Semin Musculoskelet Radiol
DOI: 10.1055/a-2836-8033

Authors Author Affiliations

Salvatore Gitto 

1   Dipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, Milan, Italy

2   IRCCS Istituto Ortopedico Galeazzi, Milan, Italy

Patrick Omoumi

3   Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland

Domenico Albano

4   Dipartimento di Scienze Biomediche, Chirurgiche ed Odontoiatriche, Università degli Studi di Milano, Milan, Italy

5   Department of Radiology, ASST Grande Ospedale Metropolitano Niguarda, Milan, Italy

Pau Xiberta

6   Graphics and Imaging Laboratory, Universitat de Girona, Girona, Catalonia

Silvia Rossi

7   Department of Neuroradiology, ASST Grande Ospedale Metropolitano Niguarda, Milan, Italy

Aldo Rizzo

2   IRCCS Istituto Ortopedico Galeazzi, Milan, Italy

Carmelo Messina

1   Dipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, Milan, Italy

8   UOC Radiodiagnostica, ASST Centro Specialistico Ortopedico Traumatologico Gaetano Pini-CTO, Milan, Italy

Alessandra Splendiani

9   Department of Biotechnological and Applied Clinical Sciences, University of L'Aquila, L'Aquila, Italy

Antonio Barile

9   Department of Biotechnological and Applied Clinical Sciences, University of L'Aquila, L'Aquila, Italy

Luca Maria Sconfienza

1   Dipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, Milan, Italy

2   IRCCS Istituto Ortopedico Galeazzi, Milan, Italy

Source of Funding This study was supported by European Union - Next Generation EU - NRRP M6C2 - Investment 2.1 Enhancement and strengthening of biomedical research in the NHS - Project Code PNRR-TR1-2023-12377797 (CUP C43C24000470001). The funding source provided financial support without any influence on the preparation and writing of the manuscript. The principal investigator (Luca Maria Sconfienza) and co-principal investigator (Salvatore Gitto) had the final responsibility for the decision to submit the article for publication. Further Information(opens Publication History section)Also available at   Buy Article(opens in new window) Permissions and Reprints(opens in new window) Abstract

Spinal disorders, one of the leading causes of disability worldwide, are routinely assessed on imaging studies. Recent advancements in artificial intelligence for spine imaging interpretation may significantly improve diagnostic accuracy and workflow efficiency, using deep learning and conventional machine learning methods. This narrative review focuses on the innovative artificial intelligence applications in spine imaging interpretation with a pathology-based approach: vertebral fractures, spinal deformities, degenerative disease, skeletal tumors, inflammatory disorders, and opportunistic screening. We provide musculoskeletal radiologists with an up-to-date overview of artificial intelligence applications in spine imaging, thus assisting them in an efficient use of these emerging technologies and promoting clinical adoption.

Keywords artificial intelligence - computer-aided diagnosis - convolutional neural network - deep learning - machine learning Publication History

Received: 10 January 2026

Accepted: 15 March 2026

Article published online:
14 April 2026

© 2026. Thieme. All rights reserved.

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