Abstract：
OBJECTIVE: The thyroid gland, a key component of the endocrine system, is pivotal in regulating bodily functions. Thermography, a non-invasive imaging technique utilizing infrared cameras, has emerged as a diagnostic tool for thyroid-related conditions, offering advantages such as early detection and risk stratification. Artificial intelligence (AI) has demonstrated success in medical diagnostics, and its integration into thermal imaging analysis holds promise for improving diagnostic capabilities. This study aims to explore the potential of AI, specifically convolutional neural networks (CNNs), in enhancing the analysis of thyroid thermograms for the detection of nodules and abnormalities.
METHODS: Artificial intelligence (AI) and machine learning techniques are integrated to enhance thyroid thermal image analysis. Specifically, a fusion of U-Net and VGG16, combined with feature engineering (FE), is proposed for accurate thyroid nodule segmentation. The novelty of this research lies in leveraging feature engineering in transfer learning for the segmentation of thyroid nodules, even in the presence of a limited dataset.
RESULTS: The study presents results from four conducted studies, demonstrating the efficacy of this approach even with a limited dataset. It\'s observed that in study 4, using FE has led to a significant improvement in the value of the dice coefficient. Even for the small size of the masked region, incorporating radiomics with FE resulted in significant improvements in the segmentation dice coefficient. It\'s promising that one can achieve higher dice coefficients by employing different models and refining them.
CONCLUSIONS: The findings here underscore the potential of AI for precise and efficient segmentation of thyroid nodules, paving the way for improved thyroid health assessment.
METHODS: Artificial intelligence (AI) and machine learning techniques are integrated to enhance thyroid thermal image analysis. Specifically, a fusion of U-Net and VGG16, combined with feature engineering (FE), is proposed for accurate thyroid nodule segmentation. The novelty of this research lies in leveraging feature engineering in transfer learning for the segmentation of thyroid nodules, even in the presence of a limited dataset.
RESULTS: The study presents results from four conducted studies, demonstrating the efficacy of this approach even with a limited dataset. It\'s observed that in study 4, using FE has led to a significant improvement in the value of the dice coefficient. Even for the small size of the masked region, incorporating radiomics with FE resulted in significant improvements in the segmentation dice coefficient. It\'s promising that one can achieve higher dice coefficients by employing different models and refining them.
CONCLUSIONS: The findings here underscore the potential of AI for precise and efficient segmentation of thyroid nodules, paving the way for improved thyroid health assessment.
摘要：
目标：甲状腺，内分泌系统的关键组成部分，是调节身体机能的关键。热成像,一种利用红外摄像机的非侵入性成像技术，已经成为甲状腺相关疾病的诊断工具，提供早期发现和风险分层等优势。人工智能（AI）在医疗诊断方面取得了成功，及其与热成像分析的集成有望提高诊断能力。这项研究旨在探索人工智能的潜力，特别是卷积神经网络(CNN)，增强甲状腺热谱图的分析，以检测结节和异常。
方法：集成了人工智能（AI）和机器学习技术，以增强甲状腺热图像分析。具体来说,U-Net和VGG16的融合，结合特征工程(FE)，提出了精确的甲状腺结节分割方法。这项研究的新颖之处在于利用迁移学习中的特征工程来分割甲状腺结节，即使存在有限的数据集。
结果：该研究展示了四项研究的结果，即使使用有限的数据集，也证明了这种方法的有效性。观察到，在研究4中，使用FE已导致骰子系数的值的显着改善。即使对于小尺寸的掩蔽区域，将影像组学与FE相结合可以显着改善分割骰子系数。通过采用不同的模型并对其进行改进，可以实现更高的骰子系数。
结论：这里的发现强调了AI对甲状腺结节精确有效分割的潜力，为改善甲状腺健康评估铺平道路。
方法：集成了人工智能（AI）和机器学习技术，以增强甲状腺热图像分析。具体来说,U-Net和VGG16的融合，结合特征工程(FE)，提出了精确的甲状腺结节分割方法。这项研究的新颖之处在于利用迁移学习中的特征工程来分割甲状腺结节，即使存在有限的数据集。
结果：该研究展示了四项研究的结果，即使使用有限的数据集，也证明了这种方法的有效性。观察到，在研究4中，使用FE已导致骰子系数的值的显着改善。即使对于小尺寸的掩蔽区域，将影像组学与FE相结合可以显着改善分割骰子系数。通过采用不同的模型并对其进行改进，可以实现更高的骰子系数。
结论：这里的发现强调了AI对甲状腺结节精确有效分割的潜力，为改善甲状腺健康评估铺平道路。
