Automated quantification of 3D wound morphology by machine learning and optical coherence tomography in type 2 diabetes.

ABSTRACT:

Background

Driven by increased prevalence of type 2 diabetes and ageing populations, wounds affect millions of people each year, but monitoring and treatment remain limited. Glucocorticoid (stress hormones) activation by the enzyme 11β-hydroxysteroid dehydrogenase type 1 (11β-HSD1) also impairs healing. We recently reported that 11β-HSD1 inhibition with oral AZD4017 improves acute wound healing by manual 2D optical coherence tomography (OCT), although this method is subjective and labour-intensive.

Objectives

Here, we aimed to develop an automated method of 3D OCT for rapid identification and quantification of multiple wound morphologies.

Methods

We analysed 204 3D OCT scans of 3 mm punch biopsies representing 24 480 2D wound image frames. A u-net method was used for

SUBMITTER: Wang Y 

PROVIDER: S-EPMC10233090 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature