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  1. B. Gracia Tello1,2,
  2. E. Ramos3,
  3. C. P. Simeón-Aznar4,
  4. V. Fonollosa Pla4,
  5. A. Guillén-Del-Castillo4,
  6. A. Selva-O’callaghan4,
  7. L. Sáez-Comet5,
  8. E. Martínez Robles6,
  9. J. J. Rios6,
  10. G. Espinosa7,
  11. J. A. Todolí Parra8,
  12. J. L. Callejas-Rubio9,
  13. N. Ortego9,
  14. B. Marí-Alfonso10,
  15. M. Freire11,
  16. P. Fanlo12
  1. 1Hospital Clinico Universitario Lozano Blesa, Department of Internal Medicine, Zaragoza, Spain
  2. 2Zaragoza, Instituto de investigación Sanitaria de Aragón (ISSA), Zaragoza, Spain
  3. 3Zaragoza, Computer Science Graduate by University of Zaragoza, Zaragoza, Spain
  4. 4Hospital Universitario Vall d’Hebron, Unit of Autoimmune Diseases, Department of Internal Medicine, Barcelona, Spain
  5. 5Hospital Universitario Miguel Servet, Zaragoza, Spain, Department of Internal Medicine, Zaragoza, Spain
  6. 6Hospital General Universitario La Paz, Department of Internal Medicine, Madrid, Spain
  7. 7Hospital Clínic, Department of Internal Medicine, Unit of Autoimmune Diseases, Barcelona, Spain
  8. 8Hospital La Fe, Valencia, Spain, Department of Internal Medicine, Valencia, Spain
  9. 9Hospital Universitario Virgen de las Nieves, Granada, Spain, Unit of Autoimmune Diseases, Department of Internal Medicine, Granada, Spain
  10. 10Hospital Universitario Parc Taulí, Sabadell (Barcelona), Spain, Department of Internal Medicine, Barcelona, Spain
  11. 11Complejo Hospitalario Universitario de Santiago, Department of Internal Medicine, Santiago de Compostela, Spain
  12. 12Complejo Hospitalario de Navarra, Department of Internal Medicine, Pamplona, Spain


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. There are two main elements to nailfold capillaroscopy: image acquisition and image interpretation. Both of these can present challenges: we need to ensure that the best possible images are captured, and we need to define the enlarged capillaries, capillary loss and pericapillary hemorrhages objectively. We introduce, 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. Together it allows a comprehensive analysis that is capable of producing detailed reports for each patient.

Objectives: The primary outcome was general sensitivity and specificity, using images assessed by expert capillaroscopists as the gold standard.

Methods: 6500 images previously analyzed by capillaroscopists from GREC were compared with Capillary morphology (enlarged capillaries, tortuosities, ramifications, megacapillaries, hemorrhages) of each of the capillaries contained in each of the images was analyzed manually by at least one expert capillaroscopist. Subsequently, the automatic image interpretation system was used to fully automatically analyze each of the capillaries contained in each image and the results obtained were compared.

Results: Overall, a total of 78.347 capillaries were compared, of which 47.734 were normal capillaries, 21.991 enlarged capillaries, 2672 megacapillaries, 8512 tortuosities, 1322 ramifications and 5149 hemorrhages. was able to detect 38.101 normal capillaries, 19.126 enlarged capillaries, 2389 megacapillaries, 5698 tortuosities, 718 ramifications and 3706 hemorraghes. presented a sensitivity (S) of 79.82% and a specificity (E) of 82% in the recognition of normal capillaries. The automatized system was able to recognize enlarged capillaries with a sensitivity of 86.97% and a specificity of 81.38%. Megacapillaries were detected with 89.41% sensitivity and 78.75% specificity. Similarly, the system was able to detect tortuosities (S 66.94%; E 67.71%), ramifications (S 54.34%; E 58.61%) and hemorrhages (S 71.36; E 73.97%).

Conclusion: is a simple, easy-learning web-based system to get interpretation of nailfold capillaroscopic images. It may be a very useful tool to standardize the interpretation of capillaroscopic pictures and could provide great research in that field.

References: [1]Bernardino V, Rodrigues A, Lladó A, Fernandes M, Panarra A. The Impact of Nailfold Capil- laroscopy in the Approach of Microcirculation. In: Vascular Biology - Selection of Mechanisms and Clinical Applications IntechOpen; 2020.

[2]Boulon C, Devos S, Mangin M, Chevoir JDL, Senet P, Lazareth I, et al. Reproducibility of capillaro- scopic classifications of systemic sclerosis: results from the SCLEROCAP study. Rheumatology 2017 jul;56(10):1713–1720.

[3]Capillary io, Capillaroscopy made quick, simple and objective; 2020. Available at https://

[4]Chen K, Wang J, Pang J, Cao Y, Xiong Y, Li X, et al. MMDetection: Open MMLab Detection Toolbox and Benchmark. arXiv preprint arXiv:190607155 2019;.

[5]Cutolo M, Pizzorni C, Sulli A. Nailfold videocapillaroscopy assessment of microvascular damage in systemic sclerosis - Reply. The Journal of Rheumatology 2000 11;27:2722–2723.

[6]Cutolo M, Trombetta AC, Melsens K, Pizzorni C, Sulli A, Ruaro B, et al. Automated assessment of absolute nailfold capillary number on videocapillaroscopic images: Proof of principle and validation in systemic sclerosis. Microcirculation 2018 May;25(4):e12447.

Disclosure of Interests: Borja Gracia Tello Shareholder of: Co-founder and shareholder of, Eduardo Ramos Shareholder of: co-founder and shareholder of, Carmen Pilar Simeón-Aznar: None declared, Vicent Fonollosa Pla: None declared, Alfredo Guillén-Del-Castillo: 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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