{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T18:48:19Z","timestamp":1787251699407,"version":"build-2736575974"},"reference-count":34,"publisher":"The Endocrine Society","issue":"3","license":[{"start":{"date-parts":[[2025,8,21]],"date-time":"2025-08-21T00:00:00Z","timestamp":1755734400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,2,20]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Context<\/jats:title>\n                    <jats:p>Acromegaly is a rare and progressive disorder often diagnosed late due to its insidious onset and gradually evolving facial features. Early detection remains a critical unmet need to reduce disease-associated morbidity and mortality.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This study aimed to develop and evaluate machine learning models that can identify acromegaly-specific facial features using prediagnostic photographs, potentially enabling earlier diagnosis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A total of 489 facial photographs from 92 patients with acromegaly and 254 images from 88 controls were analyzed. A 2-stage pipeline was implemented: (1) deep feature extraction using a pretrained VGG-Face model followed by support vector machine (SVM) classification, and (2) an interpretable model using 5 landmark-based facial measurements. Separate data sets were created using prediagnosis, postdiagnosis, and combined images to evaluate model performance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The best classification results were obtained from the prediagnosis data set (mean 7.47 years before diagnosis), with an area under the curve (AUC) of 0.982 and accuracy of 91.5%. Interpretability analyses highlighted maxillary, nasal, and orbital regions as key facial zones. The interpretable model, using facial ratios, achieved moderate accuracy (AUC\u2005=\u20050.776) while providing clinical insight into contributing features such as face width-to-height ratio and philtrum height.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Our findings demonstrate that acromegaly-related facial features can be detected years before clinical diagnosis using machine learning. By combining high-performance deep models with interpretable approaches, this study supports the potential for artificial intelligence\u2013based facial screening tools to aid in early detection of acromegaly.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1210\/clinem\/dgaf477","type":"journal-article","created":{"date-parts":[[2025,8,20]],"date-time":"2025-08-20T11:51:09Z","timestamp":1755690669000},"page":"e892-e899","source":"Crossref","is-referenced-by-count":6,"title":["Facial Analysis in Acromegaly Using Machine Learning: Toward Earlier Diagnosis"],"prefix":"10.1210","volume":"111","author":[{"given":"Banu Betul","family":"Kocaman","sequence":"first","affiliation":[{"name":"Division of Endocrinology and Metabolic Diseases, Department of Internal Medicine, Cerrahpasa Faculty of Medicine, Istanbul University-Cerrahpasa , Istanbul ,","place":["Turkey"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Oguzhan 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