The Role of Pleura and Adipose in Lung Ultrasound AI
This work addresses the challenge of enhancing AI-based diagnostic tools for lung ultrasound, which is incremental as it builds on existing methods by optimizing feature selection.
The study tackled the problem of improving AI diagnostic accuracy in lung ultrasound by focusing on the pleura and adipose tissue, finding that masking adipose tissue during training and inference while retaining pleural line and artifacts increased the model's diagnostic accuracy.
In this paper, we study the significance of the pleura and adipose tissue in lung ultrasound AI analysis. We highlight their more prominent appearance when using high-frequency linear (HFL) instead of curvilinear ultrasound probes, showing HFL reveals better pleura detail. We compare the diagnostic utility of the pleura and adipose tissue using an HFL ultrasound probe. Masking the adipose tissue during training and inference (while retaining the pleural line and Merlin's space artifacts such as A-lines and B-lines) improved the AI model's diagnostic accuracy.