Raster Scanning Optoacoustic Mesoscopy (RSOM) is a novel optoacoustic imaging modality that offers non‐invasive, label‐free, high resolution (~7 μm axial, ~30 μm lateral) imaging up to 1 to 2 mm below the skin, providing novel quantitative insights into skin pathophysiology. As the RSOM image contrast mechanism is based on light absorption, it is expected that the amount of melanin present in the skin will affect RSOM images. However, the effect of skin tone in the performance of RSOM has not been addressed so far. Herein, we present the efficiency of RSOM for in vivo skin imaging of human subjects with Fitzpatrick (FP) skin types between II to V. RSOM images acquired from the volar forearms of the subjects were used to derive metrics used in RSOM studies, such as total blood volume, vessel diameter and melanin signal intensity. Our study shows that the melanin signal intensity derived from the RSOM images exhibited an excellent correlation with that obtained from a clinical colorimeter for the subjects of varying FP skin types. We could successfully estimate the vessel diameter at different depths of the dermis. Furthermore, our study shows that there is a need to compensate for total blood volume calculated for subjects with higher FP skin types due to the lower signal‐to‐noise ratio in dermis, owing to strong absorption of light by melanin. This study sheds light into how RSOM can be used for studying various skin conditions in populations with different skin phenotypes.

The autoimmune disease systemic sclerosis (SSc) causes microvascular changes that can be easily observed cutaneously at the finger nailfold. Optoacoustic imaging (OAI), a combination of optical and ultrasound imaging, specifically raster-scanning optoacoustic mesoscopy (RSOM), offers a non-invasive high-resolution 3D visualization of capillaries allowing for a better view of microvascular changes and an extraction of volumetric measures. In this study, nailfold capillaries of patients with SSc and healthy controls are imaged and compared with each other for the first time using OAI. The nailfolds of 23 patients with SSc and 19 controls were imaged using RSOM. The acquired images were qualitatively compared to images from state-of-the-art imaging tools for SSc, dermoscopy and high magnification capillaroscopy. The vascular volume in the nailfold capillaries were computed from the RSOM images. The vascular volumes differ significantly between both cohorts (0.216 ± 0.085 mm3 and 0.337 ± 0.110 mm3; p < 0.0005). In addition, an artificial neural network was trained to automatically differentiate nailfold images from both cohorts to further assess whether OAI is sensitive enough to visualize anatomical differences in the capillaries between the two cohorts. Using transfer learning, the model classifies images with an area under the ROC curve of 0.897, and a sensitivity of 0.783 and specificity of 0.895. In conclusion, this study demonstrates the capabilities of RSOM as an imaging tool for SSc and establishes it as a modality that facilitates more in-depth studies into the disease mechanisms and progression.

Optoacoustic (photoacoustic) mesoscopy offers unique capabilities in skin imaging and resolves skin features associated with detection, diagnosis and management of disease. A critical first step in the quantitative analysis of clinical optoacoustic images is to identify the skin surface in a rapid, reliable and automated manner. Nevertheless, most common edgeand surface-detection algorithms cannot reliably detect the skin surface on 3D raster-scan optoacoustic mesoscopy (RSOM) images, due to discontinuities and diffuse interfaces in the image. We present herein a novel dynamic programming approach that extracts the skin boundary as a 2D surface in one single step, as opposed to consecutive extraction of several independent 1D contours. A domain-specific energy function is introduced, taking into account the properties of volumetric optoacoustic mesoscopy images. The accuracy of the proposed method is validated on scans of the volar forearm of 19 volunteers with different skin complexions, for which the skin surface has been traced manually to provide a reference. Additionally, the robustness and the limitations of the method are demonstrated on data where the skin boundaries are low-contrast or ill-defined. The automatic skin surface detection method can improve the speed and accuracy in the analysis of quantitative features seen on RSOM images and accelerate the clinical translation of the technique. Our method can likely be extended to identify other types of surfaces in RSOM and other imaging modalities.

The proposed method can produce optoacoustic volumes with an enlarged field of view and improved quality compared to current methods in optoacoustic imaging. However, our study also shows challenges for panoramic scans. In this view, we discuss relevant properties, challenges, and opportunities and present an evaluation of the performance of the presented approach with different input data.

Background Nailfold capillaroscopy offers a non-invasive route to observation of systemic sclerosis (SSc)-related microvascular changes and is used routinely for inspection of the capillaries at the finger nailfold. The characteristic changes in capillary structure (increased capillary width, decreased capillary density and abnormal angiogenesis) are included in the 2013 classification criteria for SSc. Optoacoustic mesoscopy is a combination of optical and ultrasound imaging enabling a 3D perspective of capillaries at a similar resolution to commercial nailfold capillaroscopy systems. We have previously reported that quantitative measures of vascular volume and density extracted from optoacoustic images differ significantly between patients with SSc and healthy controls. The aim of this study was to determine whether an artificial neural network (deep machine learning) could correctly differentiate between images from patients with SSc and healthy controls. Methods Optoacoustic (3D, iThera, Germany) and ‘standard’ capillaroscopy images (2D, Optillia, Sweden) of the right and left ring finger nailfolds were acquired. Images were taken at the centre of the nailfold. Acquisition of the same capillaries was difficult in some cases. 2D, greyscale, maximum intensity projections were created from the 3D optoacoustic images. Capillaroscopy images were downsized to match the optoacoustic image resolution. For data augmentation purposes each image, from both the optoacoustic and capillaroscopy data sets, was sliced into multiple overlapping image sections of fixed size. Transfer learning was used to train the model on 'disease' classification (SSc vs control). The pre-trained neural networks learn general image features and subsequently, are fine-tuned on the image data to classify based on the previously learned features. Results Twenty four patients with SSc (19% female; median age 65 IQR [57-69]; duration of Raynaud’s phenomenon 18 [12-28] years; time since onset of first non-Raynaud’s feature 11 (5-18) years) and 19 controls (17% female; age 15 [39-55] years) took part in the study. Fifty random data splits were used to validate the model and showed an average classification accuracy of 0.81 ± 0.15, with an area under the ROC curve of 0.88 ± 0.13 for optoacoustic data. The classification specificity and sensitivity were 0.84 ± 0.22 and 0.77 ± 0.21 respectively for optoacoustic mesoscopy. Performing the same task on capillaroscopy images, achieved an average accuracy of 0.86 ± 0.12 (AUC: 0.92 ± 0.09). Conclusion Deep learning is able to achieve excellent differentiation between images from patients with SSc and controls for both optoacoustic and standard capillaroscopy. Limitations of the study include the relatively small participant numbers and direct comparison of the same capillaries not always being possible. Optoacoustic mesoscopy offers huge potential to increase our understanding of the microvasculature in SSc. Disclosures S. Nitkunanantharajah None. K. Haedicke Corporate appointments; KH is an employee of iThera. T.L. Moore None. J.B. Manning None. G. Dinsdale None. M. Berks None. D. Jüstel None. V. Ntziachristos None. C. Taylor None. M. Dickinson None. A. Herrick None. A.K. Murray None.