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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.

Background:With nailfold capillaroscopy there are different aspects of reliability to consider, including reliability of qualitative image grading (e.g. ‘early”, ‘active” and ‘late”) and also of semi-quantitative and quantitative measures including capillary density, apical width and presence of giant capillaries. The reliability of image acquisition (i.e. test-retest reliability) is especially important if nailfold capillaroscopic parameters are to be used in longitudinal studies (e.g. clinical trials) which involve acquiring repeat images over time. Lastly when assessing reliability, it is important to recognise that the nailfold capillaries cannot always be clearly seen, and cannot therefore be evaluated.Objectives:To highlight the different aspects of reliability relating to nailfold capillaroscopy and how these have been addressed in studies over the last 10 years.Methods:Review of recent studies.Results:Intra–observer reliability has been shown in several studies to be higher than inter–observer.Assessment of ‘evaluability” varies between observers and therefore also needs to be taken into account when assessing reliability.Subject to evaluability, certain parameters demonstrate high intra– and inter–observer reliabilities. Reliability differs across different capillaroscopic parameters.Conclusion:Recent studies examining reliability of capillaroscopy suggest that certain parameters, including image grade, capillary density and apex width have high intra-and inter-observer reliabilities (subject to nailfold image evaluability, which remains a major challenge). Standardised training is likely to improve reliability.References:[1] Murray AK, Vail A, Moore TL, et al. The influence of measurement location on reliability of quantitative videocapillaroscopy in patients with SSc. Rheumatol 2012; 51 : 1323-30.[2] Overbury R, Murtaugh MA, Fisher A, et al. Primary care assessment of capillary abnormalities in patients with Raynaud”s phenomenon. Clin Rheumatol 2015; 34: 2135-40.[3] Smith V, Beeckman S, Herrick AL, et al. An EULAR study group pilot study on reliability of simple capillaroscopic definitions to describe capillary morphology in rheumatic diseases. Rheumatol 2016; 55: 883-90.[4] Dinsdale G, Moore T, O”Leary N, et al. Intra-and inter-observer reliability of nailfold videocapillaroscopy - A possible outcome measure for systemic sclerosis-related microangiopathy. Microvascular Research 2017; 112: 1-6.[5] Dinsdale G, Moore T, O”Leary N, et al. Quantitative outcome measures for systemic sclerosis-related microangiopathy – reliability of image acquisition in nailfold capillaroscopy. Microvascular Research 2017;113:56-9.[6] Boulon C, Devos S, Mangin M, et al. Reproducibility of capillaroscopic classifications of systemic sclerosis: results from the SCLEROCAP study. Rheumatology 2017; 56: 1713-20.[7] Cutolo M, Melsens K, Herrick AL, et al. Reliability of simple capillaroscopic definitions in describing capillary morphology in rheumatic diseases. Rheumatology (Letter) 2018; 57: 757-9.Disclosure of Interests:None dec...

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