AI-Assisted Detection and Localization of Spinal Metastatic Lesions
The study concludes that AI-based models can augment radiologists' capabilities by providing reliable second-opinion tools, though further refinements and diverse training data are needed for optimal performance, particularly for sclerotic lesion segmentation. The annotated CT dataset produced and shared in this research serves as a valuable resource for future advancements.
Authors
- Artūrs Ņikuļins (ORCID: https://orcid.org/0000-0002-3356-4764)
- Katrina Boločko (ORCID: https://orcid.org/0000-0003-0729-8009)
- Klinta Luīze Sprūdža
- Edgars Edelmers (ORCID: https://orcid.org/0000-0001-5329-4426)
- Elizabete Skrebele (ORCID: https://orcid.org/0009-0003-0384-7397)
- Patrīcija Stapulone
- Niks Saimons Pūce
- Everita Elīna Siņicina
- Viktorija Cīrule
- Ance Kazuša
Institutions
- Riga Technical University (LV)
- Riga Stradiņš University (LV)
- University of Latvia (LV)
Publication Details
- Journal
- Diagnostics
- Published
- 2024-11-03
- DOI
- https://doi.org/10.3390/diagnostics14212458
- Citations
- 9
- Primary Topic
- Medical Imaging and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 1.66
Funders
- Rīgas Tehniskā Universitāte