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Diagnostic value of a dynamic artificial intelligence ultrasonic intelligent auxiliary diagnosis system for benign and malignant thyroid nodules in patients with Hashimoto thyroiditis

BACKGROUND: A dynamic artificial intelligence (AI) ultrasonic intelligent assistant diagnosis system (dynamic AI) is a joint application of AI technology and medical imaging, which can conduct real-time synchronous dynamic analysis of nodules from multiple sectional views with different angles. This...

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Autores principales: Wang, Bing, Wan, Zheng, Zhang, Mingbo, Gong, Fengxia, Zhang, Linlin, Luo, Yukun, Yao, Jing, Li, Chen, Tian, Wen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: AME Publishing Company 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10240020/
https://www.ncbi.nlm.nih.gov/pubmed/37284122
http://dx.doi.org/10.21037/qims-22-889
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author Wang, Bing
Wan, Zheng
Zhang, Mingbo
Gong, Fengxia
Zhang, Linlin
Luo, Yukun
Yao, Jing
Li, Chen
Tian, Wen
author_facet Wang, Bing
Wan, Zheng
Zhang, Mingbo
Gong, Fengxia
Zhang, Linlin
Luo, Yukun
Yao, Jing
Li, Chen
Tian, Wen
author_sort Wang, Bing
collection PubMed
description BACKGROUND: A dynamic artificial intelligence (AI) ultrasonic intelligent assistant diagnosis system (dynamic AI) is a joint application of AI technology and medical imaging, which can conduct real-time synchronous dynamic analysis of nodules from multiple sectional views with different angles. This study explored the diagnostic value of dynamic AI for benign and malignant thyroid nodules in patients with Hashimoto thyroiditis (HT) and its significance in guiding surgical treatment strategies. METHODS: Data of 487 patients (154 with and 333 without HT) with 829 thyroid nodules who underwent surgery were collected. Differentiation of benign and malignant nodules was performed using dynamic AI, and diagnostic effects (specificity, sensitivity, negative predictive value, positive predictive value, accuracy, misdiagnosis rate and missed diagnosis rate) was assessed. Differences in diagnostic efficacy were compared among AI, preoperative ultrasound based on the American College of Radiology (ACR) Thyroid Imaging Reporting and Data System (TI-RADS), and fine needle aspiration cytology (FNAC) diagnoses. RESULTS: The accuracy, specificity and sensitivity of dynamic AI reached 88.06%, 80.19%, and 90.68%, respectively; besides, there was consistency with postoperative pathological consequences (κ=0.690; P<0.001). The diagnostic efficacy of dynamic AI was equivalent between patients with and without HT, and there were no significant differences in sensitivity, specificity, accuracy, positive predictive value, negative predictive value, missed diagnosis rate, and misdiagnosis rate. In patients with HT, dynamic AI had significantly higher specificity and a lower misdiagnosis rate than did preoperative ultrasound based on the ACR TI-RADS (P<0.05). Compared with FNAC diagnosis, dynamic AI had a significantly higher sensitivity and a lower missed diagnosis rate (P<0.05). CONCLUSIONS: Dynamic AI possessed an elevated diagnostic worth of malignant and benign thyroid nodules in patients with HT, which can provide a new method and valuable information for the diagnosis and development of management strategy of patients.
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spelling pubmed-102400202023-06-06 Diagnostic value of a dynamic artificial intelligence ultrasonic intelligent auxiliary diagnosis system for benign and malignant thyroid nodules in patients with Hashimoto thyroiditis Wang, Bing Wan, Zheng Zhang, Mingbo Gong, Fengxia Zhang, Linlin Luo, Yukun Yao, Jing Li, Chen Tian, Wen Quant Imaging Med Surg Original Article BACKGROUND: A dynamic artificial intelligence (AI) ultrasonic intelligent assistant diagnosis system (dynamic AI) is a joint application of AI technology and medical imaging, which can conduct real-time synchronous dynamic analysis of nodules from multiple sectional views with different angles. This study explored the diagnostic value of dynamic AI for benign and malignant thyroid nodules in patients with Hashimoto thyroiditis (HT) and its significance in guiding surgical treatment strategies. METHODS: Data of 487 patients (154 with and 333 without HT) with 829 thyroid nodules who underwent surgery were collected. Differentiation of benign and malignant nodules was performed using dynamic AI, and diagnostic effects (specificity, sensitivity, negative predictive value, positive predictive value, accuracy, misdiagnosis rate and missed diagnosis rate) was assessed. Differences in diagnostic efficacy were compared among AI, preoperative ultrasound based on the American College of Radiology (ACR) Thyroid Imaging Reporting and Data System (TI-RADS), and fine needle aspiration cytology (FNAC) diagnoses. RESULTS: The accuracy, specificity and sensitivity of dynamic AI reached 88.06%, 80.19%, and 90.68%, respectively; besides, there was consistency with postoperative pathological consequences (κ=0.690; P<0.001). The diagnostic efficacy of dynamic AI was equivalent between patients with and without HT, and there were no significant differences in sensitivity, specificity, accuracy, positive predictive value, negative predictive value, missed diagnosis rate, and misdiagnosis rate. In patients with HT, dynamic AI had significantly higher specificity and a lower misdiagnosis rate than did preoperative ultrasound based on the ACR TI-RADS (P<0.05). Compared with FNAC diagnosis, dynamic AI had a significantly higher sensitivity and a lower missed diagnosis rate (P<0.05). CONCLUSIONS: Dynamic AI possessed an elevated diagnostic worth of malignant and benign thyroid nodules in patients with HT, which can provide a new method and valuable information for the diagnosis and development of management strategy of patients. AME Publishing Company 2023-04-04 2023-06-01 /pmc/articles/PMC10240020/ /pubmed/37284122 http://dx.doi.org/10.21037/qims-22-889 Text en 2023 Quantitative Imaging in Medicine and Surgery. All rights reserved. https://creativecommons.org/licenses/by-nc-nd/4.0/Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0 (https://creativecommons.org/licenses/by-nc-nd/4.0/) .
spellingShingle Original Article
Wang, Bing
Wan, Zheng
Zhang, Mingbo
Gong, Fengxia
Zhang, Linlin
Luo, Yukun
Yao, Jing
Li, Chen
Tian, Wen
Diagnostic value of a dynamic artificial intelligence ultrasonic intelligent auxiliary diagnosis system for benign and malignant thyroid nodules in patients with Hashimoto thyroiditis
title Diagnostic value of a dynamic artificial intelligence ultrasonic intelligent auxiliary diagnosis system for benign and malignant thyroid nodules in patients with Hashimoto thyroiditis
title_full Diagnostic value of a dynamic artificial intelligence ultrasonic intelligent auxiliary diagnosis system for benign and malignant thyroid nodules in patients with Hashimoto thyroiditis
title_fullStr Diagnostic value of a dynamic artificial intelligence ultrasonic intelligent auxiliary diagnosis system for benign and malignant thyroid nodules in patients with Hashimoto thyroiditis
title_full_unstemmed Diagnostic value of a dynamic artificial intelligence ultrasonic intelligent auxiliary diagnosis system for benign and malignant thyroid nodules in patients with Hashimoto thyroiditis
title_short Diagnostic value of a dynamic artificial intelligence ultrasonic intelligent auxiliary diagnosis system for benign and malignant thyroid nodules in patients with Hashimoto thyroiditis
title_sort diagnostic value of a dynamic artificial intelligence ultrasonic intelligent auxiliary diagnosis system for benign and malignant thyroid nodules in patients with hashimoto thyroiditis
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10240020/
https://www.ncbi.nlm.nih.gov/pubmed/37284122
http://dx.doi.org/10.21037/qims-22-889
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