深度学习用于图像分析,用于诊断和管理食道癌症
Charalampos Theocharopoulos1, Spyridon Davakis2, Dimitrios C Ziogas3
1Department of Surgery, Metaxa Cancer Hospital, 18537 Piraeus, Greece.
Cancers
|October 16, 2024
概括
使用深度学习 (DL) 的人工智能增强了食道癌的检测. DL算法显示出高精度,有助于早期诊断和改善从查到治疗评估的患者管理.
科学领域:
- 胃肠病学 胃肠病学
- 在瘤学瘤学.
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 食道癌的预后不好,需要全面的多学科护理.
- 目前的诊断依赖于高清内镜和组织病理学.
- 巴雷特食道 (BE) 的过度诊断导致不必要的监测.
研究的目的:
- 介绍关于深度学习 (DL) 应用在食道瘤管理中的文献.
- 介绍目前已发表的DL研究,用于该领域的图像分析.
- 为临床医生指导DL原则,评估和图像识别的局限性.
主要方法:
- 对食道瘤中DL应用的已发表研究的综述.
- 与内镜师相比,DL算法性能的分析.
- 探索DL在诊断,细分,预测和治疗评估中的作用.
主要成果:
- 许多研究报告了用于食道损伤检测的高度准确的DL算法.
- 在许多研究中,DL的性能与内镜医生相当或优于内镜医生.
- 目前DL正在扩展到组织学诊断,瘤体积细分和治疗反应评估.
结论:
- 深度学习 (DL) 显示出作为内镜师在治疗食道瘤的辅助工具的显著前景.
- DL可以提高诊断准确度,减少不必要的监测,并有助于治疗规划和评估.
- 了解DL的原则和局限性对于临床采用和批判性评估至关重要.
相关概念视频
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During an EGD, the endoscope can be used to:
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