Atopic dermatitis (AD) is a common inflammatory skin disorder which affects ~20% of children and ~3% adults worldwide. There lacks a direct, non-invasive method of evaluating atopic dermatitis (AD) accurately. Here, the use a multispectral raster-scanning optoacoustic mesoscopy (MS-RSOM) as an objective imaging tool for AD is proposed. MS-RSOM is a novel, non-invasive optoacoustic imaging modality which can provide label-free, high resolution imaging up to 1.5 mm below the skin. It can provide useful information on melanin, oxyhemoglobin (HbO2), deoxyhemoglobin (Hb) and oxygen saturation (sO2) from the skin layers. This preliminary study was conducted on 4 AD patients and 2 healthy volunteers using MS-RSOM system. From the MS-RSOM images, the epidermis thickness and oxygen saturation were computed from the healthy volunteers as well as from the non-lesional and eczema lesional areas of the eczema subjects.
A comprehensive analysis using three machine-learning models for an AI-aided atopic dermatitis (AD) diagnosis and sub-classifying AD severities with 3D Raster Scanning Optoacoustic Mesoscopy (RSOM) images, extracted features from volumetric vascular structures and clinical information.
Inflammatory skin disorder, eczema, is usually assessed by subjective disease scoring systems such as SCORAD and EASI. These scoring systems are based on clinical observations and questionnaires and hence it is subjected to inter and intra-assessor variability. Here, for the first time, we used optoacoustic imaging to image the structural and morphological changes of the skin in a non-invasive manner. Through a clinical study, we computed specific metrics such as epidermis thickness, total blood volume, vessel diameter in the dermis, ratio of low and high frequency signals. We trained a linear kernel-based support vector machine model for eczema classification using these metrics. We could achieve an accuracy of 86.6% and high sensitivity and specificity of 96.2% and 82.1% respectively. We also formulated a novel Eczema Vascular and Structural Index (EVSI) to objectively assess the severity of eczema.
Significance: Noninvasive in vivo fast pulsatile blood flow measurement in deep tissue is important because the blood flow waveform is correlated with physiological parameters, such as blood pressure and elasticity of blood vessels. Compromised blood flow may cause diseases, such as stroke, foot ulcer, and myocardial ischemia. There is great clinical demand for a portable and cost-effective device for noninvasive pulsatile blood flow measurement.
Aim: A diffuse-optics-based method, diffuse speckle pulsatile flowmetry (DSPF), was developed for fast measurement (∼300 Hz) of deep tissue blood flow noninvasively. To validate its performance, both a phantom experiment and in vivo demonstration were conducted.
Approach: Over the past two decades, single-mode fibers have been used as detection fibers in most diffuse-optics-based deep tissue blood flow measurement modalities. We used a multimode (MM) detection fiber with a core size of 200 μm for diffused speckle pattern detection. A background intensity correction algorithm was implemented for speckle contrast calculation. The MM detection fiber helped to achieve a level of deep tissue blood flow measurement similar to that of conventional modalities, such as diffuse correlation spectroscopy and diffuse speckle contrast analysis, but it increases the measurement rate of blood flow to 300 Hz.
Results: The design and implementation of the DSPF system were introduced. The theory of the background intensity correction for the diffused speckle pattern detected by the MM fiber was explained. A flow phantom was built for validation of the performance of the DSPF system. An in vivo cuff-induced occlusion experiment was performed to demonstrate the capability of the proposed DSPF system.
Conclusions: An MM detection fiber can help to achieve fast (∼300 Hz) pulsatile blood flow measurement in the proposed DSPF method. The cost-effective device and the fiber-based flexible probe increase the usability of the DSPF system significantly.
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