{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,21]],"date-time":"2026-08-21T11:56:15Z","timestamp":1787313375587,"version":"build-2736575974"},"reference-count":37,"publisher":"The Endocrine Society","issue":"8","license":[{"start":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T00:00:00Z","timestamp":1772150400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Hyogo Foundation for Science Technology"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,7,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Context<\/jats:title>\n                    <jats:p>Acromegaly poses clinical challenges in terms of early diagnosis and intervention. Therefore, the development of novel diagnostic tools is essential. Although artificial intelligence (AI) models based on external appearance have been proposed, privacy concerns have limited their use.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To develop a privacy-conscious deep learning model for detecting acromegaly using hand images.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This nationwide multicenter study enrolled 716 patients (317 with acromegaly and 399 controls) and 11 480 images from 15 Japanese pituitary centers. The inclusion criteria were age \u226518 years and care received at the participating facilities. Hand images focusing on the dorsal and fist sign, excluding the palm\/fingerprint regions, were used to develop the model. The data were split into training\/validation (12 centers) and test (3 centers) datasets. A ResNet-50-based model was trained using PyTorch with data augmentation and 5-fold cross-validation. For each patient, the predictions were averaged over 4 images. The performance of the model was compared with that of endocrinologists.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The model achieved a sensitivity of 0.89, specificity of 0.91, positive predictive value of 0.88, negative predictive value of 0.93, F1-score of 0.89, and an area under the receiver operating characteristic curve of 0.96, outperforming specialists (F1-score range: 0.43-0.63).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This study highlights the utility of dorsal hand and fist sign as diagnostic clues for acromegaly, which the AI model captured more accurately than endocrinologists. Using this privacy-conscious feature, this model can be deployed in public settings like health checkups. Further validation using larger datasets, including healthy individuals and diverse diseases, is necessary.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1210\/clinem\/dgag027","type":"journal-article","created":{"date-parts":[[2026,1,22]],"date-time":"2026-01-22T12:54:17Z","timestamp":1769086457000},"page":"e1994-e2005","source":"Crossref","is-referenced-by-count":2,"title":["Automatic acromegaly detection using deep learning on hand images: a multicenter observational study"],"prefix":"10.1210","volume":"111","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4470-5980","authenticated-orcid":false,"given":"Yuka","family":"Ohmachi","sequence":"first","affiliation":[{"name":"Division of Diabetes and Endocrinology, Department of Internal Medicine, Kobe University Graduate School of Medicine , Kobe 650-0017, Hyogo ,","place":["Japan"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mizuho","family":"Nishio","sequence":"additional","affiliation":[{"name":"Department of Radiology, Kobe University Graduate School of Medicine , Kobe 650-0017, Hyogo ,","place":["Japan"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7545-9751","authenticated-orcid":false,"given":"Ichiro","family":"Abe","sequence":"additional","affiliation":[{"name":"Department of Endocrinology and Diabetes Mellitus, Fukuoka University Chikushi Hospital , Chikushino 818-8502, Fukuoka ,","place":["Japan"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kunihisa","family":"Kobayashi","sequence":"additional","affiliation":[{"name":"Department of Endocrinology and Diabetes Mellitus, Fukuoka University Chikushi Hospital , Chikushino 818-8502, Fukuoka ,","place":["Japan"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tomoko","family":"Iida","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Hyogo Medical University , Nishinomiya 663-8501, Hyogo ,","place":["Japan"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Manabu","family":"Shirakawa","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Hyogo Medical University , Nishinomiya 663-8501, Hyogo ,","place":["Japan"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuichi","family":"Nagata","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Nagoya University Graduate School of Medicine , Nagoya 466-8560, Aichi ,","place":["Japan"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kazuhito","family":"Takeuchi","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Nagoya University Graduate School of Medicine , Nagoya 466-8560, Aichi ,","place":["Japan"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Akira","family":"Taguchi","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Graduate School of Biomedical and Health Sciences, Hiroshima University , Hiroshima 734-8551, Hiroshima ,","place":["Japan"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yasuyuki","family":"Kinoshita","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Graduate School of Biomedical and Health Sciences, Hiroshima University , Hiroshima 734-8551, Hiroshima ,","place":["Japan"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Noriaki","family":"Fukuhara","sequence":"additional","affiliation":[{"name":"Department of Hypothalamic and Pituitary Surgery, Toranomon Hospital , Minato-ku 105-8470, Tokyo ,","place":["Japan"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9279-5861","authenticated-orcid":false,"given":"Hiroshi","family":"Nishioka","sequence":"additional","affiliation":[{"name":"Department of Hypothalamic and Pituitary Surgery, Toranomon Hospital , Minato-ku 105-8470, Tokyo ,","place":["Japan"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shigeyuki","family":"Tahara","sequence":"additional","affiliation":[{"name":"Department of Neurological Surgery, Nippon Medical School Musashikosugi Hospital , Kawasaki 211-8533, Kanagawa ,","place":["Japan"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shingo","family":"Fujio","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Graduate School of Medical and Dental Sciences, Kagoshima University , Kagoshima 890-8520, Kagoshima ,","place":["Japan"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Takafumi","family":"Ogura","sequence":"additional","affiliation":[{"name":"Division of Neurosurgery, Department of Brain and Neurosciences, Faculty of Medicine, Tottori University , Yonago 683-8504, Tottori 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