The Role of Pleura and Adipose in Lung Ultrasound AI
Gautam Rajendrakumar Gare, Wanwen Chen, Alex Ling Yu Hung, Edward, Chen, Hai V. Tran, Tom Fox, Pete Lowery, Kevin Zamora, Bennett P deBoisblanc,, Ricardo Luis Rodriguez, John Michael Galeotti
TL;DR
This study investigates how the pleura and adipose tissue influence lung ultrasound AI, demonstrating that high-frequency probes and masking adipose tissue can enhance diagnostic accuracy.
Contribution
It reveals the importance of probe frequency and tissue masking in improving AI-based lung ultrasound diagnostics.
Findings
High-frequency linear probes provide better pleura detail.
Masking adipose tissue improves AI diagnostic accuracy.
HFL ultrasound enhances lung tissue visualization.
Abstract
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.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.