Adaptación de redes neuronales convolucionales para la interpretación de ecografía abdominal en tiempo real

Point of care ultrasound (POCUS) is commonly used for diagnostic triage of internal injuries in both civilian and military trauma. In resource constrained environments, such as mass-casualty situations on the battlefield, POCUS allows medical providers to rapidly and noninvasively assess for free fluid or hemorrhage induced by trauma. A major disadvantage of POCUS diagnostics is the skill threshold needed to acquire and interpret ultrasound scans. For this purpose, AI has been shown to be an effective tool to aid the caregiver when interpreting medical imaging. Here, we focus on sophisticated AI training methodologies to improve the blind, real-time diagnostic accuracy of AI models for detection of hemorrhage in two major abdominal scan sites. In this work, we used a retrospective dataset of over 60,000 swine ultrasound images to train binary classification models exploring frame-pooling methods using the backbone of a pre-existing model architecture to handle multi-channel inputs for detecting free fluid in the pelvic and right-upper-quadrant regions. Earlier classifications models had achieved 0.59 and 0.70 accuracy metrics in blind predictions, respectively. After implementing this novel training technique, performance accuracy improved to over 0.90 for both scan sites. These are promising results demonstrating a significant diagnostic improvement which encourages further optimization to achieve similar results using clinical data. Furthermore, these results show how AI-informed diagnostics can offload cognitive burden in situations where casualties may benefit from rapid triage decision making.