Ozturk Ahmet Cankat, Haznedar Hilal, Haznedar Bulent, Ilgan Seyfettin, Erogul Osman, Kalinli Adem
Institute of Natural Science, Department of Biomedical Engineering, TOBB University of Economics and Technology, 06560 Ankara, Türkiye.
Institute of Natural Science, Department of Computer Engineering, Erciyes University, 38280 Kayseri, Türkiye.
Diagnostics (Basel). 2023 Feb 15;13(4):740. doi: 10.3390/diagnostics13040740.
The thyroid nodule risk stratification guidelines used in the literature are based on certain well-known sonographic features of nodules and are still subjective since the application of these characteristics strictly depends on the reading physician. These guidelines classify nodules according to the sub-features of limited sonographic signs. This study aims to overcome these limitations by examining the relationships of a wide range of ultrasound (US) signs in the differential diagnosis of nodules by using artificial intelligence methods. An innovative method based on training Adaptive-Network Based Fuzzy Inference Systems (ANFIS) by using Genetic Algorithm (GA) is used to differentiate malignant from benign thyroid nodules. The comparison of the results from the proposed method to the results from the commonly used derivative-based algorithms and Deep Neural Network (DNN) methods yielded that the proposed method is more successful in differentiating malignant from benign thyroid nodules. Furthermore, a novel computer aided diagnosis (CAD) based risk stratification system for the thyroid nodule's US classification that is not present in the literature is proposed.
文献中使用的甲状腺结节风险分层指南基于结节某些众所周知的超声特征，且仍具有主观性，因为这些特征的应用严格依赖于阅片医生。这些指南根据有限超声征象的子特征对结节进行分类。本研究旨在通过使用人工智能方法检查广泛的超声（US）征象在结节鉴别诊断中的关系来克服这些局限性。一种基于使用遗传算法（GA）训练自适应网络模糊推理系统（ANFIS）的创新方法用于区分甲状腺恶性结节和良性结节。将所提方法的结果与常用的基于导数的算法和深度神经网络（DNN）方法的结果进行比较，结果表明所提方法在区分甲状腺恶性结节和良性结节方面更成功。此外，还提出了一种文献中不存在的基于新型计算机辅助诊断（CAD）的甲状腺结节US分类风险分层系统。
