脊椎心脏分数的诊断验证 机器学习算法用于狗侧侧胸部X射线图
J Solomon1, S Bender1, P Durgempudi1
1IDEXX Laboratories, Inc., Westbrook, ME, USA.
The Journal of small animal practice
|August 25, 2023
概括
脊椎心脏评分 (VHS) 的新算法在评估狗心脏大小方面表现与兽医心脏病学家相提并论. 这种工具有助于识别和分期心脏病,尽管建议进一步验证.
科学领域:
- 兽医心脏病学 兽医心脏病学
- 医学成像分析分析 医学成像分析
- 人工智能在诊断中的应用
背景情况:
- 脊椎心脏评分 (VHS) 是评估狗心脏健康的关键指标.
- 准确的VHS评估对于诊断和分期心脏病以及提供预后信息至关重要.
- 手动VHS评分可以是主观的和耗时的.
研究的目的:
- 验证一种用于计算脊椎心脏得分 (VHS) 的新型算法,并将其与经过董事会认证的兽医心脏病学家的手动得分进行对比.
- 评估算法的准确性和可靠性,以预测心脏大小与胸椎相比.
主要方法:
- 一个卷积神经网络 (CNN) 被开发用于解剖特征的语义细分,以预测心脏大小和脊椎体.
- 用CNN生成的预测来计算VHS.
- 使用了一组外部验证,包括1200张狗侧面放射图,其中400张图像由三个心脏病学家使用布坎南方法手动评分.
主要成果:
- 算法的VHS预测显示,与心脏病学家得分相比,95百分比绝对差异为1.05个脊椎.
- 在算法和心脏病学家得分之间观察到0.09个脊椎的平均偏差.
- 发现该算法的性能在整个预测范围都得到了良好的校准.
结论:
- 开发的脊椎心脏评分算法的性能与经过董事会认证的兽医心脏病学家相当.
- 该算法显示出作为对犬心脏大小评估的客观工具的巨大潜力.
- 在广泛采用之前,建议在不同的临床环境中进行进一步的外部验证.
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