Dynamic analysis of Hashimoto’s Thyroiditis bio-mathematical model using artificial neural network

IF 5.4 3区材料科学 Q2 CHEMISTRY, PHYSICAL

ACS Applied Energy Materials Pub Date : 2024-10-05 DOI:10.1016/j.matcom.2024.10.001

Rakesh Kumar , Sudarshan Dhua

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引用次数: 0

Abstract

This article establishes an efficient solution scheme for a mathematical model of Hashimoto’s Thyroiditis (HT) employing artificial neural networks. HT is an auto-immune disorder hostile to the thyroid follicle cells, effectuating hypothyroid or hyperthyroidism. Under this condition, the thyroid-stimulating hormone (TSH) alters incomparably to the free thyroxine (FT4) interrupts the functioning of the hypothalamus-pituitary-thyroid (HPT) axis, implicating the thyroid follicle cells getting destroyed. We primarily focus on utilizing artificial neural network (ANN) to perform numerical simulations for the system of ordinary differential equations describing the dynamics of an existing 4D model of HT. The presented model comprises four time-dependent variables: TSH, FT4, anti-thyroid antibodies (Ab), and size of the thyroid gland (T). We utilize ND-Solver and ANN scheme in the Mathematica software to acquire the computational data and illustrate thus retrieved results with essential performance plots. Further, mean square error has been considered in validating the proposed ANN-based approach accurately. The plot for training and validation loss exhibits the effectiveness of the proposed methodology, and substantiate that the suggested ANN approach is a good fit for the solving the mathematical model of HT.

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利用人工神经网络对桥本氏甲状腺炎生物数学模型进行动态分析

本文利用人工神经网络建立了桥本氏甲状腺炎（HT）数学模型的高效求解方案。桥本氏甲状腺炎是一种敌视甲状腺滤泡细胞的自身免疫性疾病，可导致甲状腺功能减退或亢进。在这种情况下，促甲状腺激素（TSH）与游离甲状腺素（FT4）会发生巨大变化，从而干扰下丘脑-垂体-甲状腺轴（HPT）的功能，导致甲状腺滤泡细胞遭到破坏。我们主要侧重于利用人工神经网络（ANN）对描述现有 HT 四维模型动态的常微分方程系统进行数值模拟。该模型由四个随时间变化的变量组成：TSH、FT4、抗甲状腺抗体（Ab）和甲状腺大小（T）。我们利用 Mathematica 软件中的 ND-Solver 和 ANN 方案获取计算数据，并用基本性能图说明检索结果。此外，我们还考虑了均方误差，以准确验证所提出的基于 ANN 的方法。训练和验证损失图显示了所建议方法的有效性，并证明所建议的方差网络方法非常适合 HT 数学模型的求解。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

ACS Applied Energy Materials Materials Science-Materials Chemistry

CiteScore

10.30

自引率

6.20%

发文量

1368

期刊介绍： ACS Applied Energy Materials is an interdisciplinary journal publishing original research covering all aspects of materials, engineering, chemistry, physics and biology relevant to energy conversion and storage. The journal is devoted to reports of new and original experimental and theoretical research of an applied nature that integrate knowledge in the areas of materials, engineering, physics, bioscience, and chemistry into important energy applications.