Research progress on artificial intelligence technology-assisted diagnosis of thyroid diseases DOI Creative Commons
Lina Yang, Xinyuan Wang,

Shixia Zhang

et al.

Frontiers in Oncology, Journal Year: 2025, Volume and Issue: 15

Published: Feb. 20, 2025

With the rapid development of "Internet + Medical" model, artificial intelligence technology has been widely used in analysis medical images. Among them, using deep learning algorithms to identify features ultrasound and pathological images realize intelligent diagnosis diseases entered clinical verification stage. This study is based on application research reviews early screening thyroid diseases. The cure rate disease high stage, but once it deteriorates into cancer, risk death treatment costs patient increase. At present, still depends examination equipment experience doctors, there a certain misdiagnosis rate. Based above background, particularly important explore that can achieve objective lesions stages. paper provides comprehensive review recent technology. It integrates findings multiple studies traditional machine are as objects. convolutional neural network model recognition accuracy for nodules cell lesions. U-Net significantly improve nodule when segmentation algorithm. article focuses reviewing sections, hoping provide researchers with ideas help clinicians cancer.

Language: Английский

Research progress on artificial intelligence technology-assisted diagnosis of thyroid diseases DOI Creative Commons
Lina Yang, Xinyuan Wang,

Shixia Zhang

et al.

Frontiers in Oncology, Journal Year: 2025, Volume and Issue: 15

Published: Feb. 20, 2025

With the rapid development of "Internet + Medical" model, artificial intelligence technology has been widely used in analysis medical images. Among them, using deep learning algorithms to identify features ultrasound and pathological images realize intelligent diagnosis diseases entered clinical verification stage. This study is based on application research reviews early screening thyroid diseases. The cure rate disease high stage, but once it deteriorates into cancer, risk death treatment costs patient increase. At present, still depends examination equipment experience doctors, there a certain misdiagnosis rate. Based above background, particularly important explore that can achieve objective lesions stages. paper provides comprehensive review recent technology. It integrates findings multiple studies traditional machine are as objects. convolutional neural network model recognition accuracy for nodules cell lesions. U-Net significantly improve nodule when segmentation algorithm. article focuses reviewing sections, hoping provide researchers with ideas help clinicians cancer.

Language: Английский

Citations

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