基于人工智能的声评估工具在复发性呼吸道乳头瘤模型上的标准化
Mikolaj Buchwald1, Piotr Nogal2, Jan Nowak1
1Network Services Department, Poznan Supercomputing and Networking Center, Polish Academy of Sciences, Poznan, Poland.
概括
人工智能 (AI) 精确地测量了复发性呼吸道乳头瘤 (RRP) 中的乳头瘤生长. 一个人工智能系统,GC-AID,显示出在声评估中量化RRP程度的潜力.
科学领域:
- 耳鼻喉科 耳鼻喉科 耳鼻喉科
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 复发性呼吸道乳头瘤 (RRP) 影响声,需要对乳头瘤生长进行准确的评估.
- 对RRP延长的定量评估对于治疗监测和管理至关重要.
研究的目的:
- 为了评估基于AI的注释系统的有效性,Glottis覆盖 - 人工智能和深度学习 (GC-AID).
- 在白光 (WL) 和窄带成像 (NBI) 模式下评估受影响的粘膜.
- 为未来AI在RRP评估中的应用建立一个案例研究.
主要方法:
- 使用基于AI的注释系统 (GC-AID) 进行定量评估.
- 评估了4名RRP患者.
- 使用白光 (WL) 和窄带成像 (NBI) 方式评估受影响的粘膜.
主要成果:
- 健康的喉表现出最小的声面积差异 (2.6%).
- 第4号患者的RRP覆盖率从治疗后的WL从69.5%降至42.6%.
- 第1号患者表现出类似的改善,而第2号和第3号患者没有显著的益处.
结论:
- GC-AID精确地测量了治疗前和治疗后的RRP程度.
- 通过取和注释,获得了客观的定量结果.
- 由人工智能驱动的系统为精确的RRP范围测量提供了一种可行的方法.
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