Iterative intelligent algorithm-based on CT three-dimensional reconstruction in endoscopic thyroidectomy and perioperative comprehensive nursing

Abstract

This research was aimed to explore the application of computed tomography (CT) 3D reconstruction technology based on iterative intelligent algorithm in endoscopic thyroidectomy (ET) and perioperative comprehensive nursing. A total of 140 patients with thyroid mass receiving ET were randomly divided into controls (conventional ultrasound combined with perioperative routine nursing) and experimental group (CT 3D reconstruction based on iterative intelligent algorithm combined with perioperative comprehensive nursing), 70 cases in each group. The iterative intelligent algorithm used in this article is an image processing algorithm based on deep learning. Its goal is to generate more accurate and clear 3D reconstructed images by learning and optimizing the original CT images for multiple iterations. The detailed process of the algorithm is as follows: Data preprocessing, Feature extraction and representation, Iterative optimization, 3D reconstruction image generation. Finally, after multiple iterations of optimization, the algorithm will generate high-quality 3D reconstruction images, which can clearly show the anatomical structure of the thyroid mass and the surrounding tissue of the lesion. In order to evaluate the effectiveness of the algorithm, this article recorded the maximum vertical diameter, maximum left and right diameter, and maximum anteroposterior diameter of thyroid masses measured by the two imaging methods and solid specimens. In addition, the postoperative pain of patients was evaluated by visual analogue scale (VAS), and the incidence of complications and nursing satisfaction were counted.