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Performance comparison and future perspectives of deep learning and classical machine learning in bone tumor applications: a systematic review (2019–2025)

Crossref DOI link: https://doi.org/10.1186/s12911-026-03401-8

Published Online: 2026-02-24

Update policy: https://doi.org/10.1007/springer_crossmark_policy

Authors

Qiao, Yu

Eisfeld, Carolin

von Eisenhart-Rothe, Rüdiger

Hinterwimmer, Florian
Funding

Funding for this research was provided by:

Technische Universität München
License Information

Text and Data Mining valid from 2026-02-24

Accepted Manuscript valid from 2026-02-24
More Information

Article History

Received: 7 July 2025

Accepted: 14 February 2026

First Online: 24 February 2026

Declarations

:

: Not applicable.

: All authors reviewed the article and unanimously agreed to publish it.

: The authors declare no competing interests.

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