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Validation of the shoulder range of motion software for measurement of shoulder ranges of motion in consultation: coupling a red/green/blue-depth video camera to artificial intelligence

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Abstract

Purpose

Clinical evaluation of the shoulder range of motion (RoM) may vary significantly depending on the surgeon. We aim to validate an automatic shoulder RoM measurement system associating image acquisition by an RGB-D (red/green/blue–depth) video camera to an artificial intelligence (AI) algorithm.

Methods

Thirty healthy volunteers were included. A 3D RGB-D sensor that simultaneously generated a colour image and a depth map was used. Then, an open-access convolutional neural network algorithm that was programmed for shoulder recognition provided a 3D motion measure. Each volunteer adopted a randomized position successively. For each position, two observers made a visual (EyeREF) and goniometric measurement (GonioREF), blind to the automated software which was implemented by an orthopaedic surgeon. We evaluated the inter-tester intra-class correlation (ICC) between observers and the concordance correlation coefficient (CCC) between the three methods.

Results

For manual evaluations EyeREF and GonioREF, ICC remained constantly excellent for the widest motions in the vertical plane (i.e., abduction and flexion). It was very good for ER1 and IR2 and fairly good for adduction, extension, and ER2. Differences between the measurements’ means of EyeREF and shoulder RoM was significant for all motions. Compared to GonioREF, shoulder RoM provided similar results for abduction, adduction, and flexion and EyeREF provided similar results for adduction, ER1, and ER2. The three methods showed an overall good to excellent CCC. The mean bias between the three methods remained under 10° and clinically acceptable.

Conclusion

RGB-D/AI combination is reliable in measuring shoulder RoM in consultation, compared to classic goniometry and visual observation.

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Data availability

N/A.

Code availability

The used code is open source (https://github.com/princeton-vl/pose-hg-train).

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Authors and Affiliations

  1. Institut Universitaire Locomoteur et du Sport (IULS), CHU de Nice, Unité de Recherche Clinique Côte d’Azur (UR2CA), Université Côte d’Azur, Nice, France

    Marc-Olivier Gauci, Manuel Olmos, Caroline Cointat, Pierre-Emmanuel Chammas, Nicolas Bronsard & Jean-François Gonzalez

  2. Société Imascap, Plouzanée, Bretagne, France

    Manuel Urvoy

  3. B-Com, Institut de Recherche Technologique, Cesson-Sévigné, Bretagne, France

    Albert Murienne

Authors
  1. Marc-Olivier Gauci
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  2. Manuel Olmos
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  3. Caroline Cointat
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  4. Pierre-Emmanuel Chammas
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  5. Manuel Urvoy
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  6. Albert Murienne
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  7. Nicolas Bronsard
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  8. Jean-François Gonzalez
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Contributions

Conceptualization, MO Gauci; methodology: MO Gauci; software: A Murienne and M Urvoy; validation: MO Gauci and M Olmos; formal analysis: M Urvoy and MO Gauci; investigation: M Olmos, C Cointat, and PE Chammas; data curation: M Olmos; writing—original draft preparation: MO Gauci and M Olmos; writing—review and editing: MO Gauci; visualization: JF Gonzalez; supervision: MO Gauci; project administration: N Bronsard and MO Gauci.

Corresponding author

Correspondence to Marc-Olivier Gauci.

Ethics declarations

Ethics approval

The study was approved by the Institutional Review Board (or Ethics Committee) of Institut Universitaire Locomoteur et du Sport (protocol IRB: IULS202104). The study was conducted according to the guidelines of the Declaration of Helsinki.

Consent to participate

Informed consent was obtained from all individual participants included in the study.

Consent for publication

All authors have read and agreed to the published version of the manuscript.

Competing interests

Marc-Olivier Gauci is a consultant of Imascap (Now part of Stryker), and Manuel Urvoy is an employee of Imascap. The other authors and their family do not declare any conflict of interest for this study.

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Gauci, MO., Olmos, M., Cointat, C. et al. Validation of the shoulder range of motion software for measurement of shoulder ranges of motion in consultation: coupling a red/green/blue-depth video camera to artificial intelligence. International Orthopaedics (SICOT) 47, 299–307 (2023). https://doi.org/10.1007/s00264-022-05675-9

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  • DOI: https://doi.org/10.1007/s00264-022-05675-9

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