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The relaxation time maps and raw signal data were then used for training Gaussian process regression (GPR) models to predict the histology results.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Proteoglycan content was predicted by the GPR models with high accuracy (median\n                      <jats:italic>r<\/jats:italic>\n                      \u2009=\u20090.81,\n                      <jats:italic>\u03c3<\/jats:italic>\n                      \u2009=\u20090.08 and NRMSE\u2009=\u200911.7%). Predictions performed using raw MRF data outperformed those done using qMRI maps. Collagen fiber anisotropy predictions found only weak correlation (median\n                      <jats:italic>r<\/jats:italic>\n                      \u2009=\u20090.40,\n                      <jats:italic>\u03c3<\/jats:italic>\n                      \u2009=\u20090.25 &amp; NRMSE\u2009=\u200926.4%) and no significant difference was seen between models trained on raw MRF or relaxation time maps.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>These findings indicate that noninvasive prediction of proteoglycan content in cartilage from MRF measurements using a 3\u00a0T clinical scanner is feasible, holding promise for future clinical applications. Collagen fiber anisotropy could not be reliably estimated with the current setup. GPR-based prediction models were found to outperform reference linear models using the same prediction data.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s10334-026-01397-w","type":"journal-article","created":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T10:25:06Z","timestamp":1785839106000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine learning assisted prediction of cartilage histology using MR fingerprinting \u2013 a preliminary study"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9584-7500","authenticated-orcid":false,"given":"Ville","family":"Kantola","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Olli","family":"Nyk\u00e4nen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Victor","family":"Casula","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ville-Pauli","family":"Karjalainen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Martijn A.","family":"Cloos","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Riccardo","family":"Lattanzi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mikko J.","family":"Nissi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Miika T.","family":"Nieminen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,8,4]]},"reference":[{"key":"1397_CR1","doi-asserted-by":"publisher","first-page":"1711","DOI":"10.1016\/S0140-6736(20)32230-3","volume":"396","author":"DJ Hunter","year":"2020","unstructured":"Hunter DJ, March L, Chew M (2020) Osteoarthritis in 2020 and beyond: a lancet commission. 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