肺部地面玻璃结节的成像诊断:与当前状态和未来方向的叙事综述
Wenting Luo1, Yifei Ren1, Yinuo Liu1
1The Second Clinical Medical College, Nanchang University, Nanchang, China.
Quantitative imaging in medicine and surgery
|August 15, 2024
概括
人工智能 (AI) 通过分析成像数据,帮助诊断肺癌的地面玻璃结节 (GGNs). 将AI与成像功能相结合,可以改善GGN的早期检测和临床决策.
科学领域:
- 放射学和医学成像学 医学成像学
- 在瘤学瘤学.
- 人工智能在医学中的应用
背景情况:
- 全球肺癌发病率和死亡率正在增加.
- 由于先进的成像和查,地面玻璃结节 (GGNs) 的检测正在增加.
- 人工智能 (AI) 为GGN诊断和管理提供了新的方法.
研究的目的:
- 使用成像数据检查恶性肺病变的早期检测.
- 通过区分良性和恶性GGN来改善临床决策.
- 描述计算机断层扫描 (CT) 引导的活检和GGN分析中的AI进步.
主要方法:
- 使用PubMed,Elsevier ScienceDirect,Springer数据库和谷歌学者进行文学搜索.
- 来自南大学成像中心的成像资源的分析.
- 使用Adobe Illustrator 2020处理图形.
主要成果:
- 研究了常见的GGN症状及其与病变类型的关系.
- 在GGN图像细分,分类和侵入性预测中的AI应用得到了详细说明.
- 讨论了活检对于持久的纯GGNs的必要性.
结论:
- 结合成像特征可以增强GGN诊断.
- 用CT指导的活检需要仔细考虑.
- 人工智能工具为GGN图像分析和准确诊断提供了有希望的改进.
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