Ya Yang, Pan Wang, Chengzhou Yu, Jing Zhu, Jinping Sheng
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引用次数: 0
Abstract
The application of artificial intelligence (AI) technology has realized the transformation of people's production and lifestyle, and also promoted the rapid development of the medical field. At present, the application of intelligence in the medical field is increasing. Using its advanced methods and technologies of AI, this paper aims to realize the integration of medical imaging-aided diagnosis system and AI, which is helpful to analyze and solve the loopholes and errors of traditional artificial diagnosis in the diagnosis of pulmonary nodules. Drawing on the principles and rules of image segmentation methods, the construction and optimization of a medical image-aided diagnosis system is carried out to realize the precision of the diagnosis system in the diagnosis of pulmonary nodules. In the diagnosis of pulmonary nodules carried out by traditional artificial and medical imaging-assisted diagnosis systems, 231 nodules with pathology or no change in follow-up for more than two years were also tested in 200 cases. The results showed that the AI software detected a total of 881 true nodules with a sensitivity of 99.10% (881/889). The radiologists detected 385 true nodules with a sensitivity of 43.31% (385/889). The sensitivity of AI software in detecting non-calcified nodules was significantly higher than that of radiologists (99.01% vs 43.30%, P < 0.001), and the difference was statistically significant.
人工智能(AI)技术的应用实现了人们生产和生活方式的转变,也推动了医疗领域的快速发展。目前,智能化在医疗领域的应用越来越多。本文旨在利用其先进的人工智能方法和技术,实现医学影像辅助诊断系统与人工智能的融合,有助于分析和解决传统人工诊断在肺结节诊断中的漏洞和错误。借鉴图像分割方法的原理和规则,对医学图像辅助诊断系统进行构建和优化,实现诊断系统在肺结节诊断中的精准性。在传统的人工和医学影像辅助诊断系统对肺结节的诊断中,我们也对200例随访2年以上病理或无变化的结节231例进行了检测。结果显示,人工智能软件共检测出881个真结节,灵敏度为99.10%(881/889)。放射科医师共检出385个真结节,敏感性为43.31%(385/889)。人工智能软件对非钙化结节的检测灵敏度明显高于放射科医师(99.01% vs 43.30%, P
期刊介绍:
BMC Medical Informatics and Decision Making is an open access journal publishing original peer-reviewed research articles in relation to the design, development, implementation, use, and evaluation of health information technologies and decision-making for human health.