Bibliographic Details
| Title: |
Artificial intelligence in total and unicompartmental knee arthroplasty |
| Authors: |
Umile Giuseppe Longo, Sergio De Salvatore, Federica Valente, Mariajose Villa Corta, Bruno Violante, Kristian Samuelsson |
| Source: |
BMC Musculoskeletal Disorders, Vol 25, Iss 1, Pp 1-25 (2024) |
| Publisher Information: |
BMC, 2024. |
| Publication Year: |
2024 |
| Collection: |
LCC:Diseases of the musculoskeletal system |
| Subject Terms: |
AI, Artificial intelligence, Machine Learning, Orthopaedics, Joint replacement, Knee replacement, Diseases of the musculoskeletal system, RC925-935 |
| More Details: |
Abstract The application of Artificial intelligence (AI) and machine learning (ML) tools in total (TKA) and unicompartmental knee arthroplasty (UKA) emerges with the potential to improve patient-centered decision-making and outcome prediction in orthopedics, as ML algorithms can generate patient-specific risk models. This review aims to evaluate the potential of the application of AI/ML models in the prediction of TKA outcomes and the identification of populations at risk. An extensive search in the following databases: MEDLINE, Scopus, Cinahl, Google Scholar, and EMBASE was conducted using the PIOS approach to formulate the research question. The PRISMA guideline was used for reporting the evidence of the data extracted. A modified eight-item MINORS checklist was employed for the quality assessment. The databases were screened from the inception to June 2022. Forty-four out of the 542 initially selected articles were eligible for the data analysis; 5 further articles were identified and added to the review from the PUBMED database, for a total of 49 articles included. A total of 2,595,780 patients were identified, with an overall average age of the patients of 70.2 years ± 7.9 years old. The five most common AI/ML models identified in the selected articles were: RF, in 38.77% of studies; GBM, in 36.73% of studies; ANN in 34.7% of articles; LR, in 32.65%; SVM in 26.53% of articles. This systematic review evaluated the possible uses of AI/ML models in TKA, highlighting their potential to lead to more accurate predictions, less time-consuming data processing, and improved decision-making, all while minimizing user input bias to provide risk-based patient-specific care. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
1471-2474 |
| Relation: |
https://doaj.org/toc/1471-2474 |
| DOI: |
10.1186/s12891-024-07516-9 |
| Access URL: |
https://doaj.org/article/683794fb09fb4879ba6c65ef96070143 |
| Accession Number: |
edsdoj.683794fb09fb4879ba6c65ef96070143 |
| Database: |
Directory of Open Access Journals |
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