Synopsis

Keywords: Musculoskeletal: Joints, Musculoskeletal: Knee, Musculoskeletal: Skeletal

The use of artificial intelligence (AI) has revolutionized areas of image recognition, speech recognition, and natural language processing. AI is transforming the world of medicine by helping doctors to improve detection, diagnosis, treatment, and management of a disease. In this lecture, we will focus on the AI approaches that are practical and currently used in diagnosis, grading, quantification and prediction of MSK disorders. The audience will learn various AI methods emerging in MR imaging for MSK disorders. At the end, the audience will be able to differentiate various AI approaches and choose the most appropriate ones for specific research problems.

Artificial intelligence (AI) has been used for diagnosis, grading, quantification and prediction of MSK disorders. Building on top of the approaches/methods that are presented in the first three talks in this series, we will go over how AI is currently facilitated in MSK image interpretation.

Our main focus will be on diagnosis and prediction of MSK disorders, and grading of MR images. We will go over recent deep learning architectures and advancements that are used for various types of MSK research. AI techniques have been shown to have similar diagnostic performance as human readers for detecting and grading cartilage lesions within the knee on MRI¹. Using AI for MRI diagnosis of various type of joints have been an emerging field of MSK radiology with promising results². We will survey MRI sequences and DL architectures used in AI for classification and segmentation applications in knee¹, shoulder² and hip³ imaging. Lastly, I will briefly explain the current visualization/interpretation approaches for deep learning models.

Acknowledgements

No acknowledgement found.