Deep Learning Analysis With Gray Scale and Doppler Ultrasonography Images to Differentiate Graves' Disease.

Publisher: Oxford University Press Country of Publication: United States NLM ID: 0375362 Publication Model: Print Cited Medium: Internet ISSN: 1945-7197 (Electronic) Linking ISSN: 0021972X NLM ISO Abbreviation: J Clin Endocrinol Metab Subsets: MEDLINE

Publication: 2017- : New York : Oxford University Press
Original Publication: Springfield, Ill. : Charles C. Thomas

Graves Disease*/diagnostic imaging ; Deep Learning* ; Ultrasonography, Doppler*/methods; Humans ; Female ; Male ; Adult ; Middle Aged ; Diagnosis, Differential ; Sensitivity and Specificity ; Thyroid Gland/diagnostic imaging ; Thyroiditis/diagnostic imaging ; Thyrotoxicosis/diagnostic imaging ; Aged ; Algorithms ; Retrospective Studies ; Young Adult

Context: Thyrotoxicosis requires accurate and expeditious differentiation between Graves' disease (GD) and thyroiditis to ensure effective treatment decisions.
Objective: This study aimed to develop a machine learning algorithm using ultrasonography and Doppler images to differentiate thyrotoxicosis subtypes, with a focus on GD.
Methods: 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® platform.
Results: 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.
Conclusion: Machine learning models based on ultrasound and Doppler images showed promising results with high accuracy and sensitivity in differentiating GD from thyroiditis.
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Keywords: Graves’ disease; artificial intelligence; neural networks computer; thyroiditis; thyrotoxicosis; ultrasonography

Date Created: 20240412 Date Completed: 20241015 Latest Revision: 20241015

20241016

10.1210/clinem/dgae254

38609169