Artificial intelligence and machine learning in spine research

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Abstract

Artificial intelligence (AI) and machine learning (ML) techniques are revolutionizing several industrial and research fields like computer vision, autonomous driving, natural language processing, and speech recognition. These novel tools are already having a major impact in radiology, diagnostics, and many other fields in which the availability of automated solution may benefit the accuracy and repeatability of the execution of critical tasks. In this narrative review, we first present a brief description of the various techniques that are being developed nowadays, with special focus on those used in spine research. Then, we describe the applications of AI and ML to problems related to the spine which have been published so far, including the localization of vertebrae and discs in radiological images, image segmentation, computer‐aided diagnosis, prediction of clinical outcomes and complications, decision support systems, content‐based image retrieval, biomechanics, and motion analysis. Finally, we briefly discuss major ethical issues related to the use of AI in healthcare, namely, accountability, risk of biased decisions as well as data privacy and security, which are nowadays being debated in the scientific community and by regulatory agencies.

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Most cited references 86

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Some Studies in Machine Learning Using the Game of Checkers

A. L. Samuel (1959)

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Receptive fields and functional architecture of monkey striate cortex.

D H Hubel, T Wiesel (1968)

1. The striate cortex was studied in lightly anaesthetized macaque and spider monkeys by recording extracellularly from single units and stimulating the retinas with spots or patterns of light. Most cells can be categorized as simple, complex, or hypercomplex, with response properties very similar to those previously described in the cat. On the average, however, receptive fields are smaller, and there is a greater sensitivity to changes in stimulus orientation. A small proportion of the cells are colour coded.2. Evidence is presented for at least two independent systems of columns extending vertically from surface to white matter. Columns of the first type contain cells with common receptive-field orientations. They are similar to the orientation columns described in the cat, but are probably smaller in cross-sectional area. In the second system cells are aggregated into columns according to eye preference. The ocular dominance columns are larger than the orientation columns, and the two sets of boundaries seem to be independent.3. There is a tendency for cells to be grouped according to symmetry of responses to movement; in some regions the cells respond equally well to the two opposite directions of movement of a line, but other regions contain a mixture of cells favouring one direction and cells favouring the other.4. A horizontal organization corresponding to the cortical layering can also be discerned. The upper layers (II and the upper two-thirds of III) contain complex and hypercomplex cells, but simple cells are virtually absent. The cells are mostly binocularly driven. Simple cells are found deep in layer III, and in IV A and IV B. In layer IV B they form a large proportion of the population, whereas complex cells are rare. In layers IV A and IV B one finds units lacking orientation specificity; it is not clear whether these are cell bodies or axons of geniculate cells. In layer IV most cells are driven by one eye only; this layer consists of a mosaic with cells of some regions responding to one eye only, those of other regions responding to the other eye. Layers V and VI contain mostly complex and hypercomplex cells, binocularly driven.5. The cortex is seen as a system organized vertically and horizontally in entirely different ways. In the vertical system (in which cells lying along a vertical line in the cortex have common features) stimulus dimensions such as retinal position, line orientation, ocular dominance, and perhaps directionality of movement, are mapped in sets of superimposed but independent mosaics. The horizontal system segregates cells in layers by hierarchical orders, the lowest orders (simple cells monocularly driven) located in and near layer IV, the higher orders in the upper and lower layers.

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Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

Karen Simonyan, Andrea Vedaldi, Andrew Zisserman (2013)

This paper addresses the visualisation of image classification models, learnt using deep Convolutional Networks (ConvNets). We consider two visualisation techniques, based on computing the gradient of the class score with respect to the input image. The first one generates an image, which maximises the class score [Erhan et al., 2009], thus visualising the notion of the class, captured by a ConvNet. The second technique computes a class saliency map, specific to a given image and class. We show that such maps can be employed for weakly supervised object segmentation using classification ConvNets. Finally, we establish the connection between the gradient-based ConvNet visualisation methods and deconvolutional networks [Zeiler et al., 2013].

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Author and article information

Contributors

Fabio Galbusera:

ORCID: https://orcid.org/0000-0003-1826-9190

fabio.galbusera@grupposandonato.it

Journal

Journal ID (nlm-ta): JOR Spine

Journal ID (iso-abbrev): JOR Spine

Journal ID (doi): 10.1002/(ISSN)2572-1143

Journal ID (publisher-id): JSP2

Title: JOR Spine

Publisher: John Wiley & Sons, Inc. (Hoboken, USA )

ISSN (Electronic): 2572-1143

Publication date (Electronic): 05 March 2019

Publication date Collection: March 2019

Volume: 2

Issue: 1 ( doiID: 10.1002/jsp2.2019.2.issue-1 )

Electronic Location Identifier: e1044

Affiliations

[ ¹ ] Laboratory of Biological Structures Mechanics IRCCS Istituto Ortopedico Galeazzi Milan Italy

Author notes

[*] [* ] Correspondence

Fabio Galbusera, Laboratory of Biological Structures Mechanics, IRCCS Istituto Ortopedico Galeazzi, via Galeazzi 4, 20161 Milan, Italy.

Email: fabio.galbusera@ 123456grupposandonato.it

Author information

Fabio Galbusera https://orcid.org/0000-0003-1826-9190

Article

Publisher ID: JSP21044

DOI: 10.1002/jsp2.1044

PMC ID: 6686793

PubMed ID: 31463458

SO-VID: b7a2dd41-588a-4431-9ee4-c767ecb99401

License:

This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.

History

Date received : 14 December 2018

Date revision received : 31 January 2019

Date accepted : 31 January 2019

Page count

Figures: 11, Tables: 0, Pages: 20, Words: 17152

Custom metadata

source-schema-version-number 2.0

component-id jsp21044

cover-date March 2019

details-of-publishers-convertor Converter:WILEY_ML3GV2_TO_NLMPMC version:5.6.7 mode:remove_FC converted:05.08.2019

Keywords: artificial neural networks,deep learning,ethical implications,outcome prediction,segmentation

Data availability:

Keywords: artificial neural networks, deep learning, ethical implications, outcome prediction, segmentation

Artificial intelligence and machine learning in spine research

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Annual Reviews AI, Machine Learning, and Society

Most cited references 86

Some Studies in Machine Learning Using the Game of Checkers

Receptive fields and functional architecture of monkey striate cortex.

Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

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