(Jakarta Global University, Indonesia)(2) Muhammad Haikal Satria
(Jakarta Global University, Indonesia)(3) Yanuar Zulardiansyah Arief
(Universiti Malaysia Sarawak, Malaysia)(4) Antonius Darma Setiawan
(Jakarta Global University, Indonesia)(5) Agung Pangestu
(Jakarta Global University, Indonesia)(6) Hexa Apriliana Hidayah
(Universitas Jenderal Soedirman, Indonesia)*corresponding author
AbstractThe thyroid gland plays a very important role in hormonal regulation in the human body. If the thyroid gland has a disorder, it can affect the performance of body functions. The development of artificial intelligence technology today allows an expert such as a doctor to be helped by his work. One of the important roles of artificial intelligence is helping doctors, among others, to diagnose a patient to determine appropriate post-diagnosis care. The study aims to shed light on the role of artificial intelligence in the treatment of thyroid disorders.
The thyroid gland plays a very important role in hormonal regulation in the human body. If the thyroid gland has a disorder, it can affect the performance of body functions. The development of artificial intelligence technology today allows an expert such as a doctor to be helped by his work. One of the important roles of artificial intelligence is helping doctors, among others, to diagnose a patient to determine appropriate post-diagnosis care. The study aims to shed light on the role of artificial intelligence in the treatment of thyroid disorders. KeywordsDiagnose; Early detection; Expert system; Healthcare; Machine learning
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DOIhttps://doi.org/10.31763/sitech.v2i2.694 |
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References
[1] Azamris, Buku Ajar Kelainan Tiroid. Yogyakarta (ID): Deepublish Publisher, 2020. Available at : books.google.co.id.
[2] J. P. Walsh, “Managing thyroid disease in general practice,” Med. J. Aust., vol. 205, no. 4, pp. 179–184, Aug. 2016, doi: 10.5694/mja16.00545.
[3] A. Tyagi, R. Mehra, and A. Saxena, “Interactive thyroid disease prediction system using machine learning technique,” in PDGC 2018 - 2018 5th International Conference on Parallel, Distributed and Grid Computing, Institute of Electrical and Electronics Engineers Inc., Dec. 2018, pp. 689–693. doi: 10.1109/PDGC.2018.8745910.
[4] R. R. Al Hakim, Prihantini, and G. E. Setyowisnu, “Expert System Framework Design for Diagnosis of Thyroid Disorders,” in The 1st Science and Technology Students Conference "Indonesian Local Wisdom: Potentials and Challenges, Jakarta (ID): MITI Klaster Mahasiswa, 2021. Available at :researchgate.net.
[5] X. Chai, “Diagnosis Method of Thyroid Disease Combining Knowledge Graph and Deep Learning,” IEEE Access, vol. 8, pp. 149787–149795, 2020, doi: 10.1109/ACCESS.2020.3016676.
[6] P. Penikalapati and A. N. Rao, “Healthcare analytics by engaging machine learning,” Sci. Inf. Technol. Lett., vol. 1, no. 1, pp. 24–39, 2020, doi: 10.31763/SITECH.V1I1.32.
[7] V. K. Gunjan, J. M. Zurada, B. Raman, and G. R. Gangadharan, Modern Approaches in Machine Learning and Cognitive Science: A Walkthrough: Latest Trends in AI, vol. 885. 2020. doi: 10.1007/978-3-030-38445-6.
[8] R. R. Al Hakim, E. Rusdi, and M. A. Setiawan, “Android Based Expert System Application for Diagnose COVID-19 Disease : Cases Study of Banyumas Regency,” J. Intell. Comput. Heal. Informatics, vol. 1, no. 2, pp. 1–13, 2020, doi: 10.26714/jichi.v1i2.5958.
[9] R. R. Al Hakim et al., “Aplikasi Algoritma Dijkstra dalam Penyelesaian Berbagai Masalah,” Expert J. Manaj. Sist. Inf. dan Teknol., vol. 11, no. 1, pp. 42–47, 2021, doi: 10.36448/expert.v11i1.1939.
[10] E. J. Ha, “Applications of machine learning and deep learning to thyroid imaging: Where do we stand?,” Ultrasonography, vol. 40, no. 1, pp. 23–29, 2021, doi: 10.14366/usg.20068.
[11] B. Wildman-Tobriner, E. Taghi-Zadeh, and M. A. Mazurowski, “Artificial Intelligence (AI) Tools for Thyroid Nodules on Ultrasound, From the AJR Special Series on AI Applications,” Am. J. Roentgenol., 2022, doi: 10.2214/AJR.22.27430.
[12] Y. J. Chai, J. Song, M. Shaear, and K. H. Yi, “Artificial intelligence for thyroid nodule ultrasound image analysis,” Ann. Thyroid, vol. 5, p. 8, 2020, doi: 10.21037/aot.2020.04.01.
[13] B. Kezlarian and O. Lin, “Artificial Intelligence in Thyroid Fine Needle Aspiration Biopsies,” Acta Cytol., vol. 65, no. 4, pp. 324–329, 2020, doi: 10.1159/000512097.
[14] K. Z. Swan, J. Thomas, V. E. Nielsen, M. L. Jespersen, and S. J. Bonnema, “External validation of AIBx, an artificial intelligence model for risk stratification, in thyroid nodules,” Eur. Thyroid J., vol. 11, no. 2, 2022, doi: 10.1530/etj-21-0129.
[15] N. Hong, H. Park, and Y. Rhee, “Machine Learning Applications in Endocrinology and Metabolism Research: An Overview,” Endocrinol. Metab. (Seoul, Korea), vol. 35, no. 1, pp. 71–84, 2020, doi: 10.3803/EnM.2020.35.1.71.
[16] S. Zhou and W. Chen, “Prof. Young Jun Chai: artificial intelligence for thyroid ultrasound image analysis,” Ann. Thyroid, vol. 4, p. 11, 2019, doi: 10.21037/aot.2019.07.05.
[17] M. Tarabichi, “Thyroid cancer under the scope of emerging technologies,” Mol. Cell. Endocrinol., vol. 541, 2022, doi: 10.1016/j.mce.2021.111491.
[18] Y. Zhang, Q. Wu, Y. Chen, and Y. Wang, “A Clinical Assessment of an Ultrasound Computer-Aided Diagnosis System in Differentiating Thyroid Nodules With Radiologists of Different Diagnostic Experience.,” Front. Oncol., vol. 10, p. 557169, 2020, doi: 10.3389/fonc.2020.557169.
[19] Y.-T. Shen, L. Chen, W.-W. Yue, and H.-X. Xu, “Artificial intelligence in ultrasound,” Eur. J. Radiol., vol. 139, p. 109717, 2021, doi: 10.1016/j.ejrad.2021.109717.
[20] M. Barczyński, M. Stopa-Barczyńska, B. Wojtczak, A. Czarniecka, and A. Konturek, “Clinical validation of S-DetectTM mode in semi-automated ultrasound classification of thyroid lesions in surgical office,” Gland Surg., vol. 9, 2020, doi: 10.21037/gs.2019.12.23.
[21] Y. J. Yoo, E. J. Ha, Y. J. Cho, H. L. Kim, M. Han, and S. Y. Kang, “Computer-Aided Diagnosis of Thyroid Nodules via Ultrasonography: Initial Clinical Experience,” Korean J. Radiol., vol. 19, no. 4, pp. 665–672, 2018, doi: 10.3348/kjr.2018.19.4.665.
[22] A. Oral and A. Güvenış, “A digital platform for simulating the accurate detectability of overactive parathyroid glands in SPECT/CT imaging,” in 2019 Medical Technologies Congress (TIPTEKNO), Izmir (TR), 2019. doi: 10.1109/TIPTEKNO.2019.8895109.
[23] T. Turki, “An empirical study of machine learning algorithms for cancer identification,” ICNSC 2018 - 15th IEEE Int. Conf. Networking, Sens. Control, pp. 1–5, 2018, doi: 10.1109/ICNSC.2018.8361312.
[24] F. Q. Guo, “Application of artificial intelligence automatic detection system in preoperative ultrasonic diagnosis of thyroid nodules,” Acad. J. Second Mil. Med. Univ., vol. 40, no. 11, pp. 1183–1189, 2019, doi: 10.16781/j.0258-879x.2019.11.1183.
[25] D. Ivanova, “Artificial Intelligence in Internet of Medical Imaging Things: The Power of Thyroid Cancer Detection,” 2018 Int. Conf. Inf. Technol., 2018, doi: 10.1109/infotech.2018.8510725.
[26] M. Santin et al., “Detecting abnormal thyroid cartilages on CT using deep learning,” Diagn. Interv. Imaging, vol. 100, no. 4, pp. 251–257, 2019, doi: 10.1016/j.diii.2019.01.008.
[27] E. Zgheib et al., “Identification of non-validated endocrine disrupting chemical characterization methods by screening of the literature using artificial intelligence and by database exploration,” Environ. Int., vol. 154, p. 106574, 2021, doi: 10.1016/j.envint.2021.106574.
[28] H. A. Nugroho, “Impact of Implementing Data Balancing Method in Intelligent Thyroid Cancer Detection,” 2021 Int. Conf. Comput. Syst. Inf. Technol. Electr. Eng. COSITE 2021, pp. 102–106, 2021, doi: 10.1109/COSITE52651.2021.9649624.
[29] X. A. Kesarkar and K. V Kulhalli, “Thyroid Nodule Detection using Artificial Neural Network,” 2021 Int. Conf. Artif. Intell. Smart Syst., 2021, doi: 10.1109/icais50930.2021.9396035.
[30] Y. Wang, “A comparison between ACR TI-RADS and artificial intelligence TI-RADS regarding to diagnostic efficacy and ability to reduce unnecessary fine-needle aspiration cytology,” Chinese J. Ultrason., vol. 30, no. 5, pp. 408–413, 2021, doi: 10.3760/cma.j.cn131148-20201231-00989.
[31] S. Xia et al., “A computer-aided diagnosing system in the evaluation of thyroid nodules-experience in a specialized thyroid center.,” World J. Surg. Oncol., vol. 17, no. 1, p. 210, 2019, doi: 10.1186/s12957-019-1752-z.
[32] M. Hasanzad, “Artificial intelligence perspective in the future of endocrine diseases,” J. Diabetes Metab. Disord., 2022, doi: 10.1007/s40200-021-00949-2.
[33] C. Lin, “Artificial Intelligence-Assisted Electrocardiography for Early Diagnosis of Thyrotoxic Periodic Paralysis,” J. Endocr. Soc., vol. 5, no. 9, 2021, doi: 10.1210/jendso/bvab120.
[34] L. Wang et al., “Automatic thyroid nodule recognition and diagnosis in ultrasound imaging with the YOLOv2 neural network,” World J. Surg. Oncol., vol. 17, no. 1, p. 12, 2019, doi: 10.1186/s12957-019-1558-z.
[35] A. Sharifi, “Comparison of the particle swarm optimization with the genetic algorithms as a training for multilayer perceptron technique to diagnose thyroid functional disease,” Shiraz E Med. J., vol. 22, no. 1, pp. 1–7, 2021, doi: 10.5812/SEMJ.100351.
[36] L. Xu et al., “Computer-Aided Diagnosis Systems in Diagnosing Malignant Thyroid Nodules on Ultrasonography: A Systematic Review and Meta-Analysis.,” Eur. Thyroid J., vol. 9, no. 4, pp. 186–193, 2020, doi: 10.1159/000504390.
[37] E. A. Druzhinina, “Decision support system with the use of ‘histological analysis of thyroid tumors’ knowledge base,” in 2019 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus), 2019, pp. 1276–1278. doi: 10.1109/EIConRus.2019.8657004.
[38] G.-G. Wu et al., “Deep Learning Based on ACR TI-RADS Can Improve the Differential Diagnosis of Thyroid Nodules,” Front. Oncol., vol. 11, p. 575166, 2021, doi: 10.3389/fonc.2021.575166.
[39] S. Peng, “Deep learning-based artificial intelligence model to assist thyroid nodule diagnosis and management: a multicentre diagnostic study,” Lancet Digit. Heal., vol. 3, no. 4, 2021, doi: 10.1016/S2589-7500(21)00041-8.
[40] S. O. Olatunji et al., “Early diagnosis of thyroid cancer diseases using computational intelligence techniques: A case study of a Saudi Arabian dataset,” Comput. Biol. Med., vol. 131, p. 104267, 2021, doi: 10.1016/j.compbiomed.2021.104267.
[41] N. M. Thomasian, I. R. Kamel, and H. X. Bai, “Machine intelligence in non-invasive endocrine cancer diagnostics,” Nat. Rev. Endocrinol., vol. 18, no. 2, pp. 81–95, 2022, doi: 10.1038/s41574-021-00543-9.
[42] X. Liang, Y. Huang, Y. Cai, J. Liao, and Z. Chen, “A Computer-Aided Diagnosis System and Thyroid Imaging Reporting and Data System for Dual Validation of Ultrasound-Guided Fine-Needle Aspiration of Indeterminate Thyroid Nodules,” Front. Oncol., vol. 11, 2021, doi: 10.3389/fonc.2021.611436.
[43] B. Zhang et al., “Machine Learning–Assisted System for Thyroid Nodule Diagnosis,” Thyroid, vol. 29, no. 6, pp. 858–867, 2019, doi: 10.1089/thy.2018.0380.
[44] Y. Li, P. Chen, Z. Li, H. Su, L. Yang, and D. Zhong, “Rule-based automatic diagnosis of thyroid nodules from intraoperative frozen sections using deep learning,” Artif. Intell. Med., vol. 108, p. 101918, 2020, doi: 10.1016/j.artmed.2020.101918.
[45] Y. Cao, “Sparse Representation-Based Radiomics in the Diagnosis of Thyroid Nodules,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 11632, pp. 391–401, 2019, doi: 10.1007/978-3-030-24274-9_35.
[46] W. Mai et al., “The value of the Demetics ultrasound-assisted diagnosis system in the differential diagnosis of benign from malignant thyroid nodules and analysis of the influencing factors,” Eur. Radiol., vol. 31, no. 10, pp. 7936–7944, 2021, doi: 10.1007/s00330-021-07884-z.
[47] R. B. Namdeo and G. V. Janardan, “Thyroid Disorder Diagnosis by Optimal Convolutional Neuron based CNN Architecture,” J. Exp. & Theor. Artif. Intell., pp. 1–20, 2021, doi: 10.1080/0952813x.2021.1938694.
[48] D. T. Nguyen, J. K. Kang, T. D. Pham, G. Batchuluun, and K. R. Park, “Ultrasound image-based diagnosis of malignant thyroid nodule using artificial intelligence,” Sensors (Switzerland), vol. 20, no. 7, Apr. 2020, doi: 10.3390/s20071822.
[49] F. Abdolali, “A systematic review on the role of artificial intelligence in sonographic diagnosis of thyroid cancer: Past, present and future,” Front. Biomed. Technol., vol. 7, no. 4, pp. 266–280, 2020, doi: 10.18502/fbt.v7i4.5324.
[50] Y. Chen et al., “An Artificial Intelligence Model Based on ACR TI-RADS Characteristics for US Diagnosis of Thyroid Nodules.,” Radiology, p. 211455, 2022, doi: 10.1148/radiol.211455.
[51] J. Zhu et al., “An efficient deep convolutional neural network model for visual localization and automatic diagnosis of thyroid nodules on ultrasound images.,” Quant. Imaging Med. Surg., vol. 11, no. 4, pp. 1368–1380, 2021, doi: 10.21037/qims-20-538.
[52] Q. Pan, “An intelligent decision-making method for graded diagnosis and treatment of thyroid disease based on deep learning,” ACM Int. Conf. Proceeding Ser., pp. 6–11, 2018, doi: 10.1145/3231884.3231893.
[53] V. K. Bhatt and V. K. Pal, “An Intelligent System for Diagnosing Thyroid Disease in Pregnant Ladies through Artificial Neural Network,” SSRN Electron. J., 2019, doi: 10.2139/ssrn.3382654.
[54] J. H. Lee, E. J. Ha, and J. H. Kim, “Application of deep learning to the diagnosis of cervical lymph node metastasis from thyroid cancer with CT,” Eur. Radiol., vol. 29, no. 10, pp. 5452–5457, 2019, doi: 10.1007/s00330-019-06098-8.
[55] Y. J. Choi et al., “A Computer-Aided Diagnosis System Using Artificial Intelligence for the Diagnosis and Characterization of Thyroid Nodules on Ultrasound: Initial Clinical Assessment,” Thyroid, vol. 27, no. 4, pp. 546–552, Apr. 2017, doi: 10.1089/THY.2016.0372.
[56] J. Thomas and T. Haertling, “AIBx, Artificial Intelligence Model to Risk Stratify Thyroid Nodules,” Thyroid, vol. 30, no. 6, pp. 878–884, 2020, doi: 10.1089/thy.2019.0752.
[57] X. Duan, “An Ensemble Deep Learning Architecture for Multilabel Classification on TI-RADS,” Proc. - 2020 IEEE Int. Conf. Bioinforma. Biomed. BIBM 2020, pp. 576–582, 2020, doi: 10.1109/BIBM49941.2020.9313134.
[58] F. Bini et al., “Artificial Intelligence in Thyroid Field—A Comprehensive Review,” Cancers (Basel)., vol. 13, no. 19, p. 4740, 2021, doi: 10.3390/cancers13194740.
[59] D. T. Nguyen, T. D. Pham, G. Batchuluun, H. S. Yoon, and K. R. Park, “Artificial Intelligence-Based Thyroid Nodule Classification Using Information from Spatial and Frequency Domains,” J. Clin. Med., vol. 8, no. 11, 2019, doi: 10.3390/jcm8111976.
[60] P. Yang et al., “Automatic differentiation of thyroid scintigram by deep convolutional neural network: a dual center study,” BMC Med. Imaging, vol. 21, no. 1, p. 179, 2021, doi: 10.1186/s12880-021-00710-4.
[61] K. Lång, S. Hofvind, A. Rodríguez-Ruiz, and I. Andersson, “Can artificial intelligence reduce the interval cancer rate in mammography screening?,” Eur. Radiol., vol. 31, no. 8, pp. 5940–5947, 2021, doi: 10.1007/s00330-021-07686-3.
[62] D. Avola, “Multimodal Feature Fusion and Knowledge-Driven Learning via Experts Consult for Thyroid Nodule Classification,” IEEE Trans. Circuits Syst. Video Technol., 2021, doi: 10.1109/TCSVT.2021.3074414.
[63] S. K. Abbas, “Thyroid tissue segmentation and classification in ultrasound image of Artificial intelligence,” Mater. Today Proc., 2021, doi: 10.1016/j.matpr.2021.04.359.
[64] V. V. Vadhiraj, A. Simpkin, J. O’Connell, N. S. Ospina, S. Maraka, and D. T. O’Keeffe, “Ultrasound Image Classification of Thyroid Nodules Using Machine Learning Techniques,” Medicina (Kaunas)., vol. 57, no. 6, 2021, doi: 10.3390/medicina57060527.
[65] C.-K. Zhao et al., “A Comparative Analysis of Two Machine Learning-Based Diagnostic Patterns with Thyroid Imaging Reporting and Data System for Thyroid Nodules: Diagnostic Performance and Unnecessary Biopsy Rate,” Thyroid, vol. 31, no. 3, pp. 470–481, 2021, doi: 10.1089/thy.2020.0305.
[66] D. Zhang et al., “A Review of the Role of the S-Detect Computer-Aided Diagnostic Ultrasound System in the Evaluation of Benign and Malignant Breast and Thyroid Masses.,” Med. Sci. Monit., vol. 27, p. e931957, 2021, doi: 10.12659/MSM.931957.
[67] L. V van Dijk, “Improving automatic delineation for head and neck organs at risk by Deep Learning Contouring,” Radiother. Oncol., vol. 142, pp. 115–123, 2020, doi: 10.1016/j.radonc.2019.09.022.
[68] S. Wang 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,” Front. Oncol., vol. 10, p. 591846, 2020, doi: 10.3389/fonc.2020.591846.
[69] C. Fragopoulos et al., “Radial basis function artificial neural network for the investigation of thyroid cytological lesions,” J. Thyroid Res., vol. 2020, 2020, doi: 10.1155/2020/5464787.
[70] H. L. Kim, E. J. Ha, and M. Han, “Real-World Performance of Computer-Aided Diagnosis System for Thyroid Nodules Using Ultrasonography,” Ultrasound Med. Biol., vol. 45, no. 10, pp. 2672–2678, 2019, doi: 10.1016/j.ultrasmedbio.2019.05.032.
[71] J. L. Reverter et al., “Reliability of a computer-aided system in the evaluation of indeterminate ultrasound images of thyroid nodules,” Eur. Thyroid J., vol. 11, no. 1, 2022, doi: 10.1530/etj-21-0023.
[72] N. Chambara, “The diagnostic efficiency of ultrasound computer–aided diagnosis in differentiating thyroid nodules: A systematic review and narrative synthesis,” Cancers (Basel)., vol. 11, no. 11, 2019, doi: 10.3390/cancers11111759.
[73] M. T. Stib, “Thyroid Nodule Malignancy Risk Stratification Using a Convolutional Neural Network,” Ultrasound Q., vol. 36, no. 2, pp. 164–172, 2020, doi: 10.1097/RUQ.0000000000000501.
[74] B. Wildman-Tobriner et al., “Using Artificial Intelligence to Revise ACR TI-RADS Risk Stratification of Thyroid Nodules: Diagnostic Accuracy and Utility,” Radiology, vol. 292, no. 1, pp. 112–119, 2019, doi: 10.1148/radiol.2019182128.
[75] J. Wang et al., “An integrated AI model to improve diagnostic accuracy of ultrasound and output known risk features in suspicious thyroid nodules,” Eur. Radiol., vol. 32, no. 3, pp. 2120–2129, 2022, doi: 10.1007/s00330-021-08298-7.
[76] L. Watkins, G. O’Neill, D. Young, and C. McArthur, “Comparison of British Thyroid Association, American College of Radiology TIRADS and Artificial Intelligence TIRADS with histological correlation: diagnostic performance for predicting thyroid malignancy and unnecessary fine needle aspiration rate,” Br. J. Radiol., vol. 94, no. 1123, p. 20201444, 2021, doi: 10.1259/bjr.20201444.
[77] E. Y. Jeong, H. L. Kim, E. J. Ha, S. Y. Park, Y. J. Cho, and M. Han, “Computer-aided diagnosis system for thyroid nodules on ultrasonography: diagnostic performance and reproducibility based on the experience level of operators,” Eur. Radiol., vol. 29, no. 4, pp. 1978–1985, 2019, doi: 10.1007/s00330-018-5772-9.
[78] T. Li, “Computer-aided diagnosis system of thyroid nodules ultrasonography: Diagnostic performance difference between computer-aided diagnosis and 111 radiologists,” Med. (United States), vol. 99, no. 23, 2020, doi: 10.1097/MD.0000000000020634.
[79] M. Han, E. J. Ha, and J. H. Park, “Computer-Aided Diagnostic System for Thyroid Nodules on Ultrasonography: Diagnostic Performance Based on the Thyroid Imaging Reporting and Data System Classification and Dichotomous Outcomes,” Am. J. Neuroradiol., vol. 42, no. 3, pp. 559–565, 2021, doi: 10.3174/ajnr.A6922.
[80] L. Gao et al., “Computer-aided system for diagnosing thyroid nodules on ultrasound: A comparison with radiologist-based clinical assessments,” Head Neck, vol. 40, no. 4, pp. 778–783, 2018, doi: 10.1002/hed.25049.
[81] W.-J. Zhao, L.-R. Fu, Z.-M. Huang, J.-Q. Zhu, and B.-Y. Ma, “Effectiveness evaluation of computer-aided diagnosis system for the diagnosis of thyroid nodules on ultrasound: A systematic review and meta-analysis,” Medicine (Baltimore)., vol. 98, no. 32, 2019, doi: 10.1097/MD.0000000000016379.
[82] F. Q. Guo, “Efficacy of preoperative ultrasound evaluation of thyroid nodules by artificial intelligence automatic detection system version 2.0: A preliminary study,” Acad. J. Second Mil. Med. Univ., vol. 41, no. 10, pp. 1077–1083, 2020, Available at : pesquisa.bvsalud.org.
[83] A. A. Jamshidi, “An algorithmic treatment strategy for the inhibition of type-II deiodinase enzyme on thyroid secretion hormones,” Biomed. Signal Process. Control, vol. 66, 2021, doi: 10.1016/j.bspc.2021.102473.
[84] C. M. Tam, “Automated delineation of organs-at-risk in head and neck CT images using multi-output support vector regression,” Proc. SPIE 10578, Med. Imaging 2018 Biomed. Appl. Mol. Struct. Funct. Imaging, vol. 10578, no. 12 March 2018, 2018, doi: 10.1117/12.2292556.
[85] M. Buda et al., “Management of Thyroid Nodules Seen on US Images: Deep Learning May Match Performance of Radiologists,” Radiology, vol. 292, no. 3, pp. 695–701, 2019, doi: 10.1148/radiol.2019181343.
[86] S. Verma, R. Popli, and H. Kumar, “A Machine Learning Approach to Thyroid Carcinoma Prediction,” 2021 Int. Conf. Artif. Intell. Mach. Vis., 2021, doi: 10.1109/aimv53313.2021.9671012.
[87] N. T. Duc, “An ensemble deep learning for automatic prediction of papillary thyroid carcinoma using fine needle aspiration cytology,” Expert Syst. Appl., vol. 188, 2022, doi: 10.1016/j.eswa.2021.115927.
[88] J. Yoon et al., “Artificial intelligence to predict the BRAFV600E mutation in patients with thyroid cancer,” PLoS One, vol. 15, no. 11, 2020, doi: 10.1371/journal.pone.0242806.
[89] E. Xia et al., “Preoperative prediction of lymph node metastasis in patients with papillary thyroid carcinoma by an artificial intelligence algorithm,” Am. J. Transl. Res., vol. 13, no. 7, pp. 7695–7704, 2021. Available at : ncbi.nlm.nih.go.
[90] S. Zhou, “Dr. Ryan K. Orosco: robotic surgery will make surgery safer and help to enable challenging procedures,” Ann. Thyroid, vol. 4, p. 16, 2019, doi: 10.21037/aot.2019.06.03.
[91] A. Shaikh, O. Lakhani, T. Lathia, and B. Saptarshi, “‘Telethyroidology’: Managing thyroid disorders through telemedicine,” Thyroid Res. Pract., vol. 17, no. 2, p. 56, 2020, doi: 10.4103/trp.trp_20_20.
[92] R. R. Al Hakim, “Pencegahan Penularan Covid-19 Berbasis Aplikasi Android Sebagai Implementasi Kegiatan KKN Tematik Covid-19 di Sokanegara Purwokerto Banyumas,” Community Engagem. Emerg. J., vol. 2, no. 1, pp. 7–13, Aug. 2021, doi: 10.37385/ceej.v2i1.125.
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