AI Article Synopsis
- The review aimed to evaluate how reliable AI predictive models are for determining the correct implant sizes during total knee arthroplasty (TKA).
- Four studies involving over 34,500 patients were analyzed, showing that AI models could predict femoral and tibial component sizes with high accuracy, ranging from 88.3% to 99.9%.
- The findings indicate that AI can be a reliable tool in TKA planning, but further research is needed to understand its effects on patient care and costs.
Article Abstract
Purpose: This systematic review aimed to investigate the reliability of AI predictive models of intraoperative implant sizing in total knee arthroplasty (TKA).
Methods: Four databases were searched from inception till July 2023 for original studies that studied the reliability of AI prediction in TKA. The primary outcome was the accuracy ± 1 size. This review was conducted per PRISMA guidelines, and the risk of bias was assessed using the MINORS criteria.
Results: A total of four observational studies comprised of at least 34,547 patients were included in this review. A mean MINORS score of 11 out of 16 was assigned to the review. All included studies were published between 2021 and 2022, with a total of nine different AI algorithms reported. Among these AI models, the accuracy of TKA femoral component sizing prediction ranged from 88.3 to 99.7% within a deviation of one size, while tibial component sizing exhibited an accuracy ranging from 90 to 99.9% ± 1 size.
Conclusion: This study demonstrated the potential of AI as a valuable complement for planning TKA, exhibiting a satisfactory level of reliability in predicting TKA implant sizes. This predictive accuracy is comparable to that of the manual and digital templating techniques currently documented in the literature. However, future research is imperative to assess the impact of AI on patient care and cost-effectiveness.
Level Of Evidence Iii: PROSPERO registration number: CRD42023446868.
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|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10858112 | PMC |
| http://dx.doi.org/10.1007/s00590-023-03784-8 | DOI Listing |
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