AI-driven quantification of ground glass opacities in lungs of COVID-19 patients using 3D computed tomography imaging.

ABSTRACT:

Objectives

Ground-glass opacity (GGO)-a hazy, gray appearing density on computed tomography (CT) of lungs-is one of the hallmark features of SARS-CoV-2 in COVID-19 patients. This AI-driven study is focused on segmentation, morphology, and distribution patterns of GGOs.

Method

We use an AI-driven unsupervised machine learning approach called PointNet++ to detect and quantify GGOs in CT scans of COVID-19 patients and to assess the severity of the disease. We have conducted our study on the "MosMedData", which contains CT lung scans of 1110 patients with or without COVID-19 infections. We quantify the morphologies of GGOs using Minkowski tensors and compute the abnormality score of individual regions of segmented lung and GGOs.

Results

PointNet++ detects GGOs with the hi

SUBMITTER: Saha M 

PROVIDER: S-EPMC8920286 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature