{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T15:07:12Z","timestamp":1787238432030,"version":"build-2736575974"},"reference-count":36,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2024,11,3]],"date-time":"2024-11-03T00:00:00Z","timestamp":1730592000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Development Fund of Riga Technical University"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Objectives: The integration of machine learning and radiomics in medical imaging has significantly advanced diagnostic and prognostic capabilities in healthcare. This study focuses on developing and validating an artificial intelligence (AI) model using U-Net architectures for the accurate detection and segmentation of spinal metastases from computed tomography (CT) images, addressing both osteolytic and osteoblastic lesions. Methods: Our methodology employs multiple variations of the U-Net architecture and utilizes two distinct datasets: one consisting of 115 polytrauma patients for vertebra segmentation and another comprising 38 patients with documented spinal metastases for lesion detection. Results: The model demonstrated strong performance in vertebra segmentation, achieving Dice Similarity Coefficient (DSC) values between 0.87 and 0.96. For metastasis segmentation, the model achieved a DSC of 0.71 and an F-beta score of 0.68 for lytic lesions but struggled with sclerotic lesions, obtaining a DSC of 0.61 and an F-beta score of 0.57, reflecting challenges in detecting dense, subtle bone alterations. Despite these limitations, the model successfully identified isolated metastatic lesions beyond the spine, such as in the sternum, indicating potential for broader skeletal metastasis detection. Conclusions: The study concludes that AI-based models can augment radiologists\u2019 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.<\/jats:p>","DOI":"10.3390\/diagnostics14212458","type":"journal-article","created":{"date-parts":[[2024,11,4]],"date-time":"2024-11-04T05:41:23Z","timestamp":1730698883000},"page":"2458","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["AI-Assisted Detection and Localization of Spinal Metastatic Lesions"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5329-4426","authenticated-orcid":false,"given":"Edgars","family":"Edelmers","sequence":"first","affiliation":[{"name":"Faculty of Medicine, R\u012bga Stradi\u0146\u0161 University, LV-1010 Riga, Latvia"},{"name":"Faculty of Computer Science, Information Technology and Energy, Riga Technical University, LV-1048 Riga, Latvia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Art\u016brs","family":"\u0145iku\u013cins","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science, Information Technology and Energy, Riga Technical University, LV-1048 Riga, Latvia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Klinta Lu\u012bze","family":"Spr\u016bd\u017ea","sequence":"additional","affiliation":[{"name":"Faculty of Medicine, R\u012bga Stradi\u0146\u0161 University, LV-1010 Riga, Latvia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Patr\u012bcija","family":"Stapulone","sequence":"additional","affiliation":[{"name":"Faculty of Medicine, R\u012bga Stradi\u0146\u0161 University, LV-1010 Riga, Latvia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Niks Saimons","family":"P\u016bce","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science, Information Technology and Energy, Riga Technical University, LV-1048 Riga, Latvia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-0384-7397","authenticated-orcid":false,"given":"Elizabete","family":"Skrebele","sequence":"additional","affiliation":[{"name":"Faculty of Civil and Mechanical Engineering, Riga Technical University, LV-1048 Riga, Latvia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Everita El\u012bna","family":"Si\u0146icina","sequence":"additional","affiliation":[{"name":"Faculty of Biology, University of Latvia, LV-1004 Riga, Latvia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Viktorija","family":"C\u012brule","sequence":"additional","affiliation":[{"name":"Department of Radiology, Faculty of Medicine, R\u012bga Stradi\u0146\u0161 University, LV-1010 Riga, Latvia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ance","family":"Kazu\u0161a","sequence":"additional","affiliation":[{"name":"Faculty of Medicine, R\u012bga Stradi\u0146\u0161 University, LV-1010 Riga, Latvia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0729-8009","authenticated-orcid":false,"given":"Katrina","family":"Bolo\u010dko","sequence":"additional","affiliation":[{"name":"Department of Computer Graphics and Computer Vision, Riga Technical University, LV-1048 Riga, Latvia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/j.csbj.2023.11.007","article-title":"Artificial Intelligence in Skeletal Metastasis Imaging","volume":"23","author":"Dong","year":"2024","journal-title":"Comput. 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