随着诊断技术的不断进步，甲状腺结节在人群中检出率日益增加。甲状腺结节的诊断主要依赖甲状腺影像报告与数据系统(TIRAID)及甲状腺结节细针抽吸活检(FNAB)。高分辨率超声是诊断甲状腺结节最重要的影像学检查手段，具有方便、灵活、无创、无放射性等优点，而超声引导下甲状腺结节FNAB是术前诊断甲状腺结节最准确、有效的方法。人工智能(AI)是计算机科学的一个分支，旨在使机器模拟或执行与人类智能相似的行为和认知功能。人工智能逐渐应用于医学领域，并在甲状腺结节诊断中备受关注，显著提高了甲状腺结节诊断的效率和准确性。本文将从甲状腺结节超声图像、细胞病理学等方面来介绍人工智能在甲状腺结节诊断中的研究进展及应用价值，旨在通过总结人工智能在甲状腺结节诊断领域的最新研究进展，辅助医师深入了解该领域的发展现状，为甲状腺结节的临床诊疗决策提供更准确，更全面的参考信息。 With the continuous advancement of diagnostic technologies, the detection rate of thyroid nodules in the population is steadily increasing. Diagnosis primarily relies on the Thyroid Imaging Reporting and Data System (TIRADS) and fine needle aspiration biopsy (FNAB) of thyroid nodules. High-resolution ultrasound is the most important imaging modality for diagnosing thyroid nodules, offering advantages such as convenience, flexibility, non-invasiveness, and no radiation. Ultrasound-guided fine needle aspiration biopsy is the most accurate and effective method for preoperative diagnosis of thyroid nodules. Artificial intelligence (AI), a branch of computer science, aims to enable machines to simulate or perform cognitive functions similar to human intelligence. AI is gradually being applied in the medical field and has gained significant attention in the diagnosis of thyroid nodules, significantly improving the efficiency and accuracy of diagnosis. This article will explore the research progress and application value of AI in the diagnosis of thyroid nodules from aspects such as thyroid nodule ultrasound imaging and cytopathology. The goal is to summarize the latest advancements in AI for the diagnosis of thyroid nodules, assist clinicians in understanding the current state of the field, and provide more accurate and comprehensive reference information for clinical decision-making in the diagnosis and treatment of thyroid nodules.
Abstract
With the continuous advancement of diagnostic technologies, the detection rate of thyroid nodules in the population is steadily increasing. Diagnosis primarily relies on the Thyroid Imaging Reporting and Data System (TIRADS) and fine needle aspiration biopsy (FNAB) of thyroid nodules. High-resolution ultrasound is the most important imaging modality for diagnosing thyroid nodules, offering advantages such as convenience, flexibility, non-invasiveness, and no radiation. Ultrasound-guided fine needle aspiration biopsy is the most accurate and effective method for preoperative diagnosis of thyroid nodules. Artificial intelligence (AI), a branch of computer science, aims to enable machines to simulate or perform cognitive functions similar to human intelligence. AI is gradually being applied in the medical field and has gained significant attention in the diagnosis of thyroid nodules, significantly improving the efficiency and accuracy of diagnosis. This article will explore the research progress and application value of AI in the diagnosis of thyroid nodules from aspects such as thyroid nodule ultrasound imaging and cytopathology. The goal is to summarize the latest advancements in AI for the diagnosis of thyroid nodules, assist clinicians in understanding the current state of the field, and provide more accurate and comprehensive reference information for clinical decision-making in the diagnosis and treatment of thyroid nodules.
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