# Predictive models of severe disease in patients with COVID-19 pneumonia at an early stage on CT images using topological properties

## TL;DR

This paper presents a method to predict severe disease in early-stage COVID-19 pneumonia using topological features from CT images to improve patient care and decision-making.

## Contribution

The novelty lies in using topological properties from accumulated Betti number maps to predict severe disease in early-stage COVID-19 pneumonia.

## Key findings

- The model achieved an area under the ROC curve of 0.854 for predicting severe disease.
- Topological features from accumulated Betti number maps effectively characterized early-stage pneumonia.
- The model demonstrated high sensitivity (0.908) in identifying severe cases.

## Abstract

Prediction of severe disease (SVD) in patients with coronavirus disease (COVID-19) pneumonia at an early stage could allow for more appropriate triage and improve patient prognosis. Moreover, the visualization of the topological properties of COVID-19 pneumonia could help clinical physicians describe the reasons for their decisions. We aimed to construct predictive models of SVD in patients with COVID-19 pneumonia at an early stage on computed tomography (CT) images using SVD-specific features that can be visualized on accumulated Betti number (BN) maps. BN maps (b0 and b1 maps) were generated by calculating the BNs within a shifting kernel in a manner similar to a convolution. Accumulated BN maps were constructed by summing BN maps (b0 and b1 maps) derived from a range of multiple-threshold values. Topological features were computed as intrinsic topological properties of COVID-19 pneumonia from the accumulated BN maps. Predictive models of SVD were constructed with two feature selection methods and three machine learning models using nested fivefold cross-validation. The proposed model achieved an area under the receiver-operating characteristic curve of 0.854 and a sensitivity of 0.908 in a test fold. These results suggested that topological image features could characterize COVID-19 pneumonia at an early stage as SVD.

The online version contains supplementary material available at 10.1007/s12194-025-00906-1.