Song, A., Roh, K.-M., Lusk, J., Valikodath, N., Lad, E., Limkakeng, A., … Kuo, A. (2022). Assessment of deep learning-based triage for robotically acquired retinal optical coherence tomography (OCT) images in an emergency department population. In Investigative Ophthalmology and Visual Science (Vol. 63, pp. 3018-F0288). Denver, CO: The Association for Research in Vision and Ophthalmology.

Song, Ailin, Kyung-Min Roh, Jay Lusk, Nita Valikodath, Eleonora Lad, Alexander Limkakeng, Joseph Izatt, Ryan McNabb, and Anthony Kuo. “Assessment of deep learning-based triage for robotically acquired retinal optical coherence tomography (OCT) images in an emergency department population.” In Investigative Ophthalmology and Visual Science, 63:3018-F0288. The Association for Research in Vision and Ophthalmology, 2022.

Song A, Roh K-M, Lusk J, Valikodath N, Lad E, Limkakeng A, et al. Assessment of deep learning-based triage for robotically acquired retinal optical coherence tomography (OCT) images in an emergency department population. In: Investigative Ophthalmology and Visual Science. The Association for Research in Vision and Ophthalmology; 2022. p. 3018-F0288.

Song, Ailin, et al. “Assessment of deep learning-based triage for robotically acquired retinal optical coherence tomography (OCT) images in an emergency department population.” Investigative Ophthalmology and Visual Science, vol. 63, The Association for Research in Vision and Ophthalmology, 2022, pp. 3018-F0288.

Song A, Roh K-M, Lusk J, Valikodath N, Lad E, Limkakeng A, Izatt J, McNabb R, Kuo A. Assessment of deep learning-based triage for robotically acquired retinal optical coherence tomography (OCT) images in an emergency department population. Investigative Ophthalmology and Visual Science. The Association for Research in Vision and Ophthalmology; 2022. p. 3018-F0288.