{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T16:01:19Z","timestamp":1790352079212,"version":"4.1.0"},"reference-count":82,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,2,20]],"date-time":"2025-02-20T00:00:00Z","timestamp":1740009600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Oncol."],"abstract":"<jats:p>With the rapid development of the \u201cInternet + Medical\u201d model, artificial intelligence technology has been widely used in the analysis of medical images. Among them, the technology of using deep learning algorithms to identify features of ultrasound and pathological images and realize intelligent diagnosis of diseases has entered the clinical verification stage. This study is based on the application research of artificial intelligence technology in medical diagnosis and reviews the early screening and diagnosis of thyroid diseases. The cure rate of thyroid disease is high in the early stage, but once it deteriorates into thyroid cancer, the risk of death and treatment costs of the patient increase. At present, the early diagnosis of the disease still depends on the examination equipment and the clinical experience of doctors, and there is a certain misdiagnosis rate. Based on the above background, it is particularly important to explore a technology that can achieve objective screening of thyroid lesions in the early stages. This paper provides a comprehensive review of recent research on the early diagnosis of thyroid diseases using artificial intelligence technology. It integrates the findings of multiple studies and that traditional machine learning algorithms are widely used as research objects. The convolutional neural network model has a high recognition accuracy for thyroid nodules and thyroid pathological cell lesions. U-Net network model can significantly improve the recognition accuracy of thyroid nodule ultrasound images when used as a segmentation algorithm. This article focuses on reviewing the intelligent recognition technology of thyroid ultrasound images and pathological sections, hoping to provide researchers with research ideas and help clinicians achieve intelligent early screening of thyroid cancer.<\/jats:p>","DOI":"10.3389\/fonc.2025.1536039","type":"journal-article","created":{"date-parts":[[2025,2,20]],"date-time":"2025-02-20T05:11:20Z","timestamp":1740028280000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":18,"title":["Research progress on artificial intelligence technology-assisted diagnosis of thyroid diseases"],"prefix":"10.3389","volume":"15","author":[{"given":"Lina","family":"Yang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"XinYuan","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shixia","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kun","family":"Cao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianjun","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,2,20]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"36","DOI":"10.3389\/fendo.2021.691736","article-title":"Actions of thyroid hormones on thyroid cancers","volume":"12","author":"Mousa","year":"2021","journal-title":"Front Endocrinol"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.29309\/TPMJ\/2015.22.10.982","article-title":"THYROID DISORDERS","author":"Ali","year":"2015","journal-title":"Prof Med J"},{"key":"B3","doi-asserted-by":"publisher","DOI":"10.3760\/cma.j.cn115330-20191226-00783","article-title":"Research progress in the etiology of thyroid cancer","volume":"55","author":"Qin","year":"2020","journal-title":"Chin J Otorhinolaryngol Head Neck Surg"},{"key":"B4","article-title":"Emotional disorder and related psychosocial factors in tumor patients","author":"Liu","year":"2017","journal-title":"Chin J Rehabil Theory Pract"},{"key":"B5","doi-asserted-by":"publisher","DOI":"10.3760\/CMA.J.ISSN.1673-4351.2015.10.008","article-title":"Investigation of psychological health status in Malignant tumor patients","author":"Wu","year":"2015","journal-title":"Int J Nurs"},{"key":"B6","doi-asserted-by":"publisher","DOI":"10.1021\/acs.est.9b02810","article-title":"Associations between exposure to persistent organic pollutants and thyroid function in a case-control study of east China","volume":"53","author":"Han","year":"2019","journal-title":"Environ Sci Technol"},{"key":"B7","article-title":"The role of environmental factors in autoimmune thyroiditis","volume":"31","author":"Hybenova","year":"2010","journal-title":"Neuro Endocrinol Lett"},{"key":"B8","doi-asserted-by":"publisher","DOI":"10.1097\/EE9.0000000000000025","article-title":"Short-term exposure to air pollution might exacerbate autoimmune diseases","volume":"2","author":"Faustini","year":"2018","journal-title":"Environ Epidemiol"},{"key":"B9","doi-asserted-by":"publisher","DOI":"10.1002\/cam4.v11.12","article-title":"Prediction of lung metastases in thyroid cancer using machine learning based on SEER database","volume":"11","author":"Liu","year":"2022","journal-title":"Cancer Med"},{"key":"B10","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-021-08146-8","article-title":"Radiomic machine learning for predicting prognostic biomarkers and molecular subtypes of breast cancer using tumor heterogeneity and angiogenesis properties on MRI","volume":"32","author":"Lee","year":"2022","journal-title":"Eur Radiol"},{"key":"B11","doi-asserted-by":"publisher","first-page":"204","DOI":"10.1097\/MD.0000000000039602","article-title":"Risk of Malignancy in thyroid nodules with increased 11C-Choline uptake detected incidentally on PET\/CT. 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