{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T13:00:48Z","timestamp":1791464448804,"version":"4.3.4"},"reference-count":62,"publisher":"The Endocrine Society","issue":"11","license":[{"start":{"date-parts":[[2024,4,13]],"date-time":"2024-04-13T00:00:00Z","timestamp":1712966400000},"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":[[2024,10,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Context<\/jats:title>\n                  <jats:p>Thyrotoxicosis requires accurate and expeditious differentiation between Graves\u2019 disease (GD) and thyroiditis to ensure effective treatment decisions.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Objective<\/jats:title>\n                  <jats:p>This study aimed to develop a machine learning algorithm using ultrasonography and Doppler images to differentiate thyrotoxicosis subtypes, with a focus on GD.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Methods<\/jats:title>\n                  <jats:p>This study included patients who initially presented with thyrotoxicosis and underwent thyroid ultrasonography at a single tertiary hospital. A total of 7719 ultrasonography images from 351 patients with GD and 2980 images from 136 patients with thyroiditis were used. Data augmentation techniques were applied to enhance the algorithm's performance. Two deep learning models, Xception and EfficientNetB0_2, were employed. Performance metrics such as accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and F1 score were calculated for both models. Image preprocessing, neural network model generation, and neural network training results verification were performed using DEEP:PHI\u00ae platform.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>The Xception model achieved 84.94% accuracy, 89.26% sensitivity, 73.17% specificity, 90.06% PPV, 71.43% NPV, and an F1 score of 89.66 for the diagnosis of GD. The EfficientNetB0_2 model exhibited 85.31% accuracy, 90.28% sensitivity, 71.78% specificity, 89.71% PPV, 73.05% NPV, and an F1 score of 89.99.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusion<\/jats:title>\n                  <jats:p>Machine learning models based on ultrasound and Doppler images showed promising results with high accuracy and sensitivity in differentiating GD from thyroiditis.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1210\/clinem\/dgae254","type":"journal-article","created":{"date-parts":[[2024,4,11]],"date-time":"2024-04-11T15:29:08Z","timestamp":1712849348000},"page":"2872-2881","source":"Crossref","is-referenced-by-count":7,"title":["Deep Learning Analysis With Gray Scale and Doppler Ultrasonography Images to Differentiate Graves\u2019 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