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Routine intraoperative infection testing in presumed aseptic hip and knee revision: a matched cohort retrospective study.

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Performance of Artificial Intelligence Models in Radiographic Image Analysis for Predicting Hip and Knee Prosthesis

Riccardo Stuani1, Marco Di Maio1, Vincenzo Di Matteo1,2

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Artificial intelligence (AI) shows promise in analyzing hip and knee replacement radiographs to detect aseptic loosening and mechanical failure, potentially improving accuracy over traditional methods.

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Area of Science:

  • Orthopedic surgery
  • Medical imaging
  • Artificial intelligence

Background:

  • Total hip and knee arthroplasty volumes are rising, increasing the postoperative surveillance burden.
  • Current radiographic analysis for aseptic loosening is subjective and can be challenging.
  • AI offers a potential solution for objective and accurate detection of implant failure.

Purpose of the Study:

  • To review the current state of AI in radiographic analysis for aseptic loosening and mechanical failure in hip and knee prostheses.
  • To evaluate the diagnostic capabilities and limitations of AI models in this field.
  • To identify emerging trends and future research directions for AI in orthopedic implant surveillance.

Main Methods:

  • Systematic literature search across major databases (PubMed, Scopus, Web of Science, Cochrane) up to November 2025.
  • Inclusion of peer-reviewed studies on AI tools for detecting aseptic loosening or implant failure in primary hip and knee prostheses.
  • Exclusion of studies focused on infection or acute complications.

Main Results:

  • Ten studies (2020-2025) met inclusion criteria, evaluating AI for radiographic analysis.
  • AI models achieved high internal diagnostic accuracy (83.9%-97.5%) and AUC (0.86-0.99).
  • Performance decreased during external validation, highlighting a key limitation.

Conclusions:

  • AI demonstrates strong potential to equal or exceed standard radiographic interpretation for detecting implant failure.
  • Clinical implementation is hindered by inconsistent performance on external datasets.
  • Future research should focus on multi-institutional validation, explainability, and longitudinal data integration.