Ryan Swan

  • Predoctoral Candidate, Medical Informatics and Clinical Epidemiology, School of Medicine
  • Medical Informatics and Clinical Epidemiology, School of Medicine

Biography

Ryan Swan is a predoctoral fellow in Bioinformatics and Computational Biology. His work to date has focused on collaborating with the i-ROP research consortium to investigate the diagnosis of and genetic contributions to Retinopathy of Prematurity, a disease causing blindness in premature infants. He has performed work in clinical decision support, imaging, and machine learning. His dissertation focus is investigating ways of using networks to represent genetic risk in rare disease.

Education and training

    • B.S., 2006, University of Wisconsin - Madison

  • Fellowship

    • National Library of Medicine Biomedical Informatics and Data Science Research Training Fellowship

Areas of interest

  • Network Science
  • Genetic Risk
  • Clinical Decision Support
  • Computational Biology
  • Computer Science

Additional information

Honors and awards

  • OCTRI OSLER TL1 Training Grant
  • NIH VISION T32 Training Grant
  • ARVO Travel Grant

Publications

Selected publications

  • Swan, R., Kim, S. J., Campbell, J. P., Chan, R. V. P., Sonmez, K., Taylor, K. D., ... & Chiang, M. F. The Genetics of Retinopathy of Prematurity: A Model for Neovascular Retinal Disease. Ophthalmology Retina.
  • Campbell, J.P., Kalpathy-Cramer, J., Erdogmus, D., Tian, P., Kedarisetti, D., Moleta, C., Reynolds, J.D., Hutcheson, K., Shapiro, M.J., Repka, M.X. and Ferrone, P., 2016. Plus disease in retinopathy of prematurity: a continuous spectrum of vascular abnormality as a basis of diagnostic variability. Ophthalmology, 123(11), pp.2338-2344.
  • Kalpathy-Cramer, J., Campbell, J.P., Erdogmus, D., Tian, P., Kedarisetti, D., Moleta, C., Reynolds, J.D., Hutcheson, K., Shapiro, M.J., Repka, M.X. and Ferrone, P., 2016. Plus disease in retinopathy of prematurity: improving diagnosis by ranking disease severity and using quantitative image analysis. Ophthalmology, 123(11), pp.2345-2351.Vancouver
  • Campbell, J.P., Swan, R., Jonas, K., Ostmo, S., Ventura, C.V., Martinez-Castellanos, M.A., Anzures, R.G.A.S., Chiang, M.F. and Chan, R.P., 2015. Implementation and evaluation of a tele-education system for the diagnosis of ophthalmic disease by international trainees. In AMIA Annual Symposium Proceedings (Vol. 2015, p. 366). American Medical Informatics Association.
  • Kim, S.J., Port, A.D., Swan, R., Campbell, J.P., Chan, R.P. and Chiang, M.F., 2018. Retinopathy of prematurity: a review of risk factors and their clinical significance. Survey of ophthalmology, 63(5), pp.618-637.
  • Patel, S.N., Martinez-Castellanos, M.A., Berrones-Medina, D., Swan, R., Ryan, M.C., Jonas, K.E., Ostmo, S., Campbell, J.P., Chiang, M.F., Chan, R.P. and Yap, V., 2017. Assessment of a tele-education system to enhance retinopathy of prematurity training by international ophthalmologists-in-training in Mexico. Ophthalmology, 124(7), pp.953-961.
  • Coyner, A.S., Swan, R., Brown, J.M., Kalpathy-Cramer, J., Kim, S.J., Campbell, J.P., Jonas, K.E., Ostmo, S., Chan, R.P. and Chiang, M.F., 2018. Deep learning for image quality assessment of fundus images in retinopathy of prematurity. In AMIA Annual Symposium Proceedings (Vol. 2018, p. 1224). American Medical Informatics Association.
  • Kalpathy-Cramer, J., Campbell, J.P., Kim, S., Swan, R., Jonas, K.E., Ostmo, S., Tian, P., Kedarisetti, D., Ioannidis, S., Erdogmus, D. and Chan, R.P., 2017. Deep learning for the identification of plus disease in retinopathy of prematurity. Investigative Ophthalmology & Visual Science, 58(8), pp.5554-5554.
  • Coyner, A.S., Swan, R., Campbell, J.P., Ostmo, S., Brown, J.M., Kalpathy-Cramer, J., Kim, S.J., Jonas, K.E., Chan, R.P., Chiang, M.F. and Sonmez, K., 2019. Automated Fundus Image Quality Assessment in Retinopathy of Prematurity Using Deep Convolutional Neural Networks. Ophthalmology Retina.

Publications

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