SurvdigitizeR: an algorithm for automated survival curve digitization.

ABSTRACT:

Background

Decision analytic models and meta-analyses often rely on survival probabilities that are digitized from published Kaplan-Meier (KM) curves. However, manually extracting these probabilities from KM curves is time-consuming, expensive, and error-prone. We developed an efficient and accurate algorithm that automates extraction of survival probabilities from KM curves.

Methods

The automated digitization algorithm processes images from a JPG or PNG format, converts them in their hue, saturation, and lightness scale and uses optical character recognition to detect axis location and labels. It also uses a k-medoids clustering algorithm to separate multiple overlapping curves on the same figure. To validate performance, we generated survival plots form random time-to-even

SUBMITTER: Zhang JZ 

PROVIDER: S-EPMC11245803 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature