AI-assisted ultrasound for early liver trauma: Animal models & clinical validation

Abstract

The study aimed to develop an AI-assisted ultrasound model for early liver trauma identification, using data from Bama miniature pigs and patients in Beijing, China. A deep learning model was created and fine-tuned with animal and clinical data, achieving high accuracy metrics. In internal tests, the model outperformed both Junior and Senior sonographers. External tests showed the model's effectiveness, with a Dice Similarity Coefficient of 0.74, True Positive Rate of 0.80, Positive Predictive Value of 0.74, and 95% Hausdorff distance of 14.84. The model's performance was comparable to Junior sonographers and slightly lower than Senior sonographers. This AI model shows promise for liver injury detection, offering a valuable tool with diagnostic capabilities similar to those of less experienced human operators.