Wivern: a Web-Based System Enabling Computer-Aided Diagnosis and Interdisciplinary Expert Collaboration for Vascular Research

  • Jorge Novo
  • José Rouco
  • Noelia Barreira
  • Marcos Ortega
  • Manuel G. Penedo
  • Aurélio Campilho
Original Article

Abstract

A complete analysis of the vascular system is a complex task since a large number of parameters are involved. In the research herein reported we present a novel medical framework called web-based integration for vascular expert research networks (Wivern) to be used in a multi-clinical department environment for the analysis of micro and macrocirculation. This tool can manage clinical information of several specialties, such as Neurology or Ophthalmology, and provides computer-aided tools to automatically analyze retinographies, carotid ultrasounds and blood pressure monitor signals, and to automatically compute cardiovascular risk stratification. Wivern is a web-based application with a user friendly interface that provides cross-platform compatibility and device independence. Several automated procedures are integrated within the framework, as a service on the web, to extract relevant parameters from clinical data, physiological signals and medical images. The application is planned for collecting and analyzing data in several clinical studies in different hospital centers to test their behavior and practical use of the different tools of the platform. The usefulness and validation of the system was achieved after the inclusion, by the different medical units, of 800 patients to analyze their hypertensive profile. Moreover, 800 retinal images were processed as well as 400 carotid were analyzed. Wivern provides a unique opportunity for vascular research since it enables an interdisciplinary and integrated study of the vascular network, allowing a more comprehensive evaluation of the consequences of any abnormality. The application also includes automated methods to process patient data in order to simplify the physician tasks.

Keywords

Vascular system Ambulatory blood pressure monitor Carotid ultrasound Intima media thickness Retinal imaging Arterio-venous ratio 

References

  1. 1.
    van Ginneken, B., Schaefer-Prokop, C. M., & Prokop, M. (2011). Computer-aided diagnosis: how to move from the laboratory to the clinic. Radiology, 261(3), 719–732. doi:10.1148/radiol.11091710.CrossRefGoogle Scholar
  2. 2.
    Coorevits, P., Sundgren, M., Klein, G. O., Bahr, A., Claerhout, B., Daniel, C., et al. (2013). Electronic health records: New opportunities for clinical research. Journal of Internal Medicine, 274, 547–560. doi:10.1111/joim.12119.CrossRefGoogle Scholar
  3. 3.
    Sinaci, A., & Laleci-Erturkmen, G. (2013). A federated semantic metadata registry framework for enabling interoperability across clinical research and care domains. Journal of Biomedical Informatics, 46(5), 784–794.CrossRefGoogle Scholar
  4. 4.
    Yeager, V. A., Walker, D., Cole, E., Mora, A. M., & Diana, M. L. (2014). Factors related to health information exchange participation and use. Journal of Medical Systems, 38, 78. doi:10.1007/s10916-014-0078-1.CrossRefGoogle Scholar
  5. 5.
    Cross, D. S., McCarty, C. A., Steinhubl, S. R., Carey, D. J., & Erlich, P. M. (2013). Development of a multi-institutional cohort to facilitate cardiovascular disease biomarker validation using existing biorepository samples linked to electronic health records. Clinical Cardiology, 36, 486–491. doi:10.1002/clc.22146.CrossRefGoogle Scholar
  6. 6.
    Johnson, T., Markowitz, E., Bernstam, E., Herskovic, J., & Thimbleby, H. (2013). Syfsa: A framework for systematic yet flexible systems analysis. Journal of Biomedical Informatics, 46(4), 665–675.CrossRefGoogle Scholar
  7. 7.
    Koutsojannis, C., & Hatzilygeroudis, I. (2008). Piesys: A patient model-based intelligent system for continuing hypertension management. Knowledge Management for Health Care Procedures, 4924, 137–148. Lecture Notes in Computer Science.CrossRefGoogle Scholar
  8. 8.
    Goldstein, M. (2008). Using health information technology to improve hypertension management. Current Hypertension Reports, 10(3), 201–207.CrossRefGoogle Scholar
  9. 9.
    Janes, H., Pepe, M., & Gu, W. (2008). Assessing the value of risk predictions by using risk stratification tables. Annals of Internal Medicine, 148(2), 102–110.CrossRefGoogle Scholar
  10. 10.
    The Task Force for the Management of Arterial. (2007). Hypertension of the European Society of Hypertension (ESH) and of the European Society of Cardiology (ESC): 2007 guidelines for the management of arterial hypertension. Journal of Hypertension, 25, 1105–1187.CrossRefGoogle Scholar
  11. 11.
    Grundy, S. M., Brewer, H. B., Cleeman, J. I., Smith, S. C., & Lenfant, C. (2004). For the conference participants: Definition of metabolic syndrome: Report of the National Heart, Lung, and Blood Institute/American Heart Association Conference on scientific issues related to definition. Circulation, 109(3), 433–438.CrossRefGoogle Scholar
  12. 12.
    Conroy, R., Pyrl, K., Fitzgerald, A., Sans, S., Menotti, A., De Backer, G., et al. (2003). Estimation of ten-year risk of fatal cardiovascular disease in europe: The SCORE project. European Heart Journal, 24(11), 987–1003.CrossRefGoogle Scholar
  13. 13.
    Kannel, W. B., McGee, D., & Gordon, T. (1976). A general cardiovascular risk profile: The framingham study. American Journal of Cardiology, 38(1), 46–51.CrossRefGoogle Scholar
  14. 14.
    National Heart Foundation and High Blood Pressure Research Council of Australia Ambulatory Blood Pressure Monitoring Consensus Committee (2011). Ambulatory blood pressure monitoring. Australian Family Physician, 40(11), 877–880.Google Scholar
  15. 15.
    Pickering, T. G., Shimbo, D., & Haas, D. (2006). Ambulatory blood-pressure monitoring. New England Journal of Medicine, 354(22), 2368–2374. PMID: 16738273.CrossRefGoogle Scholar
  16. 16.
    Kwon, H. M., Shin, J. W., Lim, J. S., Hong, Y. H., Lee, Y. S., & Nam, H. (2013). Comparison of the effects of amlodipine and losartan on blood pressure and diurnal variation in hypertensive stroke patients: A prospective, randomized, double-blind, comparative parallel study. Clinical Therapeutics, 35(12), 1975–1982.CrossRefGoogle Scholar
  17. 17.
    Mahabala, C., Kamath, P., Bhaskaran, U., Pai, N. D., & Pai, A. U. (2013). Antihypertensive therapy: Nocturnal dippers and nondippers. Do we treat them differently? Vascular Health and Risk Management, 9, 125–133.CrossRefGoogle Scholar
  18. 18.
    Cabezas-Cerrato, J., Hermida, R. C., Cabezas-Agricola, J. M., & Ayala, D. E. (2009). Cardiac autonomic neuropathy, estimated cardiovascular risk, and circadian blood pressure pattern in diabetes mellitus. Chronobiology International, 26(5), 942–957.CrossRefGoogle Scholar
  19. 19.
    Hermida, R. C., Ayala, D. E., Mojón, A., & Fernández, J. R. (2010). Influence of circadian time of hypertension treatment on cardiovascular risk: Results of the mapec study. Chronobiology International, 27(8), 1629–1651.CrossRefGoogle Scholar
  20. 20.
    Rouco, J., Campilho, A. (2013). Robust common carotid artery lumen detection in B-mode ultrasound images using local phase symmetry. In Acoustics, speech and signal processing (ICASSP), 2013 IEEE International Conference on (pp. 929–933).Google Scholar
  21. 21.
    Rocha, R., Silva, J., & Campilho, A. J. C. (2012). Automatic segmentation of carotid B-mode images using fuzzy classification. Medical & Biological Engineering & Computing, 50(5), 533–545.CrossRefGoogle Scholar
  22. 22.
    Molinari, F., Zeng, G., & Suri, J. S. (2010). A state of the art review on intimamedia thickness (imt) measurement and wall segmentation techniques for carotid ultrasound. Computer Methods and Programs in Biomedicine, 100(3), 201–221.CrossRefGoogle Scholar
  23. 23.
    Cheng, J., Li, H., Xiao, F., Fenster, A., Zhang, X., He, X., et al. (2013). Fully automatic plaque segmentation in 3-d carotid ultrasound images. Ultrasound in Medicine and Biology, 39(12), 2431–2446.CrossRefGoogle Scholar
  24. 24.
    Loizou, C. P., Petroudi, S., Pattichis, C. S., Pantziaris, M., Kasparis, T., & Nicolaides, A. (2012). Segmentation of atherosclerotic carotid plaque in ultrasound video. Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2012, 53–56.Google Scholar
  25. 25.
    Kuo, F., Gardener, H., Dong, C., et al. (2012). Traditional cardiovascular risk factors explain the minority of the variability in carotid plaque. Stroke, 43(7), 1755–1760.CrossRefGoogle Scholar
  26. 26.
    Acharya, U. R., Faust, O., Sree, S. V., Alvin, A. P. C., Krishnamurthi, G., Seabra, J. C. R., et al. (2011). Atheromatic: Symptomatic vs. asymptomatic classification of carotid ultrasound plaque using a combination of HOS, DWT & texture. Annual International Conference of the IEEE Engineering in Medicine and Biology Society., 2011, 4489–4492.Google Scholar
  27. 27.
    Afonso, D., Seabra, J., Suri, J. S., & Sanches, J. M. (2012). A CAD system for atherosclerotic plaque assessment. Annual International Conference of the IEEE Engineering in Medicine and Biology Society., 2012, 1008–1011.Google Scholar
  28. 28.
    Gastounioti, A., Kolias, V., Golemati, S., Tsiaparas, N. N., Matsakou, A., Stoitsis, J. S., et al. (2014). CAROTID—A web-based platform for optimal personalized management of atherosclerotic patients. Computer Methods and Programs in Biomedicine, 114(2), 183–193. doi:10.1016/j.cmpb.2014.02.006.CrossRefGoogle Scholar
  29. 29.
    Sánchez, C. I., Niemeijer, M., Dumitrescu, A. V., Suttorp-Schulten, M. S. A., Abrmoff, M. D., & van Ginneken, B. (2011). Evaluation of a computer-aided diagnosis system for diabetic retinopathy screening on public data. Investigative Ophthalmology & Visual Science, 52(7), 4866–4871.CrossRefGoogle Scholar
  30. 30.
    Li, Y., Karnowski, T. P., Tobin, K. W., Giancardo, L., Morris, S., Sparrow, S. E., et al. (2011). A health insurance portability and accountability actcompliant ocular telehealth network for the remote diagnosis and management of diabetic retinopathy. Telemedicine and e-Health, 17(8), 627–634.CrossRefGoogle Scholar
  31. 31.
    Dashtbozorg, B., Mendonça, A. M., & Campilho, A. (2014). An automatic graph-based approach for artery/vein classification in retinal images. IEEE Transactions on Image Processing, 23(3), 1073–1083.MathSciNetCrossRefGoogle Scholar
  32. 32.
    Ortega, M., Barreira, N., Novo, J., Penedo, M. G., Pose-Reino, A., & Gómez-Ulla, F. (2010). Sirius: A web-based system for retinal image analysis. International Journal of Medical Informatics, 79(10), 722–732.CrossRefGoogle Scholar
  33. 33.
    Cunha-Vaz, J., Bernardes, R., Santos, T., Oliveira, C., Lobo, C., Pires, I., et al. (2012). Computer-aided detection of diabetic retinopathy progression. Digital teleretinal screening (pp. 59–66). Berlin: Springer.CrossRefGoogle Scholar
  34. 34.
    Cooper, L. S., Wong, T. Y., Klein, R., Sharrett, A. R., Bryan, R. N., Hubbard, L. D., et al. (2006). Retinal microvascular abnormalities and mridefined subclinical cerebral infarction: The atherosclerosis risk in communities study. Stroke, 37(1), 82–86.CrossRefGoogle Scholar
  35. 35.
    Wong, T., Klein, R., Sharrett, A., Duncan, B., Couper, D., Klein, B., et al. (2004). Retinal arteriolar diameter and risk for hypertension. Annals of Internal Medicine, 140(4), 248.CrossRefGoogle Scholar
  36. 36.
    Wong, T. Y., Rosamond, W., Chang, P. P., Couper, D. J., Sharrett, A. R., Hubbard, L. D., et al. (2005). Retinopathy and risk of congestive heart failure. JAMA, 293(1), 63–69.CrossRefGoogle Scholar
  37. 37.
    Coll-de-Tuero, G., González-Vázquez, S., Rodríguez-Poncelas, A., Barceló, M. A., Barrot-de-la Puente, J., Penedo, M.G., et al. (2014). Retinal arterioleto-venule ratio changes and target organ disease evolution in newly diagnosed hypertensive patients at 1-year follow-up. Journal of the American Society of Hypertension, 8(2), 83–93.CrossRefGoogle Scholar
  38. 38.
    Touboul, P. J., Hennerici, M. G., Meairs, S., Adams, et al. (2012). Mannheim Carotid IntimaMedia Thickness Consensus (2004–2006-2011). An Update on Behalf of the Advisory Board of the 3rd, 4th and 5th Watching the Risk Symposium 13th, 15th and 20th European Stroke Conferences, Mannheim, Germany, 2004, Brussels, Belgium, 2006 and Hamburg, Germany, 2011. Cerebrovascular Diseases, 34, 290–296.CrossRefGoogle Scholar
  39. 39.
    Rocha, R., Silva, J., & Campilho, A. (2014). Automatic detection of the carotid lumen axis in b-mode ultrasound images. Computer Methods and Programs in Biomedicine, 115(3), 110–118.CrossRefGoogle Scholar
  40. 40.
    Rouco, J., Azevedo, E., & Campilho, A. (2016). Automatic lumen detection on longitudinal ultrasound b-mode images of the carotid using phase symmetry. Sensors, 16(3), 350.CrossRefGoogle Scholar
  41. 41.
    Dashtbozorg, B., Mendonça, A., & Campilho, A. (2015). Optic disc segmentation using the sliding band filter. Computers in Biology and Medicine, 56, 1–12.CrossRefGoogle Scholar
  42. 42.
    Blanco, M., Penedo, M., Barreira, N., Penas, M., & Carreira, M. (2006). Localization and extraction of the optic disc using the fuzzy circular hough transform. Artificial Intelligence and soft computing ICAISC 2006 (Vol. 4029, pp. 712–721)., Lecture Notes in Computer Science Berlin: Springer.CrossRefGoogle Scholar
  43. 43.
    López, A. M., Lloret, D., Serrat, J., & Villanueva, J. J. (2000). Multilocal creaseness based on the level-set extrinsic curvature. Computer Vision and Image Understanding, 77(2), 111–144.CrossRefGoogle Scholar
  44. 44.
    Barreira, N., Ortega, M., Rouco, J., Penedo, M. G., Pose-Reino, A., & Mario, C. (2010). Semi automatic procedure for the computation of the arteriovenous ratio in retinal images. International Journal for Computational Vision and Biomechanics, 3(2), 135–147.Google Scholar
  45. 45.
    Vázquez, S., Barreira, N., Penedo, M., Ortega, M., & Pose-Reino, A. (2010). Improvements in retinal vessel clustering techniques: towards the automatic computation of the arterio venous ratio. Computing, 90(3–4), 197–217.CrossRefMATHGoogle Scholar
  46. 46.
    Vázquez, S., Barreira, N., Penedo, M., Saez, M., & Pose-Reino, A. (2010). Using retinex image enhancement to improve the artery/vein classification in retinal images. Image Analysis and Recognition, Lecture Notes in Computer Science, 6112, 50–59.CrossRefGoogle Scholar
  47. 47.
    Vázquez, S., Cancela, B., Barreira, N., Penedo, M., Saez, M. (2010). On the automatic computation of the arterio-venous ratio in retinal images: Using minimal paths for the artery/vein classification. In: Digital image computing: Techniques and applications (DICTA), 2010 International Conference on (pp. 599–604).Google Scholar
  48. 48.
    Vázquez, S., Barreira, N., Penedo, M., Rodriguez-Blanco, M., Gómez-Ulla, F., González, A., et al. (2012). Automatic arteriovenous ratio computation: Emulating the experts. Technological Innovation for Value Creation, IFIP Advances in Information and Communication Technology, 372, 563–570.CrossRefGoogle Scholar

Copyright information

© Taiwanese Society of Biomedical Engineering 2017

Authors and Affiliations

  1. 1.Department of ComputingUniversity of A CoruñaA CoruñaSpain
  2. 2.INESC-TEC, INESC Science and Technology and Faculdade de EngenhariaUniversidade do PortoPortoPortugal
  3. 3.Faculdade de EngenhariaUniversidade do PortoPortoPortugal

Personalised recommendations