Chronic wounds affect over 6.5 million Americans and are notoriously difficult to treat. Suboptimal oxygenation of the wound bed is one of the most critical and treatable wound management factors, but existing oxygenation systems do not enable concurrent measurement and delivery of oxygen in a convenient wearable platform. Thus, we developed a low-cost alternative for continuous O2 delivery and sensing comprising of an inexpensive, paper-based, biocompatible, flexible platform for locally generating and measuring oxygen in a wound region. The platform takes advantage of recent developments in the fabrication of flexible microsystems including the incorporation of paper as a substrate and the use of a scalable manufacturing technology, inkjet printing. Here, we demonstrate the functionality of the oxygenation patch, capable of increasing oxygen concentration in a gel substrate by 13% (5 ppm) in 1 h. The platform is able to sense oxygen in a range of 5–26 ppm. In vivo studies demonstrate the biocompatibility of the patch and its ability to double or triple the oxygen level in the wound bed to clinically relevant levels.

Two-photon microscopes have been successfully translated into clinical imaging tools to obtain high-resolution optical biopsies for in vivo histology. We report on clinical multiphoton coherent anti-Stokes Raman spectroscopy (CARS) tomography based on two tunable ultrashort near-infrared laser beams for label-free in vivo multimodal skin imaging. The multiphoton biopsies were obtained with the compact tomograph "MPTflex-CARS" using a photonic crystal fiber, an optomechanical articulated arm, and a four-detector-360 deg measurement head. The multiphoton tomograph has been employed to patients in a hospital with diseased skin. The clinical study involved 16 subjects, 8 patients with atopic dermatitis, 4 patients with psoriasis vulgaris, and 4 volunteers served as control. Two-photon cellular autofluorescence lifetime, second harmonic generation (SHG) of collagen, and CARS of intratissue lipids/proteins have been detected with single-photon sensitivity, submicron spatial resolution, and picosecond temporal resolution. The most important signal was the autofluorescence from nicotinamide adenine dinucleotide [NAD(P)H]. The SHG signal from collagen was mainly used to detect the epidermal-dermal junction and to calculate the ratio elastin/collagen. The CARS/Raman signal provided add-on information. Based on this view on the disease-affected skin on a subcellular level, skin areas affected by dermatitis and by psoriasis could be clearly identified. Multimodal multiphoton tomographs may become important label-free clinical high-resolution imaging tools for in vivo skin histology to realize rapid early diagnosis as well as treatment control. © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

The diagnostic possibilities of multiphoton tomography (MPT) in dermatology have already been demonstrated. Nevertheless, the analysis of MPT data is still time-consuming and operator dependent. We propose a fully automatic approach based on convolutional neural networks (CNNs) to fully realize the potential of MPT. In total, 3,663 MPT images combining both morphological and metabolic information were acquired from atopic dermatitis (AD) patients and healthy volunteers. These were used to train and tune CNNs to detect the presence of living cells, and if so, to diagnose AD, independently of imaged layer or position. The proposed algorithm correctly diagnosed AD in 97.0 ± 0.2% of all images presenting living cells. The diagnosis was obtained with a sensitivity of 0.966 ± 0.003, specificity of 0.977 ± 0.003 and F-score of 0.964 ± 0.002. Relevance propagation by deep Taylor decomposition was used to enhance the algorithm's interpretability. Obtained heatmaps show what aspects of the images are important for a given classification. We showed that MPT imaging can be combined with artificial intelligence to successfully diagnose AD. The proposed approach serves as a framework for the automatic diagnosis of skin disorders using MPT.

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