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

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.