Three-Dimensional Multiscale Fuzzy Entropy: Validation and Application to Idiopathic Pulmonary Fibrosis

Idiopathic Pulmonary Fibrosis (IPF) is a chronic, severe, and progressive lung disease with short life expectancy. Based on information theory and entropy measurement, a three-dimensional multiscale fuzzy entropy (MFE3D) algorithm is proposed to identify IPF patients from their computed tomography (CT) volumetric data. First, the validation of the algorithm was performed by analyzing several volumetric synthetic noises (white, blue, brown, and pink), MIX(p) processes-based volumes, and texture-based volumes. The entropy values obtained by MFE3D were consistent with the values obtained using the one, and two-dimensional versions, validating its use in biomedical data. Hence, MFE3D was applied to CT scans to identify the existence of IPF within two different groups, one of healthy subjects (26) and another of IPF patients (26). Statistical differences were found (p < 0.05) between the entropy values of each group in 5 scale factors out of 10. These results demonstrate that MFE(3D)could be an interesting metric to identify IPF in CT scans.

Automated summary

The study proposed a three-dimensional multiscale fuzzy entropy (MFE3D) algorithm to identify Idiopathic Pulmonary Fibrosis (IPF) patients from their computed tomography (CT) volumetric data. The algorithm was validated using synthetic noises, MIX(p) processes-based volumes, and texture-based volumes, showing consistency with one and two-dimensional versions. MFE3D was applied to CT scans of healthy subjects and IPF patients, revealing statistical differences in entropy values between the groups, indicating potential for IPF identification in CT scans.