SEMTWIST Quantification of Biofilm Infection in Human Chronic Wound Using Scanning Electron Microscopy and Machine Learning.

ABSTRACT: Objective: To develop scanning electron microscopy-based Trainable Weka (Waikato Environment for Knowledge Analysis) Intelligent Segmentation Technology (SEMTWIST), an open-source software tool, for structural detection and rigorous quantification of wound biofilm aggregates in complex human wound tissue matrix. Approach: SEMTWIST model was standardized to quantify biofilm infection (BFI) abundance in 240 distinct SEM images from 60 human chronic wound-edge biospecimens (four technical replicates of each specimen). Results from SEMTWIST were compared against human expert assessments and the gold standard for molecular BFI detection, that is, peptide nucleic acid fluorescence in situ hybridization (PNA-FISH). Results: Correlation and Bland-Altman plot demonstrate

SUBMITTER: Singh S 

PROVIDER: S-EPMC12359142 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature