Augmented Reality in Orthopedic Surgery Is Emerging from Proof of Concept Towards Clinical Studies: a Literature Review Explaining the Technology and Current State of the Art

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Abstract

Purpose of Review

Augmented reality (AR) is becoming increasingly popular in modern-day medicine. Computer-driven tools are progressively integrated into clinical and surgical procedures. The purpose of this review was to provide a comprehensive overview of the current technology and its challenges based on recent literature mainly focusing on clinical, cadaver, and innovative sawbone studies in the field of orthopedic surgery. The most relevant literature was selected according to clinical and innovational relevance and is summarized.

Recent Findings

Augmented reality applications in orthopedic surgery are increasingly reported. In this review, we summarize basic principles of AR including data preparation, visualization, and registration/tracking and present recently published clinical applications in the area of spine, osteotomies, arthroplasty, trauma, and orthopedic oncology. Higher accuracy in surgical execution, reduction of radiation exposure, and decreased surgery time are major findings presented in the literature.

Summary

In light of the tremendous progress of technological developments in modern-day medicine and emerging numbers of research groups working on the implementation of AR in routine clinical procedures, we expect the AR technology soon to be implemented as standard devices in orthopedic surgery.

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Most cited references 50

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Recent advances in augmented reality

R Azuma, Y. Baillot, R. Behringer … (2001)

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Current status, opportunities and challenges of augmented reality in education

Jyh-Chong Liang, Hsin-Yi Chang, Silvia Lee … (2013)

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3D deeply supervised network for automated segmentation of volumetric medical images.

Qi Dou, Lequan Yu, Hao Chen … (2017)

While deep convolutional neural networks (CNNs) have achieved remarkable success in 2D medical image segmentation, it is still a difficult task for CNNs to segment important organs or structures from 3D medical images owing to several mutually affected challenges, including the complicated anatomical environments in volumetric images, optimization difficulties of 3D networks and inadequacy of training samples. In this paper, we present a novel and efficient 3D fully convolutional network equipped with a 3D deep supervision mechanism to comprehensively address these challenges; we call it 3D DSN. Our proposed 3D DSN is capable of conducting volume-to-volume learning and inference, which can eliminate redundant computations and alleviate the risk of over-fitting on limited training data. More importantly, the 3D deep supervision mechanism can effectively cope with the optimization problem of gradients vanishing or exploding when training a 3D deep model, accelerating the convergence speed and simultaneously improving the discrimination capability. Such a mechanism is developed by deriving an objective function that directly guides the training of both lower and upper layers in the network, so that the adverse effects of unstable gradient changes can be counteracted during the training procedure. We also employ a fully connected conditional random field model as a post-processing step to refine the segmentation results. We have extensively validated the proposed 3D DSN on two typical yet challenging volumetric medical image segmentation tasks: (i) liver segmentation from 3D CT scans and (ii) whole heart and great vessels segmentation from 3D MR images, by participating two grand challenges held in conjunction with MICCAI. We have achieved competitive segmentation results to state-of-the-art approaches in both challenges with a much faster speed, corroborating the effectiveness of our proposed 3D DSN.

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Author and article information

Contributors

Fabio A. Casari:

ORCID: http://orcid.org/0000-0002-9530-4861

fabio.casari@balgrist.ch

Journal

Journal ID (nlm-ta): Curr Rev Musculoskelet Med

Journal ID (iso-abbrev): Curr Rev Musculoskelet Med

Title: Current Reviews in Musculoskeletal Medicine

Publisher: Springer US (New York )

ISSN (Print): 1935-973X

ISSN (Electronic): 1935-9748

Publication date (Electronic): 5 February 2021

Publication date PMC-release: 5 February 2021

Publication date Collection: April 2021

Volume: 14

Issue: 2

Pages: 192-203

Affiliations

[1 ]GRID grid.7400.3, ISNI 0000 0004 1937 0650, Department of Orthopedic Surgery, Balgrist University Hospital, , University of Zurich, ; Zurich, Switzerland

[2 ]GRID grid.7400.3, ISNI 0000 0004 1937 0650, ROCS, Research in Orthopedic Computer Science, Balgrist Campus, , University of Zurich, ; Forchstrasse 340, 8008 Zürich, Switzerland

[3 ]GRID grid.6936.a, ISNI 0000000123222966, Computer Aided Medical Procedures (CAMP), , Technische Universität München, ; Munich, Germany

[4 ]GRID grid.21107.35, ISNI 0000 0001 2171 9311, Computer Aided Medical Procedures (CAMP), , Johns Hopkins University, ; Baltimore, MD USA

[5 ]GRID grid.22937.3d, ISNI 0000 0000 9259 8492, Department of Orthopaedics and Trauma Surgery, , Medical University of Vienna, ; Vienna, Austria

[6 ]GRID grid.11899.38, ISNI 0000 0004 1937 0722, Computer Engineering and Digital Systems Department, , Escola Politécnica, Universidade de São Paulo, ; São Paulo, SP Brazil

[7 ]GRID grid.11899.38, ISNI 0000 0004 1937 0722, Laboratory of Computer Applications for Health Care, , Escola de Artes e Humanidades, Universidade de São Paulo, ; São Paulo, SP Brazil

[8 ]GRID grid.419014.9, ISNI 0000 0004 0576 9812, Orthopedics and Traumatology Department, , Faculty of Medical Sciences of Santa Casa de Sao Paulo, ; Sao Paulo, SP Brazil

Author notes

An article on behalf of the invitation by Section Editors, Dr. Sara Goldchmit and Dr. Marcelo Queiroz, to contribute an article for the Emerging Trends in Design for Musculoskeletal Medicine section in Current Reviews in Musculoskeletal Medicine.

Author information

Fabio A. Casari http://orcid.org/0000-0002-9530-4861

Article

Publisher ID: 9699

DOI: 10.1007/s12178-021-09699-3

PMC ID: 7990993

PubMed ID: 33544367

SO-VID: 0b9c964d-7829-4c3d-a7a7-201413de96bd

License:

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

History

Date accepted : 8 January 2021

Funding

Funded by: Universität Zürich

Open Access :

Open Access funding provided by Universität Zürich

Custom metadata

ScienceOpen disciplines: Orthopedics

Keywords: ar,augmented reality,computer-assisted surgery,hololens,vr,hologram

Data availability:

ScienceOpen disciplines: Orthopedics

Keywords: ar, augmented reality, computer-assisted surgery, hololens, vr, hologram

Augmented Reality in Orthopedic Surgery Is Emerging from Proof of Concept Towards Clinical Studies: a Literature Review Explaining the Technology and Current State of the Art

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Abstract

Purpose of Review

Recent Findings

Summary

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SICOT-J (Orthopedic surgery and traumatology)

Most cited references 50

Recent advances in augmented reality

Current status, opportunities and challenges of augmented reality in education

3D deeply supervised network for automated segmentation of volumetric medical images.

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