人工智能卷积神经网络软件的性能,用于检测狗和猫的确诊心力衰竭
Lauren Ellingsberg1, Mauricio Solano1, Jacqueline Milton Hicks2
1Department of Clinical Sciences, Cummings School of Veterinary Medicine at Tufts University, North Grafton, Massachusetts, USA.
概括
一个人工智能 (AI) 程序准确地识别了狗和猫心力衰竭 (HF) 的个人放射性迹象,类似于放射科医生. 然而,其对HF的整体评估需要兽医解释,特别是在猫身上.
科学领域:
- 兽医放射学 兽医放射学
- 人工智能在医学中的应用
- 心脏病学 心脏病学
背景情况:
- 放射性图像对于诊断动物心力衰竭至关重要.
- 人工智能 (AI) 在医学图像分析方面表现有前途.
- 评估AI在兽医心脏病学中的诊断能力是必不可少的.
研究的目的:
- 评估AI程序在狗和猫中检测HF的放射性迹象的能力.
- 根据放射性发现,评估AI对高频的综合评估.
- 为了比较人工智能的表现与人类放射科医生.
主要方法:
- 一个独立数据集的回顾性分析.
- 测试人工智能用于检测放射性标志:间歇性模式,心壮病,血管模式.
- 评估AI的整体HF结论对狗和猫的敏感性和特异性.
主要成果:
- 人工智能对个别的HF症状表现出高度灵敏度 (例如,对狗的心脏巨变症100%).
- 尽管特异性高 (95%),但AI的整体HF诊断灵敏度很低 (9.8%).
- 人工智能在识别个体标志方面的表现与放射科医生的准确度相匹配或超过.
结论:
- 人工智能擅长识别特定的放射性异常,这些异常表明HF.
- 人工智能的整体高频评估需要仔细的兽医解释,特别是在猫身上.
- 兽医应该使用AI报告作为补充诊断工具.
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