Abstract
Purpose
The purpose of this study was to evaluate the reliability of a newly developed AI-algorithm for the evaluation of long leg radiographs (LLR) after total knee arthroplasties (TKA).
Methods
In the validation cohort 200 calibrated LLRs of eight different common unconstrained and constrained knee systems were analysed. Accuracy and reproducibility of the AI-algorithm were compared to manual reads regarding the hip–knee–ankle (HKA) as well as femoral (FCA) and tibial component (TCA) angles. In the evaluation cohort all institutional LLRs with TKAs in 2018 (n = 1312) were evaluated to assess the algorithms’ ability of handling large data sets. Intraclass correlation (ICC) coefficient and mean absolute deviation (sMAD) were calculated to assess conformity between the AI software and manual reads.
Results
Validation cohort: The AI-software was reproducible on 96% and reliable on 92.1% of LLRs with an output and showed excellent reliability in all measured angles (ICC > 0.97) compared to manual measurements. Excellent results were found for primary unconstrained TKAs. In constrained TKAs landmark setting on the femoral and tibial component failed in 12.5% of LLRs (n = 9). Evaluation cohort: Mean measurements for all postoperative TKAs (n = 1240) were 0.2° varus ± 2.5° (HKA), 89.3° ± 1.9° (FCA), and 89.1° ± 1.6° (TCA). Mean measurements on preoperative revision TKAs (n = 74) were 1.6 varus ± 6.4° (HKA), 90.5° ± 3.1° (FCA), and 88.9° ± 4.1° (TCA).
Conclusions
AI-powered applications are reliable for automated analysis of lower limb alignment on LLRs with TKAs. They are capable of handling large data sets and could, therefore, lead to more standardized and efficient postoperative quality controls.
Level of evidence
Diagnostic Level III.
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Acknowledgements
We would like to thank ImageBiopsy Lab (Allan Hummer and Matthew D. DiFranco) for the technical support and answering all our questions.
Funding
The Michael Ogon received a research grant from ImageBiopsy Lab. The collection, analysis, and interpretation of data, writing of the report, and the decision to submit the paper for publication were performed by the authors and not influenced by ImageBiopsy Lab.
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GMS: was responsible for conceptualization, data curation, formal analysis, investigation, methodology, project administration, visualization, and writing. GMS and SS: performed the manual measurements. JAM, BJHF, AA and MD: participated in conceptualization, methodology and editing. JGH: was the senior author and accountable for conceptualization, methodology, supervision, validation, writing as well as review & editing.
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We certify that we have not signed any agreement with commercial interest related to this study, which would in any way limit publication of any and all data generated for the study or to delay publication for any reason.
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This study was approved by the local ethics committee (No. 47/2020) and has been carried out in accordance with the ethical standards in the 1964 Declaration of Helsinki.
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Schwarz, G.M., Simon, S., Mitterer, J.A. et al. Artificial intelligence enables reliable and standardized measurements of implant alignment in long leg radiographs with total knee arthroplasties. Knee Surg Sports Traumatol Arthrosc 30, 2538–2547 (2022). https://doi.org/10.1007/s00167-022-07037-9
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DOI: https://doi.org/10.1007/s00167-022-07037-9