Artificial intelligence in thyroid ultrasound
Artificial intelligence (AI), particularly deep learning (DL) algorithms, has demonstrated remarkable progress in image-recognition tasks, enabling the automatic quantitative assessment of complex medical images with increased accuracy and efficiency. AI is widely used and is becoming increasingly p...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-05-01
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| Series: | Frontiers in Oncology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2023.1060702/full |
| _version_ | 1797828984681005056 |
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| author | Chun-Li Cao Chun-Li Cao Qiao-Li Li Qiao-Li Li Jin Tong Li-Nan Shi Li-Nan Shi Wen-Xiao Li Wen-Xiao Li Ya Xu Jing Cheng Ting-Ting Du Jun Li Jun Li Xin-Wu Cui |
| author_facet | Chun-Li Cao Chun-Li Cao Qiao-Li Li Qiao-Li Li Jin Tong Li-Nan Shi Li-Nan Shi Wen-Xiao Li Wen-Xiao Li Ya Xu Jing Cheng Ting-Ting Du Jun Li Jun Li Xin-Wu Cui |
| author_sort | Chun-Li Cao |
| collection | DOAJ |
| description | Artificial intelligence (AI), particularly deep learning (DL) algorithms, has demonstrated remarkable progress in image-recognition tasks, enabling the automatic quantitative assessment of complex medical images with increased accuracy and efficiency. AI is widely used and is becoming increasingly popular in the field of ultrasound. The rising incidence of thyroid cancer and the workload of physicians have driven the need to utilize AI to efficiently process thyroid ultrasound images. Therefore, leveraging AI in thyroid cancer ultrasound screening and diagnosis cannot only help radiologists achieve more accurate and efficient imaging diagnosis but also reduce their workload. In this paper, we aim to present a comprehensive overview of the technical knowledge of AI with a focus on traditional machine learning (ML) algorithms and DL algorithms. We will also discuss their clinical applications in the ultrasound imaging of thyroid diseases, particularly in differentiating between benign and malignant nodules and predicting cervical lymph node metastasis in thyroid cancer. Finally, we will conclude that AI technology holds great promise for improving the accuracy of thyroid disease ultrasound diagnosis and discuss the potential prospects of AI in this field. |
| first_indexed | 2024-04-09T13:13:08Z |
| format | Article |
| id | doaj.art-2346cd967aa14d67a22e36ce3e45de7e |
| institution | Directory Open Access Journal |
| issn | 2234-943X |
| language | English |
| last_indexed | 2024-04-09T13:13:08Z |
| publishDate | 2023-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj.art-2346cd967aa14d67a22e36ce3e45de7e2023-05-12T05:34:28ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2023-05-011310.3389/fonc.2023.10607021060702Artificial intelligence in thyroid ultrasoundChun-Li Cao0Chun-Li Cao1Qiao-Li Li2Qiao-Li Li3Jin Tong4Li-Nan Shi5Li-Nan Shi6Wen-Xiao Li7Wen-Xiao Li8Ya Xu9Jing Cheng10Ting-Ting Du11Jun Li12Jun Li13Xin-Wu Cui14Department of Ultrasound, The First Affiliated Hospital of Shihezi University, Shihezi, ChinaNHC Key Laboratory of Prevention and Treatment of Central Asia High Incidence Diseases, First Affiliated Hospital, School of Medicine, Shihezi University, Shihezi, ChinaDepartment of Ultrasound, The First Affiliated Hospital of Shihezi University, Shihezi, ChinaNHC Key Laboratory of Prevention and Treatment of Central Asia High Incidence Diseases, First Affiliated Hospital, School of Medicine, Shihezi University, Shihezi, ChinaDepartment of Ultrasound, The First Affiliated Hospital of Shihezi University, Shihezi, ChinaDepartment of Ultrasound, The First Affiliated Hospital of Shihezi University, Shihezi, ChinaNHC Key Laboratory of Prevention and Treatment of Central Asia High Incidence Diseases, First Affiliated Hospital, School of Medicine, Shihezi University, Shihezi, ChinaDepartment of Ultrasound, The First Affiliated Hospital of Shihezi University, Shihezi, ChinaNHC Key Laboratory of Prevention and Treatment of Central Asia High Incidence Diseases, First Affiliated Hospital, School of Medicine, Shihezi University, Shihezi, ChinaDepartment of Ultrasound, The First Affiliated Hospital of Shihezi University, Shihezi, ChinaDepartment of Ultrasound, The First Affiliated Hospital of Shihezi University, Shihezi, ChinaDepartment of Ultrasound, The First Affiliated Hospital of Shihezi University, Shihezi, ChinaDepartment of Ultrasound, The First Affiliated Hospital of Shihezi University, Shihezi, ChinaNHC Key Laboratory of Prevention and Treatment of Central Asia High Incidence Diseases, First Affiliated Hospital, School of Medicine, Shihezi University, Shihezi, ChinaDepartment of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, ChinaArtificial intelligence (AI), particularly deep learning (DL) algorithms, has demonstrated remarkable progress in image-recognition tasks, enabling the automatic quantitative assessment of complex medical images with increased accuracy and efficiency. AI is widely used and is becoming increasingly popular in the field of ultrasound. The rising incidence of thyroid cancer and the workload of physicians have driven the need to utilize AI to efficiently process thyroid ultrasound images. Therefore, leveraging AI in thyroid cancer ultrasound screening and diagnosis cannot only help radiologists achieve more accurate and efficient imaging diagnosis but also reduce their workload. In this paper, we aim to present a comprehensive overview of the technical knowledge of AI with a focus on traditional machine learning (ML) algorithms and DL algorithms. We will also discuss their clinical applications in the ultrasound imaging of thyroid diseases, particularly in differentiating between benign and malignant nodules and predicting cervical lymph node metastasis in thyroid cancer. Finally, we will conclude that AI technology holds great promise for improving the accuracy of thyroid disease ultrasound diagnosis and discuss the potential prospects of AI in this field.https://www.frontiersin.org/articles/10.3389/fonc.2023.1060702/fullartificial intelligencethyroidultrasoundmachine learningdeep learning |
| spellingShingle | Chun-Li Cao Chun-Li Cao Qiao-Li Li Qiao-Li Li Jin Tong Li-Nan Shi Li-Nan Shi Wen-Xiao Li Wen-Xiao Li Ya Xu Jing Cheng Ting-Ting Du Jun Li Jun Li Xin-Wu Cui Artificial intelligence in thyroid ultrasound Frontiers in Oncology artificial intelligence thyroid ultrasound machine learning deep learning |
| title | Artificial intelligence in thyroid ultrasound |
| title_full | Artificial intelligence in thyroid ultrasound |
| title_fullStr | Artificial intelligence in thyroid ultrasound |
| title_full_unstemmed | Artificial intelligence in thyroid ultrasound |
| title_short | Artificial intelligence in thyroid ultrasound |
| title_sort | artificial intelligence in thyroid ultrasound |
| topic | artificial intelligence thyroid ultrasound machine learning deep learning |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2023.1060702/full |
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