Can artificial intelligence help physicians using diaphragmatic ultrasound?

Abstract

Purpose

We investigated the role of artificially intelligent architecture based on deep learning radiomics (DLR) in analyzing M-mode and B-mode ultrasound videos of the diaphragm for diaphragmatic ultrasound.

Methods

A total of 196 subjects underwent pulmonary function and ultrasonic examination of the diaphragm. All diaphragmatic ultrasound videos were collected by experienced sonographers as the entire dataset used in this study. The experiment was partitioned into two parts. First, the diaphragm images (including M-mode and B-mode) of 157 subjects were input into the artificial intelligence architecture by the AI team. Second, the test set comprised 39 subjects, each equipped with three mobility images and three thickness images. We applied the proposed parameter calculation method to this set. The method entails segmenting the images, extracting the diaphragmatic motion and thickness variation curves from the segmentation results, and subsequently analyzing these curves to acquire the target parameters. Concurrently, we documented the time taken for each measurement. In parallel, three medical professionals performed analogue measurements. We analysed the accuracy and consistency of the artificial intelligence measurements.

Results

The study included a total of 196 subjects. The optimal segmentation model achieved dice scores of 73.51 % and 80.76 % on the test sets of mobility images and thickness images, respectively. Our method yielded results similar to those obtained by senior sonographers and demonstrated a high level of consistency with all three medical professionals, particularly the senior sonographer, in the measurements of diaphragm excursion (DE), diaphragm contraction duration (DCD), and diaphragmatic thickness at the end of inspiration (DTei). Meanwhile, our proposed method exhibited the highest level of time efficiency. The average duration for measuring the mobility images was 1.49s and for thickness images was 0.68s, compared to critical care physicians (8.23s, 15.89s), junior sonographers (6.14s, 9.69s), and senior sonographers (4.48s,6 0.77s).

Conclusions

Our study suggests that artificial intelligence can assist physicians in obtaining accurate diaphragmatic ultrasound data and reducing interobserver variability. Additionally, it could also improve time efficiency in this process.