Background: A nailfold capillaroscopy procedure is a non-invasive, low-cost, and well-established examination that can be used to diagnose several rheumatic autoimmune diseases and support the necessary follow-up of patients. While the clinical implications of the technique are known, a rigorous and in-depth examination of nailfold capillaries remains as one of the major challenges to produce new advances in research and diagnosis, due to practical limitations for analysing the whole nailfold area of each patient. The difference between the different patterns established by Maricq and Cutolo makes it possible to predict the evolution that the patient will present. We introduce Capillary.io, an automatic image reading system able to recognize capillaries in images obtained with any microscope, generate automatic measurements of each capillary and take advantage of this information to report capillary morphology and patterns.
Objectives: to determine the ability to detect active and early scerodermiform patterns of Capillary.io.
Methods: Forty-nine complete capillaroscopies, reported by expert capillaroscopists according to the different patterns manually (gold standard), were compared with the pattern detection capability of Capillary.io. A scoring system based on the algorithm of the Spanish Capillaroscopy Study Group (GREC) was performed and interpreted by capillary.io for the global interpretation of each of the capillaroscopies analyzed.
Results: Overall, 37 of the 49 capillaroscopies reported agreed with the diagnosed pattern (75.51%). Separately, the early pattern presented a concordance of 77.27% and the active pattern of 74.07%. In reference to the findings detected by the Capillary.io system, the mean overall density was 5.01 capillaries/mm in the group with the active pattern compared to 6.46 capillaries/mm in the early pattern. The density of dilations and megacapillaries was 2.81/mm and 1.21/mm in the active pattern group versus 4.69/mm and 0.4/mm in the early pattern group. Global diameters were greater in the active pattern group with an apical mean of 37.3 μm compared to 28.5 μm in the early pattern subgroup.
Conclusion: Capillary.io is a simple, easy-to-learn web system for interpreting capillaroscopic images of nail folds. It can be a very useful tool to standardize the interpretation of capillaroscopic images, not only individually for each capillary, but also jointly through the detection of different patterns.
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Disclosure of Interests: Eduardo Ramos Shareholder of: Co-founder and shareholder of Capillary.io, Alfredo Guillén-Del-Castillo: None declared, Carmen Pilar Simeón-Aznar: None declared, Borja Gracia Tello Shareholder of: Co-founder and shareholder of Capillary.io, Vicent Fonollosa Pla: None declared, Albert Selva-O’Callaghan: None declared, Luis Sáez-Comet: None declared, Elena Martínez Robles: None declared, Juan José Rios: None declared, Gerard Espinosa: None declared, Jose Antonio Todolí Parra: None declared, Jose Luis Callejas-Rubio: None declared, Norberto Ortego: None declared, Begoña Marí-Alfonso: None declared, Mayka Freire: None declared, Patricia Fanlo: None declared
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