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Reliability of artificial intelligence in predicting total knee arthroplasty component sizes: a systematic review

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journal contribution
submitted on 2024-01-11, 09:30 and posted on 2024-01-15, 09:18 authored by Loay A. Salman, Harman Khatkar, Abdallah Al-Ani, Osama Z. Alzobi, Abedallah Abudalou, Ashraf T. Hatnouly, Ghalib Ahmed, Shamsi Hameed, Mohamed AlAteeq Aldosari

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.

Other Information

Published in: European Journal of Orthopaedic Surgery & Traumatology
License: https://creativecommons.org/licenses/by/4.0
See article on publisher's website: https://dx.doi.org/10.1007/s00590-023-03784-8

Additional institutions affiliated with: Surgical Specialty Center - HMC

Funding

Open Access funding provided by the Qatar National Library.

History

Language

  • English

Publisher

Springer Nature

Publication Year

  • 2023

License statement

This Item is licensed under the Creative Commons Attribution 4.0 International License.

Institution affiliated with

  • Hamad Medical Corporation
  • Hamad General Hospital - HMC

Methodology

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.