{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T15:42:18Z","timestamp":1786549338747,"version":"build-2736575974"},"reference-count":58,"publisher":"The Endocrine Society","license":[{"start":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T00:00:00Z","timestamp":1785456000000},"content-version":"am","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Despite significant advances in the diagnosis and treatment of acromegaly, diagnostic delay remains substantial, often exceeding 6 to 10 years, while a significant proportion of patients remain inadequately controlled despite multiple available therapeutic modalities. Artificial intelligence (AI) is a promising tool to address several challenges across the acromegaly care pathway, from early disease detection to individualized treatment selection and outcome prediction. AI-assisted facial and hand image analysis has demonstrated the potential to identify subtle phenotypic changes and reduce diagnostic delay, facilitating earlier recognition of the disease. In parallel, machine learning and deep learning approaches have been increasingly employed to predict surgical remission, response to medical therapy, and radiotherapy outcomes, often outperforming individual biomarkers. AI models further support the development of precision medicine strategies capable of improving patient stratification and therapeutic decision-making. This review summarizes current and emerging applications of AI in acromegaly, including facial recognition technologies, radiomics, and machine-learning predictive models integrating clinical, biochemical, radiological, and molecular data.<\/jats:p>","DOI":"10.1210\/clinem\/dgag311","type":"journal-article","created":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T11:56:35Z","timestamp":1785412595000},"source":"Crossref","is-referenced-by-count":0,"title":["Role of artificial intelligence in acromegaly detection and management"],"prefix":"10.1210","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1339-3192","authenticated-orcid":false,"given":"Leandro","family":"Kasuki","sequence":"first","affiliation":[{"name":"Endocrine Unit and Neuroendocrinology Research Center, Medical School and Hospital Universit\u00e1rio Clementino Fraga Filho-Universidade Federal do Rio de Janeiro , Rio de Janeiro 21941-913 ,","place":["Brazil"]},{"name":"Neuroendocrine Unit-Instituto Estadual do C\u00e9rebro Paulo Niemeyer, Secretaria Estadual de Sa\u00fade , Rio de Janeiro 20231-092 ,","place":["Brazil"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eduardo Medeiros Ferreira","family":"Gama","sequence":"additional","affiliation":[{"name":"Endocrine Unit and Neuroendocrinology Research Center, Medical School and Hospital Universit\u00e1rio Clementino Fraga Filho-Universidade Federal do Rio de Janeiro , Rio de Janeiro 21941-913 ,","place":["Brazil"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luiz Eduardo","family":"Wildemberg","sequence":"additional","affiliation":[{"name":"Endocrine Unit and Neuroendocrinology Research Center, Medical School and Hospital Universit\u00e1rio Clementino Fraga Filho-Universidade Federal do Rio de Janeiro , Rio de Janeiro 21941-913 ,","place":["Brazil"]},{"name":"Neuroendocrine Unit-Instituto Estadual do C\u00e9rebro Paulo Niemeyer, Secretaria Estadual de Sa\u00fade , Rio de Janeiro 20231-092 ,","place":["Brazil"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"80","published-online":{"date-parts":[[2026,7,31]]},"reference":[{"issue":"3","key":"2026081210411843000_dgag311-B1","doi-asserted-by":"crossref","first-page":"475","DOI":"10.1016\/j.ecl.2020.05.007","article-title":"Acromegaly: update on management and long-term morbidities","volume":"49","author":"Kasuki","year":"2020","journal-title":"Endocrinol Metab Clin North Am"},{"issue":"1","key":"2026081210411843000_dgag311-B2","doi-asserted-by":"crossref","first-page":"268","DOI":"10.1210\/er.2018-00115","article-title":"Systemic complications of acromegaly and the impact of the current treatment landscape: an update","volume":"40","author":"Gadelha","year":"2019","journal-title":"Endocr Rev"},{"issue":"2","key":"2026081210411843000_dgag311-B3","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1111\/j.1365-2265.2009.03626.x","article-title":"Features at diagnosis of 324 patients with acromegaly did not change from 1981 to 2006: acromegaly remains under-recognized and under-diagnosed","volume":"72","author":"Reid","year":"2010","journal-title":"Clin Endocrinol (Oxf)"},{"issue":"11","key":"2026081210411843000_dgag311-B4","doi-asserted-by":"crossref","first-page":"718","DOI":"10.1038\/s41574-025-01148-2","article-title":"Consensus on acromegaly therapeutic outcomes: an update","volume":"21","author":"Melmed","year":"2025","journal-title":"Nat Rev Endocrinol"},{"issue":"6","key":"2026081210411843000_dgag311-B5","doi-asserted-by":"crossref","first-page":"838","DOI":"10.1210\/endrev\/bnaf020","article-title":"Medical treatment of acromegaly: navigating the present, shaping the future","volume":"46","author":"Gadelha","year":"2025","journal-title":"Endocr Rev"},{"issue":"1","key":"2026081210411843000_dgag311-B6","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1007\/s12020-021-02766-2","article-title":"GH and IGF-I levels and tumor shrinkage in response to first generation somatostatin receptor ligands in acromegaly: a comparative study between two reference centers for pituitary diseases in Brazil","volume":"74","author":"Manosso","year":"2021","journal-title":"Endocrine"},{"issue":"2","key":"2026081210411843000_dgag311-B7","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1210\/clinem\/dgab726","article-title":"The future of somatostatin receptor ligands in acromegaly","volume":"107","author":"Gadelha","year":"2022","journal-title":"J Clin Endocrinol Metab"},{"issue":"1","key":"2026081210411843000_dgag311-B8","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1080\/17446651.2023.2293107","article-title":"Biomarkers of response to treatment in acromegaly","volume":"19","author":"Kasuki","year":"2024","journal-title":"Expert Rev Endocrinol Metab"},{"issue":"25","key":"2026081210411843000_dgag311-B9","doi-asserted-by":"crossref","first-page":"2058","DOI":"10.1093\/eurheartj\/ehz056","article-title":"Deep learning for cardiovascular medicine: a practical primer","volume":"40","author":"Krittanawong","year":"2019","journal-title":"Eur Heart J"},{"issue":"6","key":"2026081210411843000_dgag311-B10","doi-asserted-by":"crossref","first-page":"4587","DOI":"10.1111\/dom.70720","article-title":"Artificial intelligence in diabetes care","volume":"28","author":"Dubsky","year":"2026","journal-title":"Diabetes Obes Metab"},{"issue":"26","key":"2026081210411843000_dgag311-B11","doi-asserted-by":"crossref","first-page":"2588","DOI":"10.1056\/NEJMc1906060","article-title":"Machine learning in medicine","volume":"380","author":"Van Calster","year":"2019","journal-title":"N Engl J Med"},{"issue":"3","key":"2026081210411843000_dgag311-B12","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1002\/mus.28023","article-title":"The role of artificial intelligence in electrodiagnostic and neuromuscular medicine: current state and future directions","volume":"69","author":"Taha","year":"2024","journal-title":"Muscle Nerve"},{"issue":"6","key":"2026081210411843000_dgag311-B13","doi-asserted-by":"crossref","first-page":"748","DOI":"10.1002\/lsm.23414","article-title":"Deep learning in biomedical optics","volume":"53","author":"Tian","year":"2021","journal-title":"Lasers Surg Med"},{"issue":"2","key":"2026081210411843000_dgag311-B14","doi-asserted-by":"crossref","first-page":"563","DOI":"10.1148\/radiol.2015151169","article-title":"Radiomics: images are more than pictures, they are data","volume":"278","author":"Gillies","year":"2016","journal-title":"Radiology"},{"issue":"1","key":"2026081210411843000_dgag311-B15","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1038\/s41746-023-00988-4","article-title":"Generative AI and large language models in health care: pathways to implementation","volume":"7","author":"Raza","year":"2024","journal-title":"NPJ Digit Med"},{"issue":"6","key":"2026081210411843000_dgag311-B16","doi-asserted-by":"crossref","first-page":"e296","DOI":"10.1161\/CIR.0000000000001202","article-title":"Value creation through artificial intelligence and cardiovascular imaging: a scientific statement from the American Heart Association","volume":"149","author":"Hanneman","year":"2024","journal-title":"Circulation"},{"issue":"3","key":"2026081210411843000_dgag311-B17","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1007\/s11102-025-01534-z","article-title":"Artificial intelligence in acromegaly: why, when and how","volume":"28","author":"Giustina","year":"2025","journal-title":"Pituitary"},{"issue":"3","key":"2026081210411843000_dgag311-B18","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1007\/s11102-025-01515-2","article-title":"Acromegaly facial changes analysis using last generation artificial intelligence methodology: the AcroFace system","volume":"28","author":"Rashwan","year":"2025","journal-title":"Pituitary"},{"issue":"3","key":"2026081210411843000_dgag311-B19","doi-asserted-by":"crossref","first-page":"e892","DOI":"10.1210\/clinem\/dgaf477","article-title":"Facial analysis in acromegaly using machine learning: toward earlier diagnosis","volume":"111","author":"Kocaman","year":"2026","journal-title":"J Clin Endocrinol Metab"},{"issue":"8","key":"2026081210411843000_dgag311-B20","doi-asserted-by":"crossref","first-page":"e1994","DOI":"10.1210\/clinem\/dgag027","article-title":"Automatic acromegaly detection using deep learning on hand images: a multicenter observational study","volume":"111","author":"Ohmachi","year":"2026","journal-title":"J Clin Endocrinol Metab"},{"issue":"4","key":"2026081210411843000_dgag311-B21","doi-asserted-by":"crossref","first-page":"983","DOI":"10.1210\/clinem\/dgae689","article-title":"Digital voice analysis as a biomarker of acromegaly","volume":"110","author":"Vouzouneraki","year":"2025","journal-title":"J Clin Endocrinol Metab"},{"issue":"1","key":"2026081210411843000_dgag311-B22","doi-asserted-by":"crossref","DOI":"10.1093\/ejendo\/lvad005","article-title":"Real-time detection of acromegaly from facial images with artificial intelligence","volume":"188","author":"Kizilgul","year":"2023","journal-title":"Eur J Endocrinol"},{"issue":"7","key":"2026081210411843000_dgag311-B23","doi-asserted-by":"crossref","first-page":"2074","DOI":"10.1210\/jc.2011-0237","article-title":"A novel approach to the detection of acromegaly: accuracy of diagnosis by automatic face classification","volume":"96","author":"Schneider","year":"2011","journal-title":"J Clin Endocrinol Metab"},{"key":"2026081210411843000_dgag311-B24","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.ebiom.2017.12.015","article-title":"Automatic detection of acromegaly from facial photographs using machine learning methods","volume":"27","author":"Kong","year":"2018","journal-title":"EBioMedicine"},{"issue":"1","key":"2026081210411843000_dgag311-B25","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1186\/s13045-020-00925-y","article-title":"Constructing an automatic diagnosis and severity-classification model for acromegaly using facial photographs by deep learning","volume":"13","author":"Kong","year":"2020","journal-title":"J Hematol Oncol"},{"issue":"5","key":"2026081210411843000_dgag311-B26","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1159\/000502211","article-title":"Deep-learning approach to automatic identification of facial anomalies in endocrine disorders","volume":"110","author":"Wei","year":"2020","journal-title":"Neuroendocrinology"},{"issue":"2","key":"2026081210411843000_dgag311-B27","doi-asserted-by":"crossref","DOI":"10.1210\/jendso\/bvaf203","article-title":"Detection of acromegaly from facial images using machine learning: a comparison with clinical experts","volume":"10","author":"Vouzouneraki","year":"2026","journal-title":"J Endocr Soc"},{"issue":"1","key":"2026081210411843000_dgag311-B28","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1186\/s12874-026-02856-7","article-title":"A general sample size framework for developing or updating a predictive algorithm: with application to clinical prediction models","volume":"26","author":"Riley","year":"2026","journal-title":"BMC Med Res Methodol"},{"issue":"4","key":"2026081210411843000_dgag311-B29","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1530\/EJE-07-0358","article-title":"AcroBel\u2013the Belgian registry on acromegaly: a survey of the \u2018real-life\u2019 outcome in 418 acromegalic subjects","volume":"157","author":"Bex","year":"2007","journal-title":"Eur J Endocrinol"},{"issue":"3","key":"2026081210411843000_dgag311-B30","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1530\/EJE-19-0784","article-title":"Fractionated radiotherapy and radiosurgery in acromegaly: analysis of 352 patients from the German acromegaly registry","volume":"182","author":"Knappe","year":"2020","journal-title":"Eur J Endocrinol"},{"issue":"5","key":"2026081210411843000_dgag311-B31","doi-asserted-by":"crossref","first-page":"582","DOI":"10.1007\/s11102-020-01063-x","article-title":"Prognostic factors of biochemical remission after transsphenoidal surgery for acromegaly: a structured review","volume":"23","author":"Agrawal","year":"2020","journal-title":"Pituitary"},{"issue":"3","key":"2026081210411843000_dgag311-B32","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1007\/s12020-018-1590-8","article-title":"Predictors of surgical outcome and early criteria of remission in acromegaly","volume":"60","author":"Antunes","year":"2018","journal-title":"Endocrine"},{"key":"2026081210411843000_dgag311-B33","doi-asserted-by":"crossref","first-page":"643","DOI":"10.3389\/fendo.2020.00643","article-title":"Development and interpretation of multiple machine learning models for predicting postoperative delayed remission of acromegaly patients during long-term follow-up","volume":"11","author":"Dai","year":"2020","journal-title":"Front Endocrinol (Lausanne)"},{"issue":"2","key":"2026081210411843000_dgag311-B34","doi-asserted-by":"crossref","first-page":"412","DOI":"10.1007\/s12020-019-02121-6","article-title":"Development and assessment of machine learning algorithms for predicting remission after transsphenoidal surgery among patients with acromegaly","volume":"67","author":"Fan","year":"2020","journal-title":"Endocrine"},{"issue":"1","key":"2026081210411843000_dgag311-B35","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1007\/s11102-020-01086-4","article-title":"Machine learning in predicting early remission in patients after surgical treatment of acromegaly: a multicenter study","volume":"24","author":"Qiao","year":"2021","journal-title":"Pituitary"},{"issue":"4","key":"2026081210411843000_dgag311-B36","doi-asserted-by":"crossref","first-page":"523","DOI":"10.1007\/s11102-021-01128-5","article-title":"Neural network modeling for prediction of recurrence, progression, and hormonal non-remission in patients following resection of functional pituitary adenomas","volume":"24","author":"Shahrestani","year":"2021","journal-title":"Pituitary"},{"issue":"3","key":"2026081210411843000_dgag311-B37","doi-asserted-by":"crossref","first-page":"486","DOI":"10.1007\/s11102-022-01216-0","article-title":"Machine learning as a clinical decision support tool for patients with acromegaly","volume":"25","author":"Sulu","year":"2022","journal-title":"Pituitary"},{"issue":"2","key":"2026081210411843000_dgag311-B38","doi-asserted-by":"crossref","first-page":"508","DOI":"10.1007\/s12020-021-02890-z","article-title":"Machine learning-based clinical outcome prediction in surgery for acromegaly","volume":"75","author":"Zanier","year":"2022","journal-title":"Endocrine"},{"key":"2026081210411843000_dgag311-B39","doi-asserted-by":"crossref","first-page":"1036","DOI":"10.1016\/j.wneu.2024.10.091","article-title":"Federated learning for predicting postoperative remission of patients with acromegaly: a multicentered study","volume":"193","author":"Zhang","year":"2025","journal-title":"World Neurosurg"},{"issue":"3","key":"2026081210411843000_dgag311-B40","doi-asserted-by":"crossref","first-page":"756","DOI":"10.3171\/2024.7.JNS24373","article-title":"Preoperative predictors of biochemical remission in somatotroph adenoma resections: a single-institution retrospective review","volume":"142","author":"Osorio","year":"2025","journal-title":"J Neurosurg"},{"issue":"1","key":"2026081210411843000_dgag311-B41","doi-asserted-by":"crossref","first-page":"E10","DOI":"10.3171\/2025.4.FOCUS2597","article-title":"A supervised machine learning approach for predicting the need for postsurgical intervention in acromegaly","volume":"59","author":"Shinya","year":"2025","journal-title":"Neurosurg Focus"},{"key":"2026081210411843000_dgag311-B42","doi-asserted-by":"crossref","first-page":"403","DOI":"10.3389\/fendo.2019.00403","article-title":"Preoperative noninvasive radiomics approach predicts tumor consistency in patients with acromegaly: development and multicenter prospective validation","volume":"10","author":"Fan","year":"2019","journal-title":"Front Endocrinol (Lausanne)"},{"issue":"8","key":"2026081210411843000_dgag311-B43","doi-asserted-by":"crossref","DOI":"10.1016\/j.arcmed.2023.102924","article-title":"Predictors of response to treatment with first-generation somatostatin receptor ligands in patients with acromegaly","volume":"54","author":"Marques-Pamies","year":"2023","journal-title":"Arch Med Res"},{"issue":"1","key":"2026081210411843000_dgag311-B44","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1210\/jc.2014-2468","article-title":"A structural and functional acromegaly classification","volume":"100","author":"Cuevas-Ramos","year":"2015","journal-title":"J Clin Endocrinol Metab"},{"issue":"2","key":"2026081210411843000_dgag311-B45","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1016\/j.eprac.2025.10.006","article-title":"Growth hormone assay-adjusted standardization reveals distinct clinical phenotypes in acromegaly","volume":"32","author":"Biagetti","year":"2026","journal-title":"Endocr Pract"},{"issue":"6","key":"2026081210411843000_dgag311-B46","doi-asserted-by":"crossref","first-page":"2731","DOI":"10.1007\/s00330-018-5876-2","article-title":"Predicting response to somatostatin analogues in acromegaly: machine learning-based high-dimensional quantitative texture analysis on T2-weighted MRI","volume":"29","author":"Kocak","year":"2019","journal-title":"Eur Radiol"},{"issue":"6","key":"2026081210411843000_dgag311-B47","doi-asserted-by":"crossref","first-page":"691","DOI":"10.1007\/s11102-020-01077-5","article-title":"Radiomics model predicts granulation pattern in growth hormone-secreting pituitary adenomas","volume":"23","author":"Park","year":"2020","journal-title":"Pituitary"},{"issue":"6","key":"2026081210411843000_dgag311-B48","doi-asserted-by":"crossref","first-page":"755","DOI":"10.1007\/s40618-019-01159-7","article-title":"Non-invasive and real-time proliferative activity estimation based on a quantitative radiomics approach for patients with acromegaly: a multicenter study","volume":"43","author":"Fan","year":"2020","journal-title":"J Endocrinol Invest"},{"issue":"1","key":"2026081210411843000_dgag311-B49","article-title":"An interpretable machine learning model for identifying granulation patterns in somatotroph tumors: a multi-center radiomics study","volume":"8","author":"Wang","year":"2026","journal-title":"Neurooncol Adv"},{"issue":"3","key":"2026081210411843000_dgag311-B50","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1530\/EJE-22-0339","article-title":"Digital quantification of somatostatin receptor subtype 2a immunostaining: a validation study","volume":"187","author":"Campana","year":"2022","journal-title":"Eur J Endocrinol"},{"issue":"1","key":"2026081210411843000_dgag311-B51","doi-asserted-by":"crossref","DOI":"10.1111\/bpa.13313","article-title":"Somatostatin receptors in pituitary somatotroph adenomas as predictors of response to somatostatin receptor ligands: a pathologist's perspective","volume":"35","author":"Botelho","year":"2025","journal-title":"Brain Pathol"},{"issue":"7","key":"2026081210411843000_dgag311-B52","doi-asserted-by":"crossref","first-page":"2047","DOI":"10.1210\/clinem\/dgab125","article-title":"Machine learning-based prediction model for treatment of acromegaly with first-generation somatostatin receptor ligands","volume":"106","author":"Wildemberg","year":"2021","journal-title":"J Clin Endocrinol Metab"},{"issue":"2","key":"2026081210411843000_dgag311-B53","doi-asserted-by":"crossref","first-page":"484","DOI":"10.1210\/clinem\/dgaf375","article-title":"Integrative machine learning approach for predicting resistance to first-generation receptor ligands in acromegaly","volume":"111","author":"Lin","year":"2026","journal-title":"J Clin Endocrinol Metab"},{"issue":"4","key":"2026081210411843000_dgag311-B54","doi-asserted-by":"crossref","DOI":"10.1016\/j.beem.2024.101893","article-title":"Predictors of biochemical response to somatostatin receptor ligands in acromegaly","volume":"38","author":"Marazuela","year":"2024","journal-title":"Best Pract Res Clin Endocrinol Metab"},{"issue":"1","key":"2026081210411843000_dgag311-B55","doi-asserted-by":"crossref","first-page":"8979","DOI":"10.1038\/s41598-022-12955-2","article-title":"Data mining analyses for precision medicine in acromegaly: a proof of concept","volume":"12","author":"Gil","year":"2022","journal-title":"Sci Rep"},{"issue":"5","key":"2026081210411843000_dgag311-B56","doi-asserted-by":"crossref","first-page":"463","DOI":"10.3803\/EnM.2023.1820","article-title":"Multiomics approach to acromegaly: unveiling translational insights for precision medicine","volume":"38","author":"Kim","year":"2023","journal-title":"Endocrinol Metab (Seoul)"},{"key":"2026081210411843000_dgag311-B57","doi-asserted-by":"crossref","first-page":"588","DOI":"10.3389\/fendo.2019.00588","article-title":"Machine learning-based radiomics predicts radiotherapeutic response in patients with acromegaly","volume":"10","author":"Fan","year":"2019","journal-title":"Front Endocrinol (Lausanne)"},{"issue":"1","key":"2026081210411843000_dgag311-B58","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1002\/path.5974","article-title":"Low-rank fusion convolutional neural network for prediction of remission after stereotactic radiosurgery in patients with acromegaly: a proof-of-concept study","volume":"258","author":"Qiao","year":"2022","journal-title":"J Pathol"}],"container-title":["The Journal of Clinical Endocrinology &amp; Metabolism"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/jcem\/advance-article-pdf\/doi\/10.1210\/clinem\/dgag311\/70409200\/dgag311.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/jcem\/advance-article-pdf\/doi\/10.1210\/clinem\/dgag311\/70409200\/dgag311.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T14:41:54Z","timestamp":1786545714000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/jcem\/advance-article\/doi\/10.1210\/clinem\/dgag311\/8748247"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,31]]},"references-count":58,"URL":"https:\/\/doi.org\/10.1210\/clinem\/dgag311","relation":{},"ISSN":["0021-972X","1945-7197"],"issn-type":[{"value":"0021-972X","type":"print"},{"value":"1945-7197","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,31]]},"article-number":"dgag311"}}