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AI-based hip prosthesis failure prediction through evolutional radiological indices

  • Hip Arthroplasty
  • Published:
Archives of Orthopaedic and Trauma Surgery Aims and scope Submit manuscript

Abstract

Introduction

This study aimed to develop artificial intelligence models for predicting hip implant failure from radiological features. Analyzing the evolution of the periprosthetic bone and implant’s position throughout the entire follow-up period has shown the potential to be more relevant in outcome prediction than simply considering the latest radiographic images. Thus, we investigated an AI-based model employing a small set of evolutional parameters derived from conventional radiological features to predict hip prosthesis failure.

Materials and methods

One hundred sixty-nine radiological features were annotated from historical anteroposterior and lateral radiographs for 162 total hip arthroplasty patients, 32 of which later underwent implant failure. Linear regression on each patient’s chronologically sorted radiological features was employed to derive 169 corresponding evolutional parameters per image. Three sets of machine learning predictors were developed: one employing the original features (standard model), one the evolutional ones (evolutional model), and the last their union (hybrid model). Each set included a model employing all the available features (full model) and a model employing the few most predictive ones according to Gini importance (minimal model).

Results

The evolutional and hybrid predictors resulted highly effective (area under the ROC curve (AUC) of full models = 0.94), outperforming the standard one, whose AUC was only 0.82. The minimal hybrid model, employing just four features, three of which evolutional, scored an AUC of 0.95, proving even more accurate than the full one, exploiting 173 features. This tool could be shaped to be either highly specific (sensitivity: 80%, specificity: 98.6%) or highly sensitive (sensitivity: 90%, specificity: 92.4%).

Conclusion

The proposed predictor may represent a highly sensitive screening tool for clinicians, capable to predict THA failure with an advance between a few months and more than a year through only four radiological parameters, considering either their value at the latest visit or their evolution through time.

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Availability of data and materials

All data are available on a digital repository.

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Funding

The authors declare that part of the work was carried out with a research grant (GR-2018–12367275) from the Italian Ministry of Health. No other funds, grants, or other support were received during the preparation of this manuscript.

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Authors and Affiliations

  1. Department of Electronics, Information and Bioengineering, Politecnico di Milano, Via Ponzio 34/5, 20133, Milan, Italy

    Matteo Bulloni & Valentina Corino

  2. Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, 20090, Pieve Emanuele, MI, Italy

    Francesco Manlio Gambaro & Mattia Loppini

  3. IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089, Rozzano, MI, Italy

    Francesco Manlio Gambaro, Katia Chiappetta, Guido Grappiolo & Mattia Loppini

  4. Fondazione Livio Sciutto Onlus, Campus Savona, Università degli Studi di Genova, 17100, Savona, Italy

    Katia Chiappetta, Guido Grappiolo & Mattia Loppini

  5. Cardio Tech-Lab, Centro Cardiologico Monzino IRCCS, Via Carlo Parea 4, 20138, Milan, MI, Italy

    Valentina Corino

Authors
  1. Matteo Bulloni
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  2. Francesco Manlio Gambaro
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  4. Guido Grappiolo
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  5. Valentina Corino
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  6. Mattia Loppini
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by M.L., V.C., M.B., and F.M.G. The first draft of the manuscript was written by M.B. and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Mattia Loppini.

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Conflict of interest

G.G. declares royalties and licenses from Zimmer Biomet, Innomed, and Adler Ortho; Financial support for attending symposia and educational programs from Zimmer Biomet. M.L. declares a research grant (GR-2018-12367275) to IRCCS Humanitas Research Hospital from the Italian Ministry of Health and research grants to Fondazione Livio Sciutto—ONLUS to perform postmarket study for medical devices from Zimmer Biomet; Financial support for attending symposia and educational programs from Zimmer Biomet. V.C. and M.B. and F.M.G. and C.K. declare no conflict of interest.

Ethical approval

The current study was approved by the Ethical Committee of Humanitas Research Hospital (protocol number ICH-ORT-2019-01).

Consent to participate

A consent to participate in the study was signed by each participating patient.

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Bulloni, M., Gambaro, F.M., Chiappetta, K. et al. AI-based hip prosthesis failure prediction through evolutional radiological indices. Arch Orthop Trauma Surg 144, 895–907 (2024). https://doi.org/10.1007/s00402-023-05069-5

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  • DOI: https://doi.org/10.1007/s00402-023-05069-5

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