Comparison of lumbar disc degeneration grading between deep learning model SpineNet and radiologist: a longitudinal study with a 14-year follow-up
We found fair to substantial agreement in DD grading between SpineNet and the radiologist, albeit with notable discrepancies. These findings indicate that AI-based systems like SpineNet hold promise as complementary tools in radiological evaluation, including in longitudinal studies, but emphasize the need for ongoing refinement of AI algorithms.
Authors
- Niko Murto (ORCID: https://orcid.org/0000-0002-9820-1390)
- Hannu Kautiainen (ORCID: https://orcid.org/0000-0003-0786-0858)
- Liisa Kerttula
- Teija Lund (ORCID: https://orcid.org/0000-0001-9519-9355)
- Katariina Luoma (ORCID: https://orcid.org/0000-0003-2180-5927)
Institutions
- University of Helsinki (FI)
- Helsinki University Hospital (FI)
- Kuopio University Hospital (FI)
- Folkhälsans Forskningscentrum (FI)
- Hospital District of Helsinki and Uusimaa (FI)
Publication Details
- Journal
- European Spine Journal
- Published
- 2025-05-15
- DOI
- https://doi.org/10.1007/s00586-025-08900-2
- Citations
- 2
- Primary Topic
- Spine and Intervertebral Disc Pathology
- Type
- article
- Field-Weighted Citation Impact
- 2.94
Funders
- Helsingin Yliopisto