|Title||Computational pathology for musculoskeletal conditions using machine learning: advances, trends, and challenges.|
|Publication Type||Journal Article|
|Year of Publication||2022|
|Authors||Konnaris MA, Brendel M, Fontana MAlan, Otero M, Ivashkiv LB, Wang F, Bell RD|
|Journal||Arthritis Res Ther|
|Date Published||2022 Mar 11|
|Keywords||Humans, Image Processing, Computer-Assisted, Machine Learning, Musculoskeletal Diseases|
Histopathology is widely used to analyze clinical biopsy specimens and tissues from pre-clinical models of a variety of musculoskeletal conditions. Histological assessment relies on scoring systems that require expertise, time, and resources, which can lead to an analysis bottleneck. Recent advancements in digital imaging and image processing provide an opportunity to automate histological analyses by implementing advanced statistical models such as machine learning and deep learning, which would greatly benefit the musculoskeletal field. This review provides a high-level overview of machine learning applications, a general pipeline of tissue collection to model selection, and highlights the development of image analysis methods, including some machine learning applications, to solve musculoskeletal problems. We discuss the optimization steps for tissue processing, sectioning, staining, and imaging that are critical for the successful generalizability of an automated image analysis model. We also commenting on the considerations that should be taken into account during model selection and the considerable advances in the field of computer vision outside of histopathology, which can be leveraged for image analysis. Finally, we provide a historic perspective of the previously used histopathological image analysis applications for musculoskeletal diseases, and we contrast it with the advantages of implementing state-of-the-art computational pathology approaches. While some deep learning approaches have been used, there is a significant opportunity to expand the use of such approaches to solve musculoskeletal problems.
|Alternate Journal||Arthritis Res Ther|
|PubMed Central ID||PMC8915507|
|Grant List||R01 AI046712 / AI / NIAID NIH HHS / United States |
R01 AR046713 / AR / NIAMS NIH HHS / United States
R01 AR050401 / AR / NIAMS NIH HHS / United States
Computational pathology for musculoskeletal conditions using machine learning: advances, trends, and challenges.
Submitted by chz4003 on April 11, 2023 - 12:17pm
Institute of Artificial Intelligence for Digital Health