Open-access ultrasonic diaphragm dataset and an automatic diaphragm measurement using deep learning network.

ABSTRACT:

Background

The assessment of diaphragm function is crucial for effective clinical management and the prevention of complications associated with diaphragmatic dysfunction. However, current measurement methodologies rely on manual techniques that are susceptible to human error: How does the performance of an automatic diaphragm measurement system based on a segmentation neural network focusing on diaphragm thickness and excursion compare with existing methodologies?

Methods

The proposed system integrates segmentation and parameter measurement, leveraging a newly established ultrasound diaphragm dataset. This dataset comprises B-mode ultrasound images and videos for diaphragm thickness assessment, as well as M-mode images and videos for movement measurement. We introduce a nov

SUBMITTER: Li Z 

PROVIDER: S-EPMC12275275 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature