IDEXX Laboratories, Inc., Westbrook, ME, USA.
J Small Anim Pract. 2023 Dec;64(12):769-775. doi: 10.1111/jsap.13666. Epub 2023 Aug 25.
The vertebral heart score is a measurement used to index heart size relative to thoracic vertebra. Vertebral heart score can be a useful tool for identifying and staging heart disease and providing prognostic information. The purpose of this study is to validate the use of a vertebral heart score algorithm compared to manual vertebral heart scoring by three board-certified veterinary cardiologists.
A convolutional neural network centred around semantic segmentation of relevant anatomical features was developed to predict heart size and vertebral bodies. These predictions were used to calculate the vertebral heart score. An external validation study consisting of 1200 canine lateral radiographs was randomly selected to match the underlying distribution of vertebral heart scores. Three American College of Veterinary Internal Medicine board-certified cardiologists were enrolled to manually score 400 images each using the traditional Buchanan method. Post-scoring, the cardiologists evaluated the algorithm for misaligned anatomic landmarks and overall image quality.
The 95th percentile absolute difference between the cardiologist vertebral heart score and the algorithm vertebral heart score was 1.05 vertebrae (95% confidence interval: 0.97 to 1.20 vertebrae) with a mean bias of -0.09 vertebrae (95% confidence interval: -0.12 to -0.05 vertebrae). In addition, the model was observed to be well calibrated across the predictive range.
We have found the performance of the vertebral heart score algorithm comparable to three board-certified cardiologists. While validation of this vertebral heart score algorithm has shown strong performance compared to veterinarians, further external validation in other clinical settings is warranted before use in those settings.
椎体心脏评分是一种用于评估心脏相对于胸椎大小的指标。椎体心脏评分可作为识别和分期心脏疾病以及提供预后信息的有用工具。本研究的目的是验证使用椎体心脏评分算法与三位经过董事会认证的兽医心脏病专家手动椎体心脏评分的效果。
开发了一个以相关解剖特征的语义分割为中心的卷积神经网络，用于预测心脏大小和椎体。这些预测用于计算椎体心脏评分。选择了一项包含 1200 张犬侧位射线照片的外部验证研究，以匹配椎体心脏评分的基础分布。招募了三位美国兽医内科学院董事会认证的心脏病专家，每位专家使用传统的布坎南方法手动对 400 张图像进行评分。评分后，心脏病专家评估了算法在解剖标志错位和整体图像质量方面的表现。
心脏病专家的椎体心脏评分与算法的椎体心脏评分之间的第 95 个百分位绝对差值为 1.05 个椎体（95%置信区间：0.97 至 1.20 个椎体），平均偏差为-0.09 个椎体（95%置信区间：-0.12 至-0.05 个椎体）。此外，观察到该模型在整个预测范围内具有良好的校准性能。
我们发现椎体心脏评分算法的性能与三位经过董事会认证的心脏病专家相当。虽然该椎体心脏评分算法的验证显示与兽医相比具有良好的性能，但在这些环境中使用之前，还需要在其他临床环境中进行进一步的外部验证。
