Investigation of neural network models application in EU-TIRADS thyroid nodules classification for personalization of thyroid gland ultrasound diagnostic

K. V. Tsyguleva, I. A. Lozhkin, D. V. Korolev, K. S. Zajcev, M. E. Dunaev, A. A. Garmash, A. V. Manaev, S. M. Zaharova, A. A. Trukhin, E. A. Troshina
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Abstract

SUBSTANTIATION. It is known that about 69% of all thyroid nodules undergoing surgical treatment are benign formations, and up to 75% of patients with an intermediate cytological conclusion undergo unnecessary surgical intervention. This suggests that improving the quality of differential diagnosis of nodular formations will avoid excessive economic costs for the healthcare system. In this regard, AI technologies in diagnostic algorithms for the classification of thyroid nodules were involved. AIM. Improving the efficiency of automatic classification of thyroid nodules on ultrasound images by using a set of neural network models. MATERIALS AND METHODS. We used ultrasound images of thyroid nodules available in open sources and obtained with the help of 3 ultrasound devices of Endocrinology Research Centre as part of Project № 22-15-00135 of the grant of the Russian Science Foundation. This article check the hypothesis that the size of the training set cannot be increased by repeating similar images from the ultrasound cine loop of one patient, but only by expanding the dataset with new unique specimens of other patients and/or data from the augmentation process. RESULTS. As a result, a neural network model EfficientNet-B6 was proposed to solve the problem of EU-TIRADS classification of thyroid nodules based on ultrasound images of the thyroid gland. CONCLUSION. The results obtained allow us to advance in the use of artificial intelligence methods for personalized medicine in thyroid diseases.
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神经网络模型在EU-TIRADS甲状腺结节分类中应用于甲状腺超声诊断个体化的研究
实体化。众所周知,接受手术治疗的甲状腺结节中约有69%为良性形成,高达75%的细胞学结论为中等的患者接受了不必要的手术干预。这表明,提高结节形成的鉴别诊断质量将避免医疗保健系统的过度经济成本。在这方面,涉及人工智能技术在甲状腺结节分类诊断算法。的目标。利用一组神经网络模型提高超声图像上甲状腺结节自动分类的效率。材料和方法。我们使用了公开来源的甲状腺结节的超声图像,并在内分泌研究中心的3台超声设备的帮助下获得,这是俄罗斯科学基金会资助的项目№22-15-00135的一部分。本文验证了一个假设,即训练集的大小不能通过重复来自一个患者的超声电影循环的类似图像来增加,而只能通过使用其他患者的新的独特样本和/或来自增强过程的数据来扩展数据集。结果。为此,提出神经网络模型EfficientNet-B6,解决基于甲状腺超声图像的EU-TIRADS对甲状腺结节的分类问题。结论。获得的结果使我们能够在使用人工智能方法进行甲状腺疾病的个性化医疗方面取得进展。
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Associations of thyroid status and thyroperoxidase antibodies with serum trace elements Features of achieving compensation of hypothyroisis in pregnant women Structural and morphologic characteristics of nodular goiter in chronic iodine deficiency status Investigation of neural network models application in EU-TIRADS thyroid nodules classification for personalization of thyroid gland ultrasound diagnostic Press release from the Endocrine Society ENDO 2022 Annual Conference
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