专家论坛

人工智能时代甲状腺超声检查的应用与展望

展开
  • 上海交通大学医学院附属瑞金医院超声诊断科,上海 200025

收稿日期: 2021-10-08

  网络出版日期: 2022-07-27

Thyroid ultrasonography in era of artificial intelligence: application and prospect

Expand

Received date: 2021-10-08

  Online published: 2022-07-27

本文引用格式

詹维伟, 侯怡卿 . 人工智能时代甲状腺超声检查的应用与展望[J]. 外科理论与实践, 2021 , 26(06) : 500 -503 . DOI: 10.16139/j.1007-9610.2021.06.008

参考文献

[1] Kim J, Gosnell JE, Roman SA. Geographic influences in the global rise of thyroid cancer[J]. Nat Rev Endocrinol, 2020, 16(1):17-29.
[2] Li M, Dal Maso L, Vaccarella S. Global trends in thyroid cancer incidence and the impact of overdiagnosis[J]. Lancet Diabetes Endocrinol, 2020, 8(6):468-470.
[3] Sollini M, Cozzi L, Chiti A, et al. Texture analysis and machine learning to characterize suspected thyroid no-dules and differentiated thyroid cancer: where do we stand?[J]. Eur J Radiol, 2018, 99:1-8.
[4] Chang Y, Paul AK, Kim N, et al. Computer-aided diagnosis for classifying benign versus malignant thyroid no-dules based on ultrasound images: a comparison with radiologist-based assessments[J]. Med Phys, 2016, 43(1):554.
[5] Zhang B, Tian J, Pei S, et al. Machine learning- assisted system for thyroid nodule diagnosis[J]. Thyroid, 2019, 29(6):858-867.
[6] Gao L, Liu R, Jiang Y, et al. Computer-aided system for diagnosing thyroid nodules on ultrasound: a comparison with radiologist-based clinical assessments[J]. Head Neck, 40(4):778-783.
[7] Wang L, Yang S, Yang S, et al. Automatic thyroid nodule recognition and diagnosis in ultrasound imaging with the YOLOv2 neural network[J]. World J Surg Oncol, 2019, 17(1):12.
[8] Li X, Zhang S, Zhang Q, et al. Diagnosis of thyroid cancer using deep convolutional neural network models applied to sonographic images: a retrospective, multicohort, diagnostic study[J]. Lancet Oncol, 2019, 20(2):193-201.
[9] Akselrod-Ballin A, Chorev M, Shoshan Y, et al. Predic-ting breast cancer by applying deep learning to linked health records and mammograms[J]. Radiology, 2019, 292(2):331-342.
[10] Liu T, Guo Q, Lian C, et al. Automated detection and classification of thyroid nodules in ultrasound images using clinical-knowledge-guided convolutional neural networks[J]. Med Image Anal, 2019, 58:101555.
[11] Fang H, Gong L, Xu Y, et al. Reliable thyroid carcinoma detection with real-time intelligent analysis of ultrasound images[J]. Ultrasound Med Biol, 2021, 47(3):590-602.
[12] Hou Y, Chen C, Zhang L, et al. Using deep neural network to diagnose thyroid nodules on ultrasound in patients with Hashimoto's thyroiditis[J]. Front Oncol, 2021, 11:614172.
[13] Yi J, Kang HK, Kwon JH, et al. Technology trends and applications of deep learning in ultrasonography: image quality enhancement, diagnostic support, and improving workflow efficiency[J]. Ultrasonography, 2021, 40(1):7-22.
[14] Choi YJ, Baek JH, Park HS, et al. A computer-aided dia-gnosis system using arti cial intelligence for the diagnosis and characterization of thyroid nodules on ultrasound: initial clinical assessment[J]. Thyroid, 2017, 27(4):546-552.
[15] Kim HL, Ha EJ, Han M. Real-world performance of computer-aided diagnosis system for thyroid nodules using ultrasonography[J]. Ultrasound Med Biol, 2019, 45(10):2672-2678.
[16] Buda M, Wildman-Tobriner B, Hoang JK, et al. Management of thyroid nodules seen on us images: deep learning may match performance of radiologists[J]. Radiology, 2019, 292(3):695-701.
[17] Daniels K, Gummadi S, Zhu Z, et al. Machine learning by ultrasonography for genetic risk stratification of thyroid nodules[J]. JAMA Otolaryngol Head Neck Surg, 2020, 146(1):36-41.
[18] Wang S, Xu J, Tahmasebi A, et al. Incorporation of a machine learning algorithm with object detection within the thyroid imaging reporting and data system improves the diagnosis of genetic risk[J]. Front Oncol, 2020, 10:591846.
[19] Zhang Y, Wu Q, Chen Y, et al. A clinical assessment of an ultrasound computer-aided diagnosis system in diffe-rentiating thyroid nodules with radiologists of different diagnostic experience[J]. Front Oncol, 2020, 10:557169.
[20] 李潜, 丁思悦, 郭兰伟, 等. 甲状腺结节超声恶性危险分层中国指南(C-TIRADS)联合人工智能辅助诊断对甲状腺结节鉴别诊断的效能评估[J]. 中华超声影像学杂志, 2021, 30(3):231-235.
[21] Thomas J, Haertling T. AIBx, artificial intelligence model to risk stratify thyroid nodules[J]. Thyroid, 2020, 30(6):878-884.
[22] Lee JH, Baek JH, Kim JH, et al. Deep learning-based computer-aided diagnosis system for localization and dia-gnosis of metastatic lymph nodes on ultrasound: a pilot study[J]. Thyroid, 2018, 28(10):1332-1338.
[23] Yu J, Deng Y, Liu T, et al. Lymph node metastasis prediction of papillary thyroid carcinoma based on transfer learning radiomics[J]. Nat Commun, 2020, 11(1):4807.
[24] Reverter JL, Vazquez F, Puig-Domingo M. Diagnostic performance evaluation of a computer-assisted imaging analysis system for ultrasound risk stratification of thyroid nodules[J]. Am J Roentgenol, 2019, 213(1):169-174.
[25] Chung SR, Baek JH, Lee MK, et al. Computer-aided dia-gnosis system for the evaluation of thyroid nodules on ultrasonography: prospective non-inferiority study accor-ding to the experience level of radiologists[J]. Korean J Radiol, 2020, 21(3):369-376.
[26] Jeong EY, Kim HL, Ha EJ, et al. Computer-aided diagnosis system for thyroid nodules on ultrasonography: dia-gnostic performance and reproducibility based on the experience level of operators[J]. Eur Radiol, 2019, 29(4):1978-1985.
文章导航

/