Complex wound analysis using AI

Le résumé fourni par la source

Impaired wound healing is a significant clinical challenge. Standard wound analysis approaches are macroscopic, with limited histological assessments that rely on visual inspection of haematoxylin and eosin (H&E)-stained sections of biopsies. The analysis is time-consuming, requires a specialist trained to recognise various wound features, and therefore is often omitted in practice. We present an automated deep-learning (DL) approach capable of objectively and comprehensively analysing images of H&E-stained wound sections. Our model has a deep neural network (DNN) architecture, optimised for segmentation of characteristic wound features. We employed our model for the first-time analysis of human complex wounds. Histologically, human wounds are extremely variable, which presented a challenge when segmenting the different tissue classes. To validate our approach, we used mouse wound biopsy images across four timepoints of healing and employed the same DNN architecture for training and analysis in this context (89 % mean test set accuracy). We revised our approach for human complex wounds, analysing the biopsies at a cellular level, where our model performance improved (97 % mean test set accuracy). Together, our approach allows: (i) comprehensive analysis of human wound biopsy images; (ii) in-depth analysis of key features of mouse wound healing with accurate morphometric analysis and; (iii) analysis and quantification of immune cell infiltration, to aid clinical diagnosis of human complex wounds. • Analysis of human complex wounds histology may provide crucial diagnostic insights. • Novel deep-learning-based framework significantly augments wound histology analysis. • Limited dataset size/variability, does not severely inhibit model performance. • Mouse model capable of accurate inference on out-of-domain histology.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.