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  • Review Article
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Studying osteoarthritis with artificial intelligence applied to magnetic resonance imaging


The 3D nature and soft-tissue contrast of MRI makes it an invaluable tool for osteoarthritis research, by facilitating the elucidation of disease pathogenesis and progression. The recent increasing employment of MRI has certainly been stimulated by major advances that are due to considerable investment in research, particularly related to artificial intelligence (AI). These AI-related advances are revolutionizing the use of MRI in clinical research by augmenting activities ranging from image acquisition to post-processing. Automation is key to reducing the long acquisition times of MRI, conducting large-scale longitudinal studies and quantitatively defining morphometric and other important clinical features of both soft and hard tissues in various anatomical joints. Deep learning methods have been used recently for multiple applications in the musculoskeletal field to improve understanding of osteoarthritis. Compared with labour-intensive human efforts, AI-based methods have advantages and potential in all stages of imaging, as well as post-processing steps, including aiding diagnosis and prognosis. However, AI-based methods also have limitations, including the arguably limited interpretability of AI models. Given that the AI community is highly invested in uncovering uncertainties associated with model predictions and improving their interpretability, we envision future clinical translation and progressive increase in the use of AI algorithms to support clinicians in optimizing patient care.

Key points

  • Applications of deep learning to accelerate MRI acquisition and reconstruction show exciting results; nevertheless, fundamental questions remain regarding the most appropriate metrics for evaluating the quality of reconstructed images.

  • Image segmentation errors from artificial intelligence (AI) models lie within the intra-reader variability range. Nonetheless, deployment in clinical practice still requires some form of quality assurance, which might include visual inspection of segmentation outputs.

  • The role of AI in osteoarthritis lesion detection is not to provide a final diagnosis but rather to serve as an additional input for decision-making.

  • AI has been tasked with searching for novel image features indicative of short-term and long-term progression of osteoarthritis to predict disease course on a patient-specific basis.

  • Future research will likely be devoted to the interpretability and estimation of the uncertainty of deep learning models, with the aim of improving clinician trust in AI for supporting patient care.

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Fig. 1: The role of artificial intelligence in stages of imaging.
Fig. 2: Anatomical structures on MRI scans influencing artificial intelligence model prediction of total knee replacement within 5 years.

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Correspondence to Sharmila Majumdar.

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Machine learning

Computer programmes that learn how to perform tasks with some degree of automation and are based in statistics and algorithms.

Deep learning

A type of machine learning that performs multiple layers of mathematical algorithms (analogous to artificial neurons) on input data to execute a pre-defined task as output.


Predicting an observation based on a training dataset that includes observations about the predicted variable. The prediction assumes categorical values. In osteoarthritis, an example is predicting the presence or absence of a knee meniscus tear.


Predicting an observation based on a training dataset that includes observations about the predicted variable that assumes a continuous value (that is, not categorical); for example, the task of prescribing the imaging acquisition plane in MRI.


A form of classification at the pixel level of an image to identify a region of interest (ROI). The goal of segmentation is to assign each pixel to a label that describes the category to which the pixels belongs.

Raw data

Refers to the data stored during the acquisition of an MRI sequence as a function of time. Also known as k-space in MRI.

Supervised learning

The process of learning the relationship between input data and their labels, where all the samples in the training dataset have been labelled.

Ground truth

Information provided by empirical evidence rather than by the inference of a machine learning model; for example, the pixel-by-pixel annotation of a particular region of interest in a segmentation task, or the fully sampled image in accelerated MRI reconstruction.

Unsupervised learning

The process of learning the relationship between input data and their labels, where the data have no labels and the role of the algorithm is to find the underlying structure of the data to use it on the task at hand.

Model training

The stage in machine learning in which an algorithm (regression or classification) is instructed to perform a task. Training can be supervised, semi-supervised or unsupervised. Also known as learning.

Loss function

A mathematical entity for quantifying how well a machine learning algorithm models the data. Higher values indicate poorer modelling ability. During training, the loss function is coupled with an optimizer, which is used to tune the parameters of the machine learning or deep learning algorithm to minimize the loss function and ultimately maximize algorithm performance.


The k-space represents the spatial frequency information in two or three dimensions of an object, and each point in the k-space data matrix contains a portion of the information for the complete image. Also known as raw data in MRI.

Fourier transform

A mathematical transform utilized in image processing to decompose an image into its sine and cosine components, which allows mapping from the spatial domain of an image to its frequency domain. In MRI, the Fourier transform and its inverse maps raw data to image space and vice versa, respectively.

Image-domain learning

The procedure in which an algorithm learns to reconstruct a fully sampled MR image starting from an undersampled MR image.

k-space-domain learning

The procedure in MRI reconstruction in which an algorithm learns to fill in the missing information in an undersampled k-space to ultimately obtain a fully sampled k-space.

Domain mapping

In image reconstruction, this term refers to the procedure where an algorithm learns the mapping from undersampled k-space to fully sampled image.

Aliased image

Aliasing is a signal processing term referring to effects that cause different signals to be indistinguishable. Aliasing can also describe artefacts or distortions in a signal reconstructed from a limited number of samples, which cause the output signal to differ from the original signal.

Relaxometry maps

Quantitative maps reporting measurements of relaxation times from MR images. T1, T2 and T2* are common relaxometry maps that all require appropriate pulse sequence and parameters.

T2 relaxation time

A metric to quantify the T2 relaxation rate in a region of interest. The relaxation time is described by a combination of exponential decay curves, in part owing to different compartments of water in tissues.


A machine-learning algorithm comprising an encoder, which is used to condense the input into a smaller (encoded), meaningful and descriptive representation, and a decoder, which re-expands the encoded representation until the original input is reconstructed.

Domain adaptation

A set of techniques that are used to apply an algorithm trained on a source domain to a different related domain termed the target domain, with the aim of minimizing performance drop.

Neural network

A mathematical system comprising artificial neurons that mimic human neural networks and that are used to identify relationships in datasets. In AI, neural networks are considered building blocks of the most powerful machine learning algorithms. Multiple layers of neurons are concatenated to produce the popular deep neural networks.

Dice similarity coefficient

A statistic that is used to quantify the similarity between two samples; in image segmentation, it measures the overlap between the ground truth and the model-produced segmentation.

Volumetric overlap error

A metric for quantifying the dissimilarity between two datasets. In segmentation, it is computed as the overlap between ground truth and a model-produced segmentation, divided by the union of the two segmentations, subtracted from 1.


A statistic for quantifying the difference between values predicted by a model and the observed values (ground truth). It is computed as the square root of the average of squared errors between predicted and expected values.

Coefficient of variation

A statistical measure of the dispersion of a probability distribution, which is calculated as the ratio between the standard deviation and the average.

Average symmetric surface distance

(ASSD). A metric for evaluating segmentation performance, which is computed by averaging all the distances from points on the boundary of the region segmented by a model to the boundary of the ground truth, and vice versa.

Figure of merit

A quantity utilized to characterize the performance of an algorithm. Sometimes, it is used interchangeably with the term loss function.


A model is interpretable when the relationship between input and predicted output is clear to the user.


Labelling the data so that it can be utilized for training and testing machine learning algorithms. In MRI, annotation can be done at different levels of granularity depending on the type of task, such as classification tasks, segmentation or regression.

Gradient boosting

A machine learning technique used for regression and classification. Starting from multiple prediction models, gradient boosting is used to create a prediction model in the form of an ensemble of those models.

Occlusion maps

An image overlay representing the change in probability for a model’s prediction as a function of position in the image.

Multitask learning

Training a machine learning algorithm to simultaneously learn to perform more than one task at a time, which forces the algorithm to identify a more general model for the data.

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Calivà, F., Namiri, N.K., Dubreuil, M. et al. Studying osteoarthritis with artificial intelligence applied to magnetic resonance imaging. Nat Rev Rheumatol 18, 112–121 (2022).

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