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REVIEW article

Front. Radiol.
Sec. Artificial Intelligence in Radiology
Volume 4 - 2024 | doi: 10.3389/fradi.2024.1332535
This article is part of the Research Topic Advances in Artificial Intelligence and Machine Learning Applications for the Imaging of Bone and Soft Tissue Tumors View all 11 articles

Artificial Intelligence (AI) and Machine Learning (ML) applications for the imaging of bone and soft tissue tumors

Provisionally accepted
Paniz Sabeghi Paniz Sabeghi 1Ketki Kinkar Ketki Kinkar 2Gloria d. Castañeda Gloria d. Castañeda 3Liesl S. Eibschutz Liesl S. Eibschutz 1Brandon K. Fields Brandon K. Fields 1Bino Varghese Bino Varghese 1Daksesh B. Patel Daksesh B. Patel 1Ali Gholamrezanezhad Ali Gholamrezanezhad 1*
  • 1 Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, California, United States
  • 2 University of Southern California, Los Angeles, California, United States
  • 3 Keck School of Medicine, University of Southern California, Los Angeles, California, United States

The final, formatted version of the article will be published soon.

    Recent advancements in artificial intelligence (AI) and machine learning (ML) offer numerous opportunities in musculoskeletal (MSK) radiology to potentially bolster diagnostic accuracy, workflow efficiency, and predictive modeling. AI tools have the capability to assist radiologists in many tasks ranging from image segmentation, anomaly detection, and more.In bone and soft tissue tumor imaging, radiomics and deep learning show promise for malignancy stratification, grading, prognostication, and treatment planning. However, challenges like standardization, data integration, and ethical concerns regarding patient data need to be addressed before clinical translation. AI also faces obstacles in robust algorithm development due to limited disease incidence. While global initiatives aim to develop multitasking AI systems, multidisciplinary collaboration is crucial for successful AI integration into clinical practice.Robust approaches addressing challenges and embodying ethical practices are warranted to fully realize AI's potential for enhancing diagnostic accuracy and advancing patient care.• Deep learning models have been developed for diagnosing MSK tumors and show potential to achieve diagnostic efficacy comparable to radiologists in limited classification tasks. • AI algorithms can address issues related to variance in acquisition parameters and noise between MR scans using techniques like edge-preserving denoising and intensity standardization. • Multitasking AI systems that can efficiently perform multiple segmentation and analytical tasks at once hold promise for potentially useful prospective implementations in clinical practice.

    Keywords: artificial intelligence - AI, machine learning, deep learning, Musculoskeletal, Sarcoma

    Received: 03 Nov 2023; Accepted: 01 Aug 2024.

    Copyright: © 2024 Sabeghi, Kinkar, Castañeda, Eibschutz, Fields, Varghese, Patel and Gholamrezanezhad. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Ali Gholamrezanezhad, Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, 90033, California, United States

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.