The current situation and prospects of diagnosis and treatment of thyroid nodules in the era of artificial intelligence_CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY

Authors：

 TIAN Wen

Department of Thyroid and Hernia Surgery, Medical Department of General Surgery, the First Medical Center of Chinese PLA General Hospital, Beijing 100853, P. R. China;

Corresponding?author：

TIAN Wen, Email: tianwen301_cta01@163.com

Keywords：

artificial intelligence; thyroid cancer; smart healthcare; internet+thyroid

DOI：

10.7507/1007-9424.202506098

Video：

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In recent years, the incidence of thyroid cancer has risen significantly, making it the third most common malignant tumor among women in China. With the rapid development of artificial intelligence technology, the diagnosis and treatment models of thyroid nodules are undergoing profound changes. By efficiently processing massive amounts of data, providing precise analysis and personalized suggestions, artificial intelligence has significantly promoted the integrated development of thyroid cancer in preoperative diagnosis, surgical navigation, surgical methods and postoperative management, thus facilitating the realization of “precision medicine and smart medicine” in thyroid surgery. This article focuses on the current development status of the “Internet+thyroid” model and looks forward to its future development.

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2.	田文, 石臣磊, 萬政. 我國甲狀腺癌外科治療近10年進展. 中國實用外科雜志, 2022, 42(8): 841-844.
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5.	王冰, 張明博, 萬政, 等. 動態人工智能超聲輔助診斷系統對甲狀腺結節診斷價值研究. 中國實用外科雜志, 2022, 42(6): 680-684.
6.	劉志艷, 劉東戈. 我國甲狀腺結節細胞病理近十年的回顧與展望. 中華病理學雜志, 2025, 54(6): 575-579.
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8.	花蘇榕, 王智弘, 李佳頤, 等. 深度學習技術識別喉返神經在經胸乳入路腔鏡甲狀腺手術中的探索. 中華內分泌外科雜志, 2022, 16(3): 287-292.
9.	花蘇榕, 王智弘, 高俊義, 等. 深度學習技術識別喉返神經在經腋窩腔鏡甲狀腺手術中的探索. 中華內分泌外科雜志, 2022, 16(1): 5-11.
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12.	Wang B, Zheng J, Yu JF, et al. Development of artificial intelligence for parathyroid recognition during endoscopic thyroid surgery. Laryngoscope, 2022, 132(12): 2516-2523.
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14.	田文, 姚京, 王冰, 等. 5G遠程手術機器人輔助甲狀腺癌根治術初步研究. 中國實用外科雜志, 2024, 44(9): 1075-1077,1080.
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19.	Mao Y, Huang Y, Xu L, et al. Surgical methods and social factors are associated with long-term survival in follicular thyroid carcinoma: construction and validation of a prognostic model based on machine learning algorithms. Front Oncol, 2022, 12: 816427. doi: 10.3389/fonc.2022.816427.
20.	Kim SY, Kim YI, Kim HJ, et al. New approach of prediction of recurrence in thyroid cancer patients using machine learning. Medicine (Baltimore), 2021, 100(42): e27493. doi: 10.1097/MD.0000000000027493.
21.	Park YM, Lee BJ. Machine learning-based prediction model using clinico-pathologic factors for papillary thyroid carcinoma recurrence. Sci Rep, 2021, 11(1): 4948. doi: 10.1038/s41598-021-84504-2.
22.	Gruson D, Dabla P, Stankovic S, et al. Artificial intelligence and thyroid disease management: considerations for thyroid function tests. Biochem Med (Zagreb), 2022, 32(2): 020601. doi: 10.11613/BM.2022.020601.
23.	Zeng H, Zheng R, Guo Y, et al. Cancer survival in China, 2003-2005: a population-based study. Int J Cancer, 2015, 136(8): 1921-1930.
24.	Zeng H, Zheng R, Sun K, et al. Cancer survival statistics in China 2019-2021: a multicenter, population-based study. J Natl Cancer Cent, 2024, 4(3): 203-213.

1. 王少明, 鄭榮壽, 韓冰峰, 等. 2022年中國人群惡性腫瘤發病與死亡年齡特征分析. 中國腫瘤, 2024, 33(3): 165-174.
2. 田文, 石臣磊, 萬政. 我國甲狀腺癌外科治療近10年進展. 中國實用外科雜志, 2022, 42(8): 841-844.
3. Tessler FN, Middleton WD, Grant EG, et al. ACR Thyroid imaging, reporting and data system (TI-RADS) : white paper of the ACR TI-RADS committee. J Am Coll Radiol, 2017, 14(5): 587-595.
4. 萬政, 王冰, 惠慶磊, 等. 人工智能技術聯合甲狀腺超聲影像和數據系統 (TI-RADS) 分級對甲狀腺結節良惡性診斷價值的研究. 中華內分泌外科雜志, 2022, 16(2): 185-189.
5. 王冰, 張明博, 萬政, 等. 動態人工智能超聲輔助診斷系統對甲狀腺結節診斷價值研究. 中國實用外科雜志, 2022, 42(6): 680-684.
6. 劉志艷, 劉東戈. 我國甲狀腺結節細胞病理近十年的回顧與展望. 中華病理學雜志, 2025, 54(6): 575-579.
7. Hu G, Niu W, Ge J, et al. Identification of thyroid cancer biomarkers using WGCNA and machine learning. Eur J Med Res, 2025, 30(1): 244. doi: 10.1186/s40001-025-02466-x.
8. 花蘇榕, 王智弘, 李佳頤, 等. 深度學習技術識別喉返神經在經胸乳入路腔鏡甲狀腺手術中的探索. 中華內分泌外科雜志, 2022, 16(3): 287-292.
9. 花蘇榕, 王智弘, 高俊義, 等. 深度學習技術識別喉返神經在經腋窩腔鏡甲狀腺手術中的探索. 中華內分泌外科雜志, 2022, 16(1): 5-11.
10. 田文, 陳志達, 郗洪慶, 等. 近紅外自體熒光顯像技術在甲狀腺癌根治性手術中輔助甲狀旁腺識別應用1例報告并文獻復習. 中國實用外科雜志, 2020, 40(5): 591-593.
11. Avci SN, Isiktas G, Berber E. A visual deep learning model to localize parathyroid-specific autofluorescence on near-infrared imaging: localization of parathyroid autofluorescence with deep learning. Ann Surg Oncol, 2022 Mar 28. doi: 10.1245/s10434-022-11632-y.
12. Wang B, Zheng J, Yu JF, et al. Development of artificial intelligence for parathyroid recognition during endoscopic thyroid surgery. Laryngoscope, 2022, 132(12): 2516-2523.
13. Yao S, Shen P, Dai F, et al. Thyroid cancer central lymph node metastasis risk stratification based on homogeneous positioning deep learning. Research (Wash D C), 2024, 7: 0432. doi: 10.34133/research.0432.
14. 田文, 姚京, 王冰, 等. 5G遠程手術機器人輔助甲狀腺癌根治術初步研究. 中國實用外科雜志, 2024, 44(9): 1075-1077,1080.
15. Lee D, Kong HJ, Kim D, et al. Preliminary study on application of augmented reality visualization in robotic thyroid surgery. Ann Surg Treat Res, 2018, 95(6): 297-302.
16. Lee JH, Lee CY, Eom JS, et al. Predictions for three-month postoperative vocal recovery after thyroid surgery from spectrograms with deep neural network. Sensors (Basel), 2022, 22(17): 6387. doi: 10.3390/s22176387.
17. Seib CD, Roose JP, Hubbard AE, et al. Ensemble machine learning for the prediction of patient-level outcomes following thyroidectomy. Am J Surg, 2021, 222(2): 347-353.
18. Yang CQ, Gardiner L, Wang H, et al. Creating prognostic systems for well-differentiated thyroid cancer using machine learning. Front Endocrinol (Lausanne), 2019 May 8: 10: 288. doi: 10.3389/fendo.2019.00288.
19. Mao Y, Huang Y, Xu L, et al. Surgical methods and social factors are associated with long-term survival in follicular thyroid carcinoma: construction and validation of a prognostic model based on machine learning algorithms. Front Oncol, 2022, 12: 816427. doi: 10.3389/fonc.2022.816427.
20. Kim SY, Kim YI, Kim HJ, et al. New approach of prediction of recurrence in thyroid cancer patients using machine learning. Medicine (Baltimore), 2021, 100(42): e27493. doi: 10.1097/MD.0000000000027493.
21. Park YM, Lee BJ. Machine learning-based prediction model using clinico-pathologic factors for papillary thyroid carcinoma recurrence. Sci Rep, 2021, 11(1): 4948. doi: 10.1038/s41598-021-84504-2.
22. Gruson D, Dabla P, Stankovic S, et al. Artificial intelligence and thyroid disease management: considerations for thyroid function tests. Biochem Med (Zagreb), 2022, 32(2): 020601. doi: 10.11613/BM.2022.020601.
23. Zeng H, Zheng R, Guo Y, et al. Cancer survival in China, 2003-2005: a population-based study. Int J Cancer, 2015, 136(8): 1921-1930.
24. Zeng H, Zheng R, Sun K, et al. Cancer survival statistics in China 2019-2021: a multicenter, population-based study. J Natl Cancer Cent, 2024, 4(3): 203-213.

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