■军事工作犬(MWDs)对于各种任务中的军事行动至关重要。有了这个关键的角色,MWD可能成为需要专门兽医护理的人员伤亡,而这些护理在战场上可能并不总是可用的。一些损伤如气胸,血胸,或腹部出血可以使用诸如GlobalFAST®检查的护理点超声(POCUS)来诊断。这为人工智能(AI)提供了独特的机会,以帮助解释超声图像。在这篇文章中,开发了深度学习分类神经网络,用于MWD中的POCUS评估。
■在全麻或深度镇静下,对GlobalFAST®检查中的所有扫描点在五个MWD中收集图像。对于代表性的伤害,我们使用了尸体模型,从中捕获了阳性和阴性损伤图像.总共捕获了327个超声剪辑,并在扫描点之间进行分割,以训练三种不同的AI网络结构:MobileNetV2,DarkNet-19和ShrapML。为代表性图像生成梯度类激活映射(GradCAM)覆盖,以更好地解释AI预测。
■对于所有扫描点,AI模型的性能达到了82%以上的精度。使用MobileNetV2网络对具有最高性能的模型进行了训练,以获得99.8%的准确度。在所有经过训练的网络中,the肌肝肾扫描点具有最佳的整体性能。然而,GradCAM叠加显示,精度最高的模型,像MobileNetV2,并不总是识别相关的功能。相反,ShrapML的GradCAM热图显示与最能指示流体积聚的区域基本一致。
■总的来说,开发的人工智能模型可以自动预测MWD中的POCUS。初步而言,ShrapML具有最强的性能和预测率,与准确跟踪流体积聚部位配对,使其成为与超声系统最终实时部署的最合适的选择。该技术与成像技术的进一步集成将扩大基于POCUS的MWD分类的使用。
UNASSIGNED: Military working dogs (MWDs) are essential for military operations in a wide range of missions. With this pivotal role, MWDs can become casualties requiring specialized veterinary care that may not always be available far forward on the battlefield. Some injuries such as pneumothorax, hemothorax, or abdominal hemorrhage can be diagnosed using point of care ultrasound (POCUS) such as the Global FAST® exam. This presents a unique opportunity for artificial intelligence (AI) to aid in the interpretation of ultrasound images. In this article, deep learning classification neural networks were developed for POCUS assessment in MWDs.
UNASSIGNED: Images were collected in five MWDs under general anesthesia or deep sedation for all scan points in the Global FAST® exam. For representative injuries, a cadaver model was used from which positive and negative injury images were captured. A total of 327 ultrasound clips were captured and split across scan points for training three different AI network architectures: MobileNetV2, DarkNet-19, and ShrapML. Gradient class activation mapping (GradCAM) overlays were generated for representative images to better explain AI predictions.
UNASSIGNED: Performance of AI models reached over 82% accuracy for all scan points. The model with the highest performance was trained with the MobileNetV2 network for the cystocolic scan point achieving 99.8% accuracy. Across all trained networks the diaphragmatic hepatorenal scan point had the best overall performance. However, GradCAM overlays showed that the models with highest accuracy, like MobileNetV2, were not always identifying relevant features. Conversely, the GradCAM heatmaps for ShrapML show general agreement with regions most indicative of fluid accumulation.
UNASSIGNED: Overall, the AI models developed can automate POCUS predictions in MWDs. Preliminarily, ShrapML had the strongest performance and prediction rate paired with accurately tracking fluid accumulation sites, making it the most suitable option for eventual real-time deployment with ultrasound systems. Further integration of this technology with imaging technologies will expand use of POCUS-based triage of MWDs.