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Artificial Intelligence, Deep Learning, and Machine Learning Applications in Total Hip Arthroplasty

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Total Hip Arthroplasty

Abstract

Artificial intelligence (AI) recently gained popularity in total hip arthroplasty (THA) applications due to several reasons including technological improvements such as availability of data storage, processor capabilities, AI technique developments, and surgery-related improvements including presurgical analysis techniques developed and data collected for input to algorithms (Mont, et al. J Arthroplast. 34(10):2199–200, 2019). In this work the focus will be on the research literature covering AI, deep learning (DL), and machine learning (ML) techniques that relate to only THA. This coverage excludes the combined results for total knee arthroplasty (TKA) and THA unless THA is analyzed independently from TKA. Applications determined include THA-related economic analysis and payment models, patients’ well-being, risk of blood transfusion, hip fracture detection (Kim and MacKinnon. Clin Radiol. 73:439–45, 2018). Biomechanical considerations, optimal implant design, post-THA implant brand detection, hip disability upon THA, inpatient and outpatient THA surgery detection, automating and improving angle of acetabular component, text-based database search for THA-related factors, mechanical loosening detection of the transplant, patient comfort after THA, and implant failure detection. Many more applications are possible using AI, DL, and ML with few of them suggested in the conclusion section.

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Authors and Affiliations

  1. School of Computing and Engineering, Quinnipiac University, Hamden, CT, USA

    Emre Tokgöz

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  1. Emre Tokgöz
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Tokgöz, E. (2023). Artificial Intelligence, Deep Learning, and Machine Learning Applications in Total Hip Arthroplasty. In: Total Hip Arthroplasty. Springer, Cham. https://doi.org/10.1007/978-3-031-08927-5_11

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  • DOI: https://doi.org/10.1007/978-3-031-08927-5_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-08926-8

  • Online ISBN: 978-3-031-08927-5

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