Validation of a You Only Look Once–LabelMe Hybrid AI Model for Automated Assessment of Pressure Injury Size and Pocket

ABSTRACT

Quantitative assessment of the pressure injury size (S) and pocket (P, undermining) is essential for evaluating wound healing. Conventional LabelMe annotations often lead to oversegmentation, whereas object detection with You Only Look Once (YOLO) enables accurate wound localization. Here, we developed a hybrid AI model that combines LabelMe segmentation with YOLO gating. This single-centre retrospective study compared two models: LabelMe-Seeded Auto-Recognition (LSAR) and YOLO-gated LSAR (YGL). Model performance was evaluated by concordance rate and weighted Cohen's κ for DESIGN-R staging and by mean absolute error (MAE) and median (interquartile range) for area error. Overall, 1017 wound images (training: 979; testing: 38) were analysed. For S, the YGL model achieved higher concordance (85.7%, κ = 0.969) and smaller area errors than did the LSAR. For P, the YGL model demonstrated higher concordance and smaller area errors than did the LSAR model. The YOLO–LabelMe hybrid model improved the stage concordance and size accuracy compared with LabelMe alone. Despite the residual outliers in P assessment, this approach represents a promising step towards automated, clinically adaptable wound measurements.