深度学习技术用于成像诊断细胞癌:当前和新兴趋势
Zijie Wang1, Xiaofei Zhang2, Xinning Wang3
1Department of Vascular Intervention, ShengLi Oilfield Center Hospital, Dongying, China.
Frontiers in oncology
|September 20, 2023
概括
深度学习在使用医学成像诊断细胞癌 (RCC) 方面显示出显著的前景. 本综述涵盖了AI在RCC检测中的当前进展和未来方向,使患者受益.
科学领域:
- 人工智能的人工智能
- 医学成像分析 医学成像分析
- 在瘤学瘤学.
背景情况:
- 医学成像对于细胞癌 (RCC) 检测,监测和治疗评估至关重要.
- 像CET,超声波和MRI这样的成像技术的数字化使人工智能应用成为可能.
- 深度学习正在快速推进医学成像,改变传统的诊断范式.
研究的目的:
- 审查细胞癌 (RCC) 诊断深度学习的最新成果,挑战和未来研究.
- 突出深度学习技术在改善RCC诊断方面的潜力.
- 在RCC评估中提供深度学习应用的全面概述.
主要方法:
- 关于深度学习应用在细胞癌 (RCC) 诊断中的当前文献的综述.
- 分析该领域的成就,挑战和未来方向.
- 专注于与医学成像模式 (CECT,MRI,超声波) 结合的深度学习.
主要成果:
- 深度学习正在成为提高RCC诊断的准确性和速度的强大工具.
- 人工智能驱动的成像工具有助于临床医生更精确地评估瘤.
- 该研究确定了用于RCC的AI未来研究的关键进展和领域.
结论:
- 深度学习技术对细胞癌 (RCC) 诊断的未来有很大的前景.
- 持续的研究和开发对于实现AI在RCC患者护理中的全部潜力至关重要.
- 合作努力可以加快深度学习在临床实践中的整合,以改善患者的治疗结果.
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