Artificial Intelligence in Musculoskeletal Imaging: Current Status and Future Directions

Abstract

OBJECTIVE. The objective of this article is to show how artificial intelligence (AI) has impacted different components of the imaging value chain thus far as well as to describe its potential future uses. CONCLUSION. The use of AI has the potential to greatly enhance every component of the imaging value chain. From assessing the appropriateness of imaging orders to helping predict patients at risk for fracture, AI can increase the value that musculoskeletal imagers provide to their patients and to referring clinicians by improving image quality, patient centricity, imaging efficiency, and diagnostic accuracy.

Keywords: MRI; artificial intelligence; deep learning; fast MRI; machine learning; musculoskeletal imaging.

Fig. 1—

Schematic showing machine learning as specialized subcategory of artificial intelligence. Deep learning is another subcategory of machine learning that studies use of certain category of computational models that are fit to large datasets.

Fig. 2—

Learning paradigms. A, Schematic shows classic machine learning paradigm. B, Schematic shows deep learning paradigm.

Fig. 3—

Schematic shows training of deep neural network for task of image classification. Process of prediction and backpropagation is repeated for numerous examples from training dataset, with progressive refinement of weights to improve future predictions. Circles represent nodes. Black lines represent weighted connections between nodes, with thickness of each line representing magnitude of corresponding weight. Long arrow below each process denotes flow of information through network at this step. Shaded areas of bars and small triangles to right of each process show probability of input image being categorized in certain category. Areas of bars between double-headed arrows denote difference of prediction to actual ground truth that drives refining of weights in training.

Fig. 4—

Schematic of example of convolutional neural network (CNN) architecture that takes input image and performs binary classification on basis of content of image. Dimensions of images, as they are processed by CNN, are shown below images. Please note that choice of four convolutional channels in every convolution layer is arbitrary choice in this didactic example of CNN architecture. Architecture consists of five convolutional layers that alternate with pooling layers, each of which combines its input into output that is four times smaller (i.e., 320 × 320, followed by 160 × 160, 80 × 80, 40 × 40, and then 20 × 20), followed by fully connected layer. Convolutional layers perform task of feature extraction at progressively higher level. Fully connected layer performs classification. Values in brackets denote output of classification obtained by network (1 corresponding to image being corrupted by artifacts, 0 corresponding to uncorrupted image).

Fig. 5—

Comparison of 4-times-accelerated data acquisition with machine learning (ML) reconstruction to conventional, fully sampled (FS) clinical protocol in two anonymized patients. A and B, Tear of medial meniscus (arrows) can be easily seen on FS (A) and ML (B) coronal proton density images. C and D, Bone contusion (black arrows) and subchondral edema (white arrows) in medial femoral condyle can be easily seen on FS (C) and ML (D) coronal fat-suppressed T2-weighted images.