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The aim of this study was to develop and validate an artificial neural network for the detection of definite radiographic sacroiliitis as a manifestation of axial spondyloarthritis (axSpA).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      Conventional radiographs of the sacroiliac joints obtained in two independent studies of patients with axSpA were used. The first cohort comprised 1553 radiographs and was split into training (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u20091324) and validation (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u2009229) sets. The second cohort comprised 458 radiographs and was used as an independent test dataset. All radiographs were assessed in a central reading session, and the final decision on the presence or absence of definite radiographic sacroiliitis was used as a reference. The performance of the neural network was evaluated by calculating areas under the receiver operating characteristic curves (AUCs) as well as sensitivity and specificity. Cohen\u2019s kappa and the absolute agreement were used to assess the agreement between the neural network and the human readers.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The neural network achieved an excellent performance in the detection of definite radiographic sacroiliitis with an AUC of 0.97 and 0.94 for the validation and test datasets, respectively. Sensitivity and specificity for the cut-off weighting both measurements equally were 88% and 95% for the validation and 92% and 81% for the test set. The Cohen\u2019s kappa between the neural network and the reference judgements were 0.79 and 0.72 for the validation and test sets with an absolute agreement of 90% and 88%, respectively.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Deep artificial neural networks enable the accurate detection of definite radiographic sacroiliitis relevant for the diagnosis and classification of axSpA.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s13075-021-02484-0","type":"journal-article","created":{"date-parts":[[2021,4,8]],"date-time":"2021-04-08T04:03:39Z","timestamp":1617854619000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":90,"title":["Deep learning for detection of radiographic sacroiliitis: achieving expert-level performance"],"prefix":"10.1186","volume":"23","author":[{"given":"Keno K.","family":"Bressem","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1537-1424","authenticated-orcid":false,"given":"Janis L.","family":"Vahldiek","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lisa","family":"Adams","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stefan Markus","family":"Niehues","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hildrun","family":"Haibel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Valeria Rios","family":"Rodriguez","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Murat","family":"Torgutalp","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mikhail","family":"Protopopov","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fabian","family":"Proft","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Judith","family":"Rademacher","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joachim","family":"Sieper","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Martin","family":"Rudwaleit","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bernd","family":"Hamm","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marcus R.","family":"Makowski","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kay-Geert","family":"Hermann","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Denis","family":"Poddubnyy","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,4,8]]},"reference":[{"issue":"4","key":"2484_CR1","doi-asserted-by":"publisher","first-page":"361","DOI":"10.1002\/art.1780270401","volume":"27","author":"S van der Linden","year":"1984","unstructured":"van der Linden S, Valkenburg HA, Cats A. 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Rethinking the inception architecture for computer vision. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR); 2016. p. 2818\u201326."}],"container-title":["Arthritis Research &amp; Therapy"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s13075-021-02484-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1186\/s13075-021-02484-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s13075-021-02484-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,4,8]],"date-time":"2021-04-08T04:07:27Z","timestamp":1617854847000},"score":1,"resource":{"primary":{"URL":"https:\/\/arthritis-research.biomedcentral.com\/articles\/10.1186\/s13075-021-02484-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,8]]},"references-count":31,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,12]]}},"alternative-id":["2484"],"URL":"https:\/\/doi.org\/10.1186\/s13075-021-02484-0","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2020.05.19.20105304","asserted-by":"object"}]},"ISSN":["1478-6362"],"issn-type":[{"value":"1478-6362","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,4,8]]},"assertion":[{"value":"11 February 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 March 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 April 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Both PROOF and GESPIC cohorts were approved by the local ethics committees of each study centre in accordance with the local laws and regulations and is being conducted in accordance with the Declaration of Helsinki and Good Clinical Practice. GESPIC was additionally approved by a central ethics committee of the coordinating centre. Written informed consent to participate was obtained from all patients.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"K.K.B., L.C.A., J.R., M.T. and M.M. have nothing to disclose.J.L.V. reports non-financial support from Bayer, non-financial support from Guerbet, non-financial support from Medtronic, and personal fees and non-financial support from Merit Medical, outside the submitted work.S.M.N. reports grants from the German Research Foundation (Deutsche Forschungsgemeinschaft), personal fees from Bracco Imaging, personal fees from Canon Medical Systems, personal fees from Guerbet, personal fees from Teleflex Medical GmbH, and personal fees from Bayer Vital GmbH, outside the submitted work.H.H. reports personal fees from Pfizer, personal fees from Janssen, personal fees from Novartis, personal fees from Roche, and personal fees from MSD, outside the submitted work.V.R.R. reports personal fees from Abbvie and personal fees from Novartis, outside the submitted work.M.P. reports personal fees from Novartis and personal fees from AbbVie, outside the submitted work.F.P. reports personal fees from AbbVie, personal fees from AMGEN, personal fees from BMS, personal fees from Celgene and from MSD, grants and personal fees from Novartis, personal fees from Pfizer and from Roche, and personal fees from UCB, outside the submitted work.J.S. reports grants from AbbVie, during the conduct of the study; personal fees from AbbVie, personal fees from Novartis; personal fees from Pfizer; personal fees from Roche; personal fees from UCB; personal fees from Boehringer Ingelheim; personal fees from Janssen; and personal fees from Merk, outside the submitted work.M.R. received honoraria and\/or consulting fees from AbbVie, BMS, Celgene, Janssen, Eli Lilly, MSD, Novartis, Pfizer, Roche, and UCB Pharma.B.H. reports grants from Abbot, grants from Actelion Pharmaceuticals, grants from Bayer Schering Pharma, grants from Bayer Vital, grants from BRACCO Group, grants from Bristol-Myers Squibb, grants from Charite Research Organisation GmbH, grants from Deutsche Krebshilfe, grants from Essex Pharma, grants from Guerbet, grants from INC Research, grants from lnSightec Ud, grants from IPSEN Pharma, grants from Kendlel MorphoSys AG, grants from Lilly GmbH, grants from MeVis Medical Solutions AG, grants from Nexus Oncology, grants from Novartis, grants from Parexel Clinical Research Organisation Service, grants from Pfizer GmbH, grants from Philipps, grants from Sanofis-Aventis, grants from Siemens, grants from Teruma Medical Corporation, grants from Toshiba, grants from Zukunftsfond Berlin, grants from Amgen, grants from AO Foundation, grants from BARD, grants from BBraun, grants from Boehring Ingelheimer, grants from Brainsgate, grants from CELLACT Pharma, grants from CeloNova Bio-Sciences, grants from GlaxoSmithKline, grants from Jansen, grants from Roehe, grants from Sehumaeher GmbH, grants from Medronic, grants from Pluristem, grants from Quintiles, grants from Roehe, grants from Astellas, grants from Chiltern, grants from Respicardia, grants from TEVA, grants from Abbvie, grants from AstraZenaca, and grants from Galmed Research and Development Ltd., outside the submitted work.K.G.H. reports personal fees from AbbVie, personal fees from Pfizer, personal fees from MSD, and personal fees from Roche, outside the submitted work.D.P. reports grants and personal fees from AbbVie, during the conduct of the study; grants and personal fees from AbbVie; personal fees from BMS; personal fees from Celgene; grants and personal fees from Lilly; grants and personal fees from MSD; grants and personal fees from Novartis; grants and personal fees from Pfizer; personal fees from Roche; and personal fees from UCB, outside the submitted work.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"106"}}