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Artificial intelligence and machine learning applications for the imaging of bone and soft tissue tumors.
Sabeghi, Paniz; Kinkar, Ketki K; Castaneda, Gloria Del Rosario; Eibschutz, Liesl S; Fields, Brandon K K; Varghese, Bino A; Patel, Dakshesh B; Gholamrezanezhad, Ali.
Afiliación
  • Sabeghi P; Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States.
  • Kinkar KK; Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States.
  • Castaneda GDR; Keck School of Medicine, University of Southern California, Los Angeles, CA, United States.
  • Eibschutz LS; Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States.
  • Fields BKK; Department of Radiology & Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States.
  • Varghese BA; Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States.
  • Patel DB; Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States.
  • Gholamrezanezhad A; Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States.
Front Radiol ; 4: 1332535, 2024.
Article en En | MEDLINE | ID: mdl-39301168
ABSTRACT
Recent advancements in artificial intelligence (AI) and machine learning offer numerous opportunities in musculoskeletal 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, lesion 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 such as standardization, data integration, and ethical concerns regarding patient data need to be addressed ahead of clinical translation. In the realm of musculoskeletal oncology, AI also faces obstacles in robust algorithm development due to limited disease incidence. While many 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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Front Radiol Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Suiza
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Front Radiol Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Suiza