在甲状腺成像中进行深度学习的叙述性回顾:目前的进展和未来的前景
Wan-Ting Yang1, Bu-Yun Ma1, Yang Chen1
1Department of Medical Ultrasound, West China Hospital, Sichuan University, Chengdu, China.
Quantitative imaging in medicine and surgery
|February 28, 2024
概括
深度学习 (DL) 增强了甲状腺图像分析,以更好地诊断和治疗. 本综述探讨了甲状腺疾病中DL的应用,指导了未来的内分泌学精密医学.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 内分泌学 在内分泌学.
背景情况:
- 深度学习 (DL) 显著推进图像分析,推动诊断和治疗中的精准医学.
- 甲状腺成像为开发DL模型提供了广泛的数据,以解决全球甲状腺疾病患病率的问题.
研究的目的:
- 审查甲状腺医学图像分析中DL网络的一般规则和未来方向.
- 评价2018年至2023年间发表的原创文章.
主要方法:
- 在PubMed,科学网络和谷歌学者中进行系统的文献搜索.
- 关键词包括AI/DL,甲状腺疾病和甲状腺结节/癌症.
- 从2018年4月到2023年9月的英语文章的分析.
主要成果:
- 甲状腺成像中的DL计算机视觉任务包括分类,细分和检测.
- 目前DL的应用包括管理甲状腺结节/癌症,评估转移风险和区分功能性疾病.
结论:
- DL有望改善甲状腺图像质量和评估精度.
- 预计DL将提高诊断准确度,并为甲状腺疾病的临床决策提供信息.
相关概念视频
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