This paper is in the following e-collection/theme issue:

Foundations of AI (47) AI Data (13) Applications of AI (80)

Published on in Vol 2 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/44779, first published .
A Scalable Radiomics- and Natural Language Processing–Based Machine Learning Pipeline to Distinguish Between Painful and Painless Thoracic Spinal Bone Metastases: Retrospective Algorithm Development and Validation Study

A Scalable Radiomics- and Natural Language Processing–Based Machine Learning Pipeline to Distinguish Between Painful and Painless Thoracic Spinal Bone Metastases: Retrospective Algorithm Development and Validation Study

A Scalable Radiomics- and Natural Language Processing–Based Machine Learning Pipeline to Distinguish Between Painful and Painless Thoracic Spinal Bone Metastases: Retrospective Algorithm Development and Validation Study

Authors of this article:

Hossein Naseri1 Author Orcid Image ;   Sonia Skamene2 Author Orcid Image ;   Marwan Tolba2 Author Orcid Image ;   Mame Daro Faye2 Author Orcid Image ;   Paul Ramia2 Author Orcid Image ;   Julia Khriguian2 Author Orcid Image ;   Marc David2 Author Orcid Image ;   John Kildea1 Author Orcid Image
Article Authors Cited by (1) Tweetations (3) Metrics

Journals

  1. Vrettos K, Triantafyllou M, Marias K, Karantanas A, Klontzas M. Artificial intelligence-driven radiomics: developing valuable radiomics signatures with the use of artificial intelligence. BJR|Artificial Intelligence 2024;1(1) View