract_inverted_index.geometric | 57 |
| abstract_inverted_index.postures, | 135, 179 |
| abstract_inverted_index.postures. | 23 |
| abstract_inverted_index.potential | 183 |
| abstract_inverted_index.problems. | 197 |
| abstract_inverted_index.surpassed | 147 |
| abstract_inverted_index.(AI)-based | 13 |
| abstract_inverted_index.Segmenting | 51 |
| abstract_inverted_index.accurately | 131 |
| abstract_inverted_index.artificial | 11 |
| abstract_inverted_index.assessment | 174 |
| abstract_inverted_index.asymmetry. | 145 |
| abstract_inverted_index.dependable | 187 |
| abstract_inverted_index.evaluation | 18, 168 |
| abstract_inverted_index.extracting | 54 |
| abstract_inverted_index.identified | 132 |
| abstract_inverted_index.instrument | 188 |
| abstract_inverted_index.keypoints, | 55 |
| abstract_inverted_index.management | 193 |
| abstract_inverted_index.prevention | 191 |
| abstract_inverted_index.technology | 171 |
| abstract_inverted_index.validating | 62 |
| abstract_inverted_index.Performance | 70 |
| abstract_inverted_index.algorithmic | 68 |
| abstract_inverted_index.calculating | 56 |
| abstract_inverted_index.components. | 69 |
| abstract_inverted_index.facilitates | 172 |
| abstract_inverted_index.outstanding | 95 |
| abstract_inverted_index.performance | 84, 96 |
| abstract_inverted_index.sensitivity | 111 |
| abstract_inverted_index.specificity | 115 |
| abstract_inverted_index.Conclusions: | 156 |
| abstract_inverted_index.coefficient. | 81 |
| abstract_inverted_index.conventional | 149 |
| abstract_inverted_index.intelligence | 12 |
| abstract_inverted_index.measurements | 71 |
| abstract_inverted_index.occupational | 195 |
| abstract_inverted_index.quantitative | 173 |
| abstract_inverted_index.Characteristic | 125 |
| abstract_inverted_index.classification | 63, 151 |
| abstract_inverted_index.musculoskeletal | 196 |
| abstract_inverted_index.threshold-based | 150 |
| abstract_inverted_index.cross-validation. | 90 |
| abstract_inverted_index.training-validation | 37 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile |