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Scoring performance was compared with human annotations in terms of macro-area under the receiver operating characteristic curve (AUC) and balanced accuracy using fivefold cross-validation. Validation was performed on an independent dataset of MRIs from a second patient cohort.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>In total, 211 MRIs from 112 patients (14 906 region of interests (ROIs)) were included for training\/internal validation using cross-validation and 220 MRIs from 75 patients (11 040 ROIs) for external validation of the networks. The networks achieved high mean (SD) macro-AUC of 92%\u00b11% for erosions, 91%\u00b12% for osteitis and 85%\u00b12% for synovitis. Compared with human annotation, CNNs achieved a high mean Spearman correlation for erosions (90\u00b12%), osteitis (78\u00b18%) and synovitis (69\u00b17%), which remained consistent in the validation dataset.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>We developed a CNN-based automated scoring system that allowed a rapid grading of erosions, osteitis and synovitis with good diagnostic accuracy and using less MRI sequences compared with conventional scoring. This CNN-based approach may help develop standardised cost-efficient and time-efficient assessments of hand MRIs for patients with arthritis.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1136\/rmdopen-2024-004273","type":"journal-article","created":{"date-parts":[[2024,6,17]],"date-time":"2024-06-17T20:45:29Z","timestamp":1718657129000},"page":"e004273","update-policy":"https:\/\/doi.org\/10.1136\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":30,"title":["Deep learning-based classification of erosion, synovitis and osteitis in hand MRI of patients with inflammatory 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