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Knowledge of the stone composition helps the urologists choose appropriate medical interventions for patients. The parameters from the spectral computerized tomography (CT) analysis have potential values for identification of the urinary stone compositions.<\/jats:p><\/jats:sec><jats:sec><jats:title>Purpose<\/jats:title><jats:p>The present study aims to identify the compositions of urinary stones in vivo using parameters from spectral CT and machine learning, based on multi\u2010label classification modeling.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>This retrospective study collected 252 urinary stone samples with single\/mixed compositions (including carbapatite [CP], calcium oxalate monohydrate [COM], calcium oxalate dehydrate [COD], uric acid [UA], and struvite [STR]), which were confirmed by ex vivo infrared spectroscopy. Parameters were extracted from an energy spectrum analysis (ESA) of the spectral CT, including the effective atomic number (Z<jats:sub>eff<\/jats:sub>), Z<jats:sub>eff<\/jats:sub> histogram, CT values at a given x\u2010ray energy level, and material densities. These ESA parameters were utilized for composition analysis via a multi\u2010label classification fusion framework, where 250 multi\u2010label models were built and the classification decisions from the top performance models were integrated by a multi\u2010criterion weighted fusion (MCWF) approach in order to reach a consensus prediction. An example\u2010based metric  and label\u2010based metric  were used for global and label\u2010wise accuracy evaluations, respectively. The top\u2010ranked parameters associated with discriminating the stone composition were also identified.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The multi\u2010label classification fusion framework achieved an overall  of 81.2%, with  of 86.7% (CP), 90.6% (COM), 80.6% (COD), 95.0% (UA), and 94.4% (STR) for each composition on the independent testing cohort 1, and  of 76.4% with  of 80.5% (CP), 88.7% (COM), 74.9% (COD), 94.4% (UA), and 98.5% (STR) on the independent testing cohort 2.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>The parameters extracted from the ESA on spectral CT can be utilized to characterize single or mixed stone compositions via multi\u2010label classification modeling. The generalization capability of the proposed methodology still requires further verification.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mp.16154","type":"journal-article","created":{"date-parts":[[2022,12,15]],"date-time":"2022-12-15T14:38:45Z","timestamp":1671115125000},"page":"661-674","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Machine learning based multi\u2010label classification of single or mixed\u2010composition urinary stones in in vivo spectral CT"],"prefix":"10.1002","volume":"50","author":[{"given":"Xian","family":"Li","sequence":"first","affiliation":[{"name":"Department of Radiology the First Affiliated Hospital of Guangzhou Medical University  Guangzhou Guangdong China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peng","family":"Tang","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering Southern Medical University  Guangzhou Guangdong China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fangrong","family":"Liang","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering Southern Medical University  Guangzhou Guangdong China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Linjing","family":"Wang","sequence":"additional","affiliation":[{"name":"Radiotherapy Center Affiliated Cancer Hospital &amp; 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