Artificial Intelligence-Driven Janus Dressing for Visual Wound Theranostics

Wound exudate contains rich biochemical markers, among which dynamic pH fluctuations serve as a key predictor of infection progression and healing prognosis. However, real-time pH monitoring remains limited by the lack of platforms enabling exudate extraction and stable sensing. Here, we present an asymmetric theranostic Janus wound dressing that integrates unidirectional exudate transport, wide-gamut colorimetric pH sensing, and antibacterial therapy into a single closed-loop platform. The bilayer rapidly drains exudate from the curcumin/Ag-loaded hydrophobic polyurethane (PU) layer into a hydrophilic hydrogel for biochemical analysis. To overcome the inherent instability and leaching issues of conventional pH indicators, phenol red is confined within zeolitic imidazolate framework-8 nanoparticles, forming a leaching-resistant optical sensor with enhanced sensitivity and a broadened dynamic color range. This strategy enables accurate, lighting-independent pH quantification via a deep-learning-assisted smartphone application, enabling objective in situ assessment of infection risk and healing status. Both in vitro and in vivo evaluations confirm that this platform not only suppresses infection and accelerates wound closure but also provides prognostic biochemical feedback with clinical relevance. This work presents a comprehensive AI-assisted theranostic strategy that integrates wound microenvironment engineering with digital health tools to precision wound management.