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Artificial intelligence in orthopedic implant model classification: a systematic review

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Abstract

Although artificial intelligence models have demonstrated high accuracy in identifying specific orthopedic implant models from imaging, which is an important and time-consuming task, the scope of prior works and performance of prior models have not been evaluated. We performed a systematic review to summarize the scope, methodology, and performance of artificial intelligence algorithms in classifying orthopedic implant models. We performed a literature search in PubMed, EMBASE, and the Cochrane Library for studies published up to March 10, 2021, using search terms related to “artificial intelligence”, “orthopedic”, “implant”, and “arthroplasty”. Studies were assessed using a modified version of the methodologic index for non-randomized studies. Reported outcomes included area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. The search identified 2689 records, of which 11 were included in the final review. The number of implant models evaluated ranged from 2 to 27. Five studies reported overall AUC across all included models which ranged from 0.94 to 1.0. Overall accuracy values ranged from 0.804 to 1.0. One study compared AI model performance with that of three surgeons, reporting similar performance. There was a large degree of variation in methodology and reporting quality. Artificial intelligence algorithms have demonstrated strong performance in classifying orthopedic implant models from radiographs. Further research is needed to compare artificial intelligence alone and as an adjunct with human experts in implant identification. Future studies should aim to adhere to rigorous artificial intelligence development methods and thorough, transparent reporting of methods and results.

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Authors and Affiliations

  1. Department of Orthopedic Surgery, Columbia University Irving Medical Center, New York, NY, USA

    Mark Ren

  2. Johns Hopkins University School of Medicine, Baltimore, MD, USA

    Mark Ren

  3. University of Maryland Intelligent Imaging (UMII) Center, Department of Diagnostic Radiology & Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD, USA

    Paul H. Yi

  4. Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD, USA

    Paul H. Yi

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  1. Mark Ren
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Correspondence to Paul H. Yi.

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Ren, M., Yi, P.H. Artificial intelligence in orthopedic implant model classification: a systematic review. Skeletal Radiol 51, 407–416 (2022). https://doi.org/10.1007/s00256-021-03884-8

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