Advances in Applied Nano-Bio Technologies

Published date: Jun 30 2025

Journal Title: Advances in Applied Nano-Bio Technologies

Issue title: Advances in Applied Nano-Bio Technologies: Volume 6 Issue 2

Pages: 66 - 78

DOI: 10.18502/aanbt.v6i2.18262

Melika GudarziDepartment of Biomedical Engineering, Medical Branch, Islamic Azad University, Tehran

Mohadeseh Parhizkarist_m_parhizkari@azad.ac.irDepartment of Biomedical Engineering, South Tehran Branch, Islamic Azad University, Tehran

Fatemeh AbbasiChemistry Department, College of Sciences, Shiraz University, Shiraz 71946-84795

Thyroid diseases, encompassing both benign nodules and differentiated thyroid cancers, are highly prevalent worldwide and necessitate accurate diagnostic imaging for effective management and treatment. The traditional interpretation of medical images, however, remains subjective and is heavily reliant on the expertise of clinicians, which can lead to variability in diagnostic outcomes and treatment decisions. Recent advancements in deep learning (DL) techniques have shown considerable promise in addressing these limitations. This review aims to summarize the application of DL in thyroid disease diagnosis across multiple imaging modalities, including single-photon emission computed tomography (SPECT), ultrasound, and computed tomography (CT). We focus on five recent studies that utilized state-of-the-art DL architectures, such as residual networks (ResNet), Xception-based multi-channel models, and ensemble learning approaches, which have demonstrated remarkable efficacy in image classification and disease characterization. These DL models were evaluated based on diagnostic accuracy, clinical integration, interpretability, and real-world performance, with a direct comparison to radiologists and fine-needle aspiration (FNA). Across all imaging modalities, DL models consistently demonstrated robust performance, frequently matching or exceeding the diagnostic accuracy of experienced clinicians. For instance, diagnostic accuracies up to 98.9% were achieved in ultrasound and CT-based multi-channel models, with areas under the receiver operating characteristic curve (AUC) surpassing 0.93 in several studies. Additionally, prospective validation and the incorporation of clinical risk factors, such as patient demographics and prior medical history, further enhanced the reliability and clinical relevance of these models. Deep learning has demonstrated significant promise in enhancing thyroid imaging diagnostics by providing quicker, more standardized, and possibly noninvasive substitutes for conventional diagnostic techniques. Despite challenges related to generalizability and model interpretability, the integration of AI into thyroid imaging holds significant promise for improving diagnostic precision and enhancing the efficiency of endocrine care. As these technologies continue to evolve, they may lead to a paradigm shift in the way thyroid disorders are diagnosed and managed, moving toward a more accurate, reproducible, and patient-centered approach.

Keywords: artificial intelligence, deep learning, thyroid cancer, SPECT imaging, ultrasound, convolutional neural networks

[1] Senashova O, Samuels M. Diagnosis and management of nodular thyroid disease. Tech Vasc Interv Rad. 2022;25:100816.

[2] Pinto-Coelho L. How artificial intelligence is shaping medical imaging technology: a survey of innovations and applications. Bioeng. 2023;10:1435.

[3] Aversano L, Bernardi ML, Cimitile M, Maiellaro A, Pecori R. A systematic review on artificial intelligence techniques for detecting thyroid diseases. PeerJ Comput Sci. 2023;9:e1394.

[4] Habchi Y, Himeur Y, Kheddar H, Boukabou A, Atalla S, Chouchane A, et al. Ai in thyroid cancer diagnosis: Techniques, trends, and future directions. Systems. 2023;11:519.

[5] Peng S, Liu Y, Lv W, Liu L, Zhou Q, Yang H, et al. Deep learning-based artificial intelligence model to assist thyroid nodule diagnosis and management: a multicentre diagnostic study. Lancet Digit Health. 2021;3:e250-e259.

[6] Yang L, Wang X, Zhang S, Cao K, Yang J. Research progress on artificial intelligence technologyassisted diagnosis of thyroid diseases. Front Oncol. 2025;15:1536039.

[7] Anari S, Tataei Sarshar N, Mahjoori N, Dorosti S, Rezaie A. Review of deep learning approaches for thyroid cancer diagnosis. Math Probl Eng. 2022;2022:5052435.

[8] Kim G, Lee E, Kim H, Yoon J, Park V, Kwak J. Convolutional neural network to stratify the malignancy risk of thyroid nodules: Diagnostic performance compared with the American College of Radiology thyroid imaging reporting and data system implemented by experienced radiologists. Am J Neuroradiol. 2021;42:1513-1519.

[9] Wu G-G, Lv W-Z, Yin R, Xu J-W, Yan Y-J, Chen R-X, et al. Deep learning based on ACR TI-RADS can improve the differential diagnosis of thyroid nodules. Front Oncol. 2021;11:575166.

[10] Jin Z, Zhu Y, Zhang S, Xie F, Zhang M, Zhang Y, et al. Ultrasound computer-aided diagnosis (CAD) based on the thyroid imaging reporting and data system (TI-RADS) to distinguish benign from malignant thyroid nodules and the diagnostic performance of radiologists with different diagnostic experience. Med Sci Monit: Int J Exp Clin Res. 2020;26:e918452-918451.

[11] Liang X, Yu J, Liao J, Chen Z. Convolutional neural network for breast and thyroid nodules diagnosis in ultrasound imaging. Biomed Res Int. 2020;2020:1763803.

[12] Buda M, Wildman-Tobriner B, Hoang JK, Thayer D, Tessler FN, Middleton WD, et al. Management of thyroid nodules seen on US images: deep learning may match performance of radiologists. Radiology. 2019;292:695-701.

[13] Ko SY, Lee JH, Yoon JH, Na H, Hong E, Han K, et al. Deep convolutional neural network for the diagnosis of thyroid nodules on ultrasound. Head Neck. 2019;41:885-891.

[14] Park VY, Han K, Seong YK, Park MH, Kim E-K, Moon HJ, et al. Diagnosis of thyroid nodules: performance of a deep learning convolutional neural network model vs. radiologists. Sci Rep. 2019;9:17843.

[15] Wang L, Yang S, Yang S, Zhao C, Tian G, Gao Y, et al. Automatic thyroid nodule recognition and diagnosis in ultrasound imaging with the YOLOv2 neural network. World J Surg Oncol. 2019;17:1-9.

[16] Li X, Zhang S, Zhang Q, Wei X, Pan Y, Zhao J, et al. Diagnosis of thyroid cancer using deep convolutional neural network models applied to sonographic images: a retrospective, multicohort, diagnostic study. Lancet Oncol. 2019;20:193-201.

[17] Chi J, Walia E, Babyn P, Wang J, Groot G, Eramian M. Thyroid nodule classification in ultrasound images by fine-tuning deep convolutional neural network. J Digit Imaging. 2017;30:477-486.

[18] Ma J, Wu F, Zhu J, Xu D, Kong D. A pre-trained convolutional neural network based method for thyroid nodule diagnosis. Ultrason. 2017;73:221-230.

[19] Zhao H, Zheng C, Zhang H, Rao M, Li Y, Fang D, et al. Diagnosis of thyroid disease using deep convolutional neural network models applied to thyroid scintigraphy images: a multicenter study. Front Endocrinol (Lausanne). 2023;14:1224191.

[20] Zheng G, Zhang H, Lin F, Zafereo M, Gross N, Sun P, et al. Performance of CT-based deep learning in diagnostic assessment of suspicious lateral lymph nodes in papillary thyroid cancer: a prospective diagnostic study. Int J Surg. 2023;109:3337-3345.

[21] Zhang X, Lee VC, Rong J, Liu F, Kong H. Multi-channel convolutional neural network architectures for thyroid cancer detection. PLoS One. 2022;17:e0262128.

[22] Wang S, Zhang Y. Grad-CAM: understanding AI models. Comput Mater Contin. 2023;76:1321-1324.

[23] Wasilewski T, Kamysz W, Gębicki J. AI-assisted detection of biomarkers by sensors and biosensors for early diagnosis and monitoring. Biosens. 2024;14:356.

[24] Shao C, Li Z, Zhang C, Zhang W, He R, Xu J, et al. Optical diagnostic imaging and therapy for thyroid cancer. Materials Today Bio. 2022;17:100441.

[25] Bhattacharya S, Mahato RK, Singh S, Bhatti GK, Mastana SS, Bhatti JS. Advances and challenges in thyroid cancer: The interplay of genetic modulators, targeted therapies, and AI-driven approaches. Life Sci. 2023;332:122110.

[26] Bini F, Pica A, Azzimonti L, Giusti A, Ruinelli L, Marinozzi F, et al. Artificial intelligence in thyroid field—a comprehensive review. Cancers (Basel). 2021;13:4740.

[27] Sharifi Y, Bakhshali MA, Dehghani T, DanaiAshgzari M, Sargolzaei M, Eslami S. Deep learning on ultrasound images of thyroid nodules. Biocybernetics Biomed Eng. 2021;41:636-655.

[28] Zampella E, Klain M, Pace L, Cuocolo A. PET/CT in the management of differentiated thyroid cancer. Diagn Interv Imaging. 2021;102:515-523.