{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,26]],"date-time":"2026-04-26T05:33:11Z","timestamp":1777181591379,"version":"3.51.4"},"reference-count":24,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2025,5,15]],"date-time":"2025-05-15T00:00:00Z","timestamp":1747267200000},"content-version":"vor","delay-in-days":14,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000051","name":"National Human Genome Research Institute","doi-asserted-by":"publisher","award":["ZIA HG200406"],"award-info":[{"award-number":["ZIA HG200406"]}],"id":[{"id":"10.13039\/100000051","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000051","name":"National Human Genome Research Institute","doi-asserted-by":"publisher","award":["ZIAHG000215"],"award-info":[{"award-number":["ZIAHG000215"]}],"id":[{"id":"10.13039\/100000051","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100009633","name":"Eunice Kennedy Shriver National Institute of Child Health and Human Development","doi-asserted-by":"publisher","award":["ZIAHD009024"],"award-info":[{"award-number":["ZIAHD009024"]}],"id":[{"id":"10.13039\/100009633","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J of Inher Metab Disea"],"published-print":{"date-parts":[[2025,5]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>\n                    Deep learning (DL) is increasingly used to analyze medical imaging, but is less refined for rare conditions, which require novel pre\u2010processing and analytical approaches. To assess DL in the context of rare diseases, this study focused on alkaptonuria (AKU), a rare disorder that affects the spine and involves other sequelae; treatments include the medication nitisinone. Since assessing x\u2010rays to determine disease severity can be a slow, manual process requiring considerable expertise, this study aimed to determine whether these DL methods could accurately identify overall spine severity at specific regions of the spine and whether patients were receiving nitisinone. DL performance was evaluated versus clinical experts using cervical and lumbar spine radiographs. DL models predicted global severity scores (30\u2010point scale) within 1.72\u2009\u00b1\u20091.96 points of expert clinician scores for cervical and 2.51\u2009\u00b1\u20091.96 points for lumbar radiographs. For region\u2010specific metrics, the degrees of narrowing, calcium, and vacuum disc phenomena at each intervertebral space (IVS) were assessed. The model's narrowing scores were within 0.191\u20130.557 points from clinician scores (6\u2010point scale), calcium was predicted with 78%\u201390% accuracy (present, absent, or disc fusion), and vacuum disc phenomenon predictions were less consistent (41%\u201390%). Intriguingly, DL models predicted nitisinone treatment status with 68%\u201377% accuracy, while expert clinicians appeared unable to discern nitisinone status (51% accuracy) (\n                    <jats:italic>p<\/jats:italic>\n                    \u2009=\u20092.0\u2009\u00d7\u200910\n                    <jats:sup>\u22129<\/jats:sup>\n                    ). This highlights the potential for DL to augment certain types of clinical assessments in rare disease, as well as identifying occult features like treatment status.\n                  <\/jats:p>","DOI":"10.1002\/jimd.70042","type":"journal-article","created":{"date-parts":[[2025,5,15]],"date-time":"2025-05-15T23:29:13Z","timestamp":1747351753000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Deep Learning Study of Alkaptonuria Spinal Disease Assesses Global and Regional Severity and Detects Occult Treatment Status"],"prefix":"10.1002","volume":"48","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-9473-1354","authenticated-orcid":false,"given":"Kendall A.","family":"Flaharty","sequence":"first","affiliation":[{"name":"Medical Genomics Unit, Medical Genetics Branch National Human Genome Research Institute, National Institutes of Health  Bethesda Maryland USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vibha","family":"Chandrasekar","sequence":"additional","affiliation":[{"name":"Medical Genomics Unit, Medical Genetics Branch National Human Genome Research Institute, National Institutes of Health  Bethesda Maryland USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Irene J.","family":"Castillo","sequence":"additional","affiliation":[{"name":"Human Biochemical Genetics Section, Medical Genetics Branch National Human Genome Research Institute, National Institutes of Health  Bethesda Maryland USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dat","family":"Duong","sequence":"additional","affiliation":[{"name":"Medical Genomics Unit, Medical Genetics Branch National Human Genome Research Institute, National Institutes of Health  Bethesda Maryland USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Carlos R.","family":"Ferreira","sequence":"additional","affiliation":[{"name":"Unit on Skeletal Genomics, Eunice Kennedy Shriver National Institute of Child Health and Human Development National Institutes of Health  Bethesda Maryland USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Suzanna","family":"Ledgister Hanchard","sequence":"additional","affiliation":[{"name":"Medical Genomics Unit, Medical Genetics Branch National Human Genome Research Institute, National Institutes of Health  Bethesda Maryland USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ping","family":"Hu","sequence":"additional","affiliation":[{"name":"Medical Genomics Unit, Medical Genetics Branch National Human Genome Research Institute, National Institutes of Health  Bethesda Maryland USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rebekah L.","family":"Waikel","sequence":"additional","affiliation":[{"name":"Medical Genomics Unit, Medical Genetics Branch National Human Genome Research Institute, National Institutes of Health  Bethesda Maryland USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Francis","family":"Rossignol","sequence":"additional","affiliation":[{"name":"Human Biochemical Genetics Section, Medical Genetics Branch National Human Genome Research Institute, National Institutes of Health  Bethesda Maryland USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wendy J.","family":"Introne","sequence":"additional","affiliation":[{"name":"Human Biochemical Genetics Section, Medical Genetics Branch National Human Genome Research Institute, National Institutes of Health  Bethesda Maryland USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Benjamin D.","family":"Solomon","sequence":"additional","affiliation":[{"name":"Medical Genomics Unit, Medical Genetics Branch National Human Genome Research Institute, National Institutes of Health  Bethesda Maryland USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2025,5,15]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"crossref","unstructured":"H.Gu H.Dong J.Yang andM. A.Mazurowski \u201cHow to Build the Best Medical Image Segmentation Algorithm Using Foundation Models: A Comprehensive Empirical Study With Segment Anything Model \u201d2024 arXivPreprint arXiv:2404.09957 https:\/\/github.com\/mazurowski\u2010lab\/finetune\u2010SAM.","DOI":"10.59275\/j.melba.2025-86a6"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.xhgg.2021.100053"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.xops.2022.100225"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btae239"},{"key":"e_1_2_10_6_1","doi-asserted-by":"crossref","unstructured":"J.Irvin P.Rajpurkar K.Michael et\u00a0al. \u201cCheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison \u201dProceedings of the AAAI Conference on Artificial Intelligence(2019):590\u2013597.","DOI":"10.1609\/aaai.v33i01.3301590"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41597\u2010024\u201003383\u20100"},{"key":"e_1_2_10_8_1","doi-asserted-by":"publisher","DOI":"10.3390\/app13158646"},{"key":"e_1_2_10_9_1","first-page":"89","article-title":"Unusual Erosive Osteoarthropathy of a Single Midcarpal Joint","volume":"3","author":"Balague F.","year":"1985","journal-title":"Clinical and Experimental Rheumatology"},{"key":"e_1_2_10_10_1","volume-title":"GeneReviews","author":"Introne W. 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